Financial Markets Industry
Xenomorph is sponsoring the networking reception at the A-Team DMS event in London this week, and if you are attending then I wanted to extend a cordial invite to you to attend the drinks and networking reception at the end of day at 5:30pm on Thursday.
In preparation for Thursday’s Agenda then the blog links below are a quick reminder of some of the main highlights from last September’s DMS:
- Data Architecture: Sticks or Carrots?
- What Will Drive Data Management?
- Big Data, Cloud, In-Memory
- The Chief Data Officer Challenge
- Managed Services and the Utility Model
I will also be speaking on the 2pm panel “Reporting for the C-Suite: Data Management for Enterprise & Risk Analytics”. So if you like what you have heard during the day, come along to the drinks and firm up your understanding with further discussion with like-minded individuals. Alternatively, if you find your brain is so full by then of enterprise data architecture, managed services, analytics, risk and regulation that you can hardly speak, come along and allow your cerebellum to relax and make sense of it all with your favourite beverage in hand. Either way your you will leave the event more informed then when you went in...well that’s my excuse and I am sticking with it!
Hope to see you there!
Posted by Brian Sentance | 3 March 2014 | 6:33 pm
Very pleased that our partnering with Aqumin and their AlphaVision visual landscapes has been announced this week (see press release from Monday). Further background and visuals can be found at the following link and for those of you that like instant gratification please find a sample visual below showing some analysis of the S&P500.
Posted by Brian Sentance | 31 January 2014 | 7:04 pm
Quick plug for the New York version of F# in Finance event taking place next Wednesday December 11th, following on from the recent event in London. Don Syme of Microsoft Research will be demonstrating access to market data using F# and TimeScape. Hope to see you there!
Posted by Brian Sentance | 6 December 2013 | 7:49 am
Quick thank you to Don Syme of Microsoft Research for including a demonstration of F# connecting to TimeScape running on the Windows Azure cloud in the F# in Finance event this week in London. F# is functional language that is developing a large following in finance due to its applicability to mathematical problems, the ease of development with F# and its performance. You can find some testimonials on the language here.
Don has implemented a proof-of-concept F# type provider for TimeScape. If that doesn't mean much to you, then a practical example below will help, showing how the financial instrument data in TimeScape is exposed at runtime into the F# programming environment. I guess the key point is just how easy it looks to code with data, since effectively you get guided through what is (and is not!) available as you are coding (sorry if I sound impressed, I spent a reasonable amount of time writing mathematical C code using vi in the mid 90's - so any young uber-geeks reading this, please make allowances as I am getting old(er)...). Example steps are shown below:
Referencing the Xenomorph TimeScape type provider and creating a data context:
Connecting to a TimeScape database:
Looking at categories (classes) of financial instrument available:
Choosing an item (instrument) in a category by name:
Looking at the properties associated with an item:
The intellisense-like behaviour above is similar to what TimeScape's Query Explorer offers and it is great to see this implemented in an external run-time programming language such as F#. Don additionally made the point that each instrument only displays the data it individually has available, making it easy to understand what data you have to work with. This functionality is based on F#'s ability to make each item uniquely nameable, and to optionally to assign each item (instrument) a unique type, where all the category properties (defined at the category schema level) that are not available for the item are hidden.
The next event for F# in Finance will take place in New York on Wednesday 11th of December 2013 in New York, so hope to see you there. We are currently working on a beta program for this functionality to be available early in the New Year so please get in touch if this is of interest via firstname.lastname@example.org.
Posted by Brian Sentance | 27 November 2013 | 1:28 pm
An exciting departure from Xenomorph's typical focus on data management for risk in capital markets, but one of our partners, i2i Logic, has just announced the launch of their customer engagement platform for institutional and commercial banks based on Xenomorph's TimeScape. The i2i Logic team have a background in commercial banking, and have put together a platform that allows much greater interaction with a corporate client that a bank is trying to engage with.
Hosted in the cloud, and delivered to sales staff through an easy and powerful tablet app, the system enables bank sales staff to produce analysis and reports that are very specific to a particular client, based upon predictive analytics and models applied to market, fundamentals and operational data, initially supplied by S&P Capital IQ. This allows the bank and the corporate to discuss and understand where the corporate is when benchmarked against peers in a variety of metrics current across financial and operational performance, and to provide insight on where the bank's services may be able to assist in the profitability, efficiency and future growth of the corporate client.
Put another way, it sounds like the corporate customers of commercial banks are in not much better a position than us individuals dealing with retail banks, in that currently the offerings from the banks are not that engaging, generic and very hard to differentiate. Sounds like the i2i Logic team are on to something, so I wish them well in trying to move the industry's expectations of customer service and engagement, and would like to thank them for choosing TimeScape as the analytics and data management platform behind their solution.
Posted by Brian Sentance | 19 November 2013 | 2:17 pm
Another good event from PRMIA at the Harmonie Club here in NYC last week, entitled Risk Data Agregation and Risk Reporting - Progress and Challenges for Risk Management. Abraham Thomas of Citi and PRMIA introduced the evening, setting the scene by refering to the BCBS document Principles for effective risk data aggregation and risk reporting, with its 14 principles to be implemented by January 2016 for G-SIBs (Globally Systemically Important Banks) and December 2016 for D-SIBS (Domestically Systemically Important Banks).
The event was sponsored by SAP and they were represented by Dr Michael Adam on the panel, who gave a presentation around risk data management and the problems have having data siloed across many different systems. Maybe unsurprisingly Michael's presentation had a distinct "in-memory" focus to it, with Michael emphasizing the data analysis speed that is now possible using technologies such as SAP's in-memory database offering "Hana".
Following the presentation, the panel discussion started with a debate involving Dilip Krishna of Deloitte and Stephanie Losi of the Federal Reserve Bank of New York. They discussed whether the BCBS document and compliance with it should become a project in itself or part of existing initiatives to comply with data intensive regulations such as CCAR and CVA etc. Stephanie is on the board of the BCBS committee for risk data aggregation and she said that the document should be a guide and not a check list. There seemed to be general agreement on the panel that data architectures should be put together not with a view to compliance with one specific regulation but more as a framework to deal with all regulation to come, a more generalized approach.
Dilip said that whilst technology and data integration are issues, people are the biggest issue in getting a solid data architecture in place. There was an audience question about how different departments need different views of risk and how were these to be reconciled/facilitated. Stephanie said that data security and control of who can see what is an issue, and Dilip agreed and added that enterprise risk views need to be seen by many which was a security issue to be resolved.
Don Wesnofske of PRMIA and Dell said that data quality was another key issue in risk. Dilip agreed and added that the front office need to be involved in this (data management projects are not just for the back office in insolation) and that data quality was one of a number of needs that compete for resources/budget at many banks at the moment. Coming back to his people theme, Dilip also said that data quality also needed intuition to be carried out successfully.
An audience question from Dan Rodriguez (of PRMIA and Credit Suisse) asked whether regulation was granting an advantage to "Too Big To Fail" organisations in that only they have the resources to be able to cope with the ever-increasing demands of the regulators, to the detriment of the smaller financial insitutions. The panel did not completely agree with Dan's premise, arguing that smaller organizations were more agile and did not have the legacy and complexity of the larger institutions, so there was probably a sweet spot between large and small from a regulatory compliance perspective (I guess it was interesting that the panel did not deny that regulation was at least affecting the size of financial institutions in some way...)
Again focussing on where resources should be deployed, the panel debated trade-offs such as those between accuracy and consistency. The Legal Entity Identifier (LEI) initiative was thought of as a great start in establishing standards for data aggregation, and the panel encouraged regulators to look at doing more. One audience question was around the different and inconsistent treatment of gross notional and trade accounts. Dilip said that yes this was an issue, but came back to Stephanie's point that what is needed is a single risk data platform that is flexible enough to be used across multiple business and compliance projects. Don said that he suggests four "views" on risk:
- Risk Taking
- Risk Management
- Risk Measurement
- Risk Regulation
Stephanie added that organisations should focus on the measures that are most appropriate to your business activity.
The next audience question asked whether the panel thought that the projects driven by regulation had a negative return. Dilip said that his experience was yes, they do have negative returns but this was simply a cost of being in business. Unsurprisingly maybe, Stephanie took a different view advocating the benefits side coming out of some of the regulatory projects that drove improvements in data management.
The final audience question was whether the panel through the it was possible to reconcile all of the regulatory initiatives like Dodd-Frank, Basel III, EMIR etc with operational risk. Don took a data angle to this question, taking about the benefits of big data technologies applied across all relevant data sets, and that any data was now potentially valuable and could be retained. Dilip thought that the costs of data retention were continually going down as data volumes go up, but that there were costs in capturing the data need for operational risk and other applications. Dilip said that when compared globally across many industries, financial markets were way behind the data capabilities of many sectors, and that finance was more "Tiny Data" than "Big Data" and again he came back to the fact that people were getting in the way of better data management. Michael said that many banks and market data vendors are dealing with data in the 10's of TeraBytes range, whereas the amount of data in the world was around 8-900 PetaBytes (I thought we were already just over into ZetaBytes but what are a few hundred PetaBytes between friends...).
Abraham closed off the evening, firstly by asking the audience if they thought the 2016 deadline would be achieved by their organisation. Only 3 people out of around 50+ said yes. Not sure if this was simply people's reticence to put their hand up, but when Abraham asked one key concern for many was that the target would change by then - my guess is that we are probably back into the territory of the banks not implementing a regulation because it is too vague, and the regulators not being too prescriptive because they want feedback too. So a big game of chicken results, with the banks weighing up the costs/fines of non-compliance against the costs of implementing something big that they can't be sure will be acceptable to the regulators. Abraham then asked the panel for closing remarks: Don said that data architecture was key; Stephanie suggested getting the strategic aims in place but implementing iteratively towards these aims; Dilip said that deciding your goal first was vital; and Michael advised building a roadmap for data in risk.
Posted by Brian Sentance | 2 November 2013 | 1:48 pm
...Xenomorph!!! Thanks to all who voted for us in the recent A-Team Data Management Awards, it was great to win the award for Best Risk Data Management and Analytics Platform. Great that our strength in the Data Management for Risk field is being recognised, and big thanks again to clients, partners and staff who make it all possible!
Please also find below some posts for the various panel debates at the event:
- Data Architecture: Sticks or Carrots?
- What Will Drive Data Management?
- Big Data, Cloud, In-Memory
- The Chief Data Officer Challenge
- Managed Services and the Utility Model
Some photos, slides and videos from the event are now available on the A-Team site.
Posted by Brian Sentance | 24 October 2013 | 7:17 pm
Guest blog post by Qi Fu of PRMIA and Credit Suisse NYC with some notes on a model risk management event held ealier in September of this year. Big thank you to Qi for his notes and to all involved in organising the event:
The PRMIA event on Model Risk Management (MRM) was held in the evening of September 16th at Credit Suisse. The discussion was sponsored by Ernst & Young, and was organized by Cynthia Williams, Regulatory Coordinator for Americas at Credit Suisse.
As financial institutions have shifted considerable focus to model governance and independent model validation, MRM is as timely a topic as any in risk management, particularly since the Fed and OCC issued the Supervisory Guidance on Model Risk Management, also known as SR 11-7.
The event brings together a diverse range of views: the investment banks Morgan Stanley, Bank of American Merrill Lynch, and Credit Suisse are each represented, also on the panel are a consultant from E&Y and a regulator from Federal Reserve Bank of NY. The event was well attended with over 100 attendees.
Colin Love-Mason, Head of Market Risk Analytics at CS moderated the panel, and led off by discussing his 2 functions at Credit Suisse, one being traditional model validation (MV), the other being VaR development and completing gap assessment, as well as compiling model inventory. Colin made an analogy between model risk management with real estate. As in real estate, there are three golden rules in MRM, which are emphasized in SR 11-7: documentation, documentation, and documentation. Looking into the future, the continuing goals in MRM are quantification and aggregation.
Gagan Agarwala of E&Y’s Risk Advisory Practice noted that there is nothing new about many of the ideas in MRM. Most large institutions already have in place guidance on model validation and model risk management. In the past validation consisted of mostly quantitative analysis, but the trend has shifted towards establishing more mature, holistic, and sustainable risk management practices.
Karen Schneck of FRBNY’s Models and Methodology Department spoke about her role at the FRB where she is on the model validation unit for stress testing for Comprehensive Capital Analysis and Review (CCAR); thus part of her work was on MRM before SR 11-7 was written. SR 11-7 is definitely a “game changer”; since its release, there is now more formalization and organization around the oversight of MRM; rather than a rigid organization chart, the reporting structure at the FRB is much more open minded. In addition, there is an increased appreciation of the infrastructure around the models themselves and the challenges faced by practitioners, in particularly the model implementation component, which is not always immediately recognized.
Craig Wotherspoon of BAML Model Risk Management remarked on his experience in risk management, and comments that a new feature in the structure of risk governance is that model validation is turning into a component of risk management. In addition, the people involved are changing: risk professionals with the combination of a scientific mind, business sense, and writing skills will be in as high demand as ever.
Jon Hill, Head of Morgan Stanley’s Quantitative Analytics Group discussed his past experience in MRM since 90’s, when then the primary tools applied were “sniff tests”. Since then, the landscape has long been completely changed. In the past, focus had been on production, while documentation of models was an afterthought, now documentation must be detailed enough for highly qualified individual to review. In times past the focus was only around validating methodology, nowadays it is just as important to validate the implementation. There is an emphasis on stress testing, especially for complex models, in addition to internal threshold models and independent benchmarking. The definition of what a model is has also expanded to anything that takes numbers in and haves numbers as output. However, these increased demands require a substantial increase in resources; the difficulty of recruiting talent in these areas will remain a major challenge.
Colin noted a contrast in the initial comments of the panelists, on one hand some are indicating that MRM is mostly common sense; but Karen in particular emphasized the “game-changing” implications of SR 11-7, with MRM becoming more process oriented, when in the past it had been more of an intellectual exercise. With regards to recruitment, it is difficult to find candidates with all the prerequisite skill sets, one option is to split up the workload to make it easier to hire.
Craig noted the shift in the risk governance structure, the model risk control committees are defining what models are, more formally and rigorously. Gagan added that models have lifecycles, and there are inherent risks associated within that lifecycle. It is important to connect the dots to make sure everything is conceptually sound, and to ascertain that other control functions understand the lifecycles.
Karen admits that additional process requirements contain the risk of trumping value. MRM should aim to maintain high standards while not get overwhelmed by the process itself, so that some ideas become too expensive to implement. There is also the challenge of maintaining independence of the MV team.
Jon concurred with Karen on the importance of maintaining independence. A common experience is when validators find mistakes in the models, they become drawn into the development process with the modelers. He also notes differences with the US, UK, and European MV processes, and Jon asserts his view that the US is ahead of the curve and setting standards.
Colin noted the issue of the lack of an analogous PRA document to SR 11-7, that drills down into nuts and bolts of the challenges in MRM. He also concurred on the difficulty of maintaining independence, particularly in areas with no established governance. It is important to get model developers to talk to other developers about the definition and scope of the models, as well as possible expansion of scope. There is a wide gamut of models: core, pricing, risk, vendor, sensitivity, scenarios, etc. Who is responsible for validating which? Who checks on the calibration, tolerance, and weights of the models? These are important questions to address.
Craig commented further on the complexity and uncertainty of defining what a model is, and on whose job it is to determine that, amongst the different stakeholders. It also needs to be taken into consideration that model developers maybe biased towards limiting the number of models.
Gagan followed up by noting that while the generic definition of models is broad, and will need to be redefined, but analytics do not all need to have the same standards, the definition should leave some flexibility for context. Also, the highest standard should be assigned to risk models.
Karen adds that, defining and validating models used to have a narrow focus, and done in a tailor-controlled environment. It would be better to broaden the scope, and to reexamine the question on an ongoing basis (it is however important to point out that annual review does not equal annual re-validation). In addition to the primary models, some challenge models also need to be supported; developers should discuss why they’re happy with primary model, how it is different from challenger model, and how it impacts output.
Colin brought up the point of stress-testing. Jon asserts that stress-testing is more important for stochastic models, which are more likely to break under nonsensical inputs. Also any model that plugs into the risk system should require judicious decision-making, as well as annual reviews to look at changes since the previous review.
Colin also brought up the topic of change management: what are the system challenges when model developers release code, which may include experimental releases. Often discussed are concepts of annual certification and checkpoints. Jon commented that the focus should be on changes of 5% or more, with pricing model being less of a priority; and firms should move towards centralized source code depositories.
Karen also added the question of what ought to considered material change: the more conservative answer is any variation, even if a pure code change that didn’t change model usage or business application, may need to be communicated to upper management.
Colin noted that developers often have a tendency to encapsulate intentions, and have difficulty or reluctance to document changes, thus resulting in many grey areas. Gagan added that infrastructure is crucial. Especially when market conditions are rapidly changing, MRM need to have controls that are in place. Also, models are in Excel make the change management process more difficult.
The panel discussion was followed by a lively Q&A session with an engaged audience, below are some highlights.
Q: How do you distinguish between a trader whose model actually needs change, versus a trader who is only saying so because he/she has lost money?
Colin: Maintain independent price verification and control functions.
Craig: Good process for model change, and identify all stakeholders.
Karen: Focus on what model outputs are being changed, what the trader’s assumptions are, and what is driving results.
Q: How do you make sure models are used in business in a way that makes sense?
Colin: This can be difficult, front office builds the models, states what is it good for, there is no simple answer from the MV perspective; usage means get as many people in the governance process as possible, internal audit and setting up controls.
Gagan: Have coordination with other functions, holistic MRM.
Karen: Need structure, inventory a useful tool for governance function.
Q: Comments on models used in the insurance industry?
Colin: Very qualitative, possible to give indications, difficult to do exact quantitative analysis, estimates are based on a range of values. Need to be careful with inputs for very complex models, which can be based on only a few trades.
Q: What to do about big shocks in CCAR?
Jon: MV should validate for severe shocks, and if model fails may need only simple solution.
Karen: Validation tools, some backtesting data, need to benchmark, quant element of stress testing need to substantiated and supported by qualitative assessment.
Q: How to deal with vendor models?
Karen: Not acceptable just to say it’s okay as long as the vendor is reputable, want to see testing done, consider usage also compare to original intent.
Craig: New guidance makes it difficult to buy vendors models, but if vendor recognizes this, this will give them competitive advantage.
Q: How to define independence for medium and small firms?
Colin: Be flexible with resources, bring in different people, get feedback from senior management, and look for consistency.
Jon: Hire E&Y? There is never complete independence even in a big bank.
Gagan: Key is the review process.
Karen: Consultants could be cost effective; vendor validation may not be enough.
Q: At firm level, do you see practice of assessing risk models?
Jon: Large bank should appoint Model Risk Officer.
Karen: Just slapping on additional capital is not enough
Q: Who actually does MV?
Colin: First should be user, then developer, 4 eyes principle.
Q: Additional comments on change management?
Colin: Ban Excel for anything official; need controlled environment.
Posted by Brian Sentance | 24 October 2013 | 5:59 pm
Very pleased to announce our new data integration for TimeSCape with S&P Capital IQ - see the press release
Posted by Brian Sentance | 21 October 2013 | 1:25 pm
Great event from PRMIA on Tuesday evening of last week, entitled Credit Risk: The link between Loss Given Default and Default. The event was kicked off by Melissa Sexton of PRMIA, who introduced Jon Frye of the Federal Reserve Bank of Chicago. Jon seems to an acknowledged expert in the field of Loss Given Default (LGD) and credit risk modelling. I am sure that the slides will be up on the PRMIA event page above soon, but much of Jon's presentation seems to be around the following working paper. So take a look at the paper (which is good in my view) but I will stick to an overview and in particular any anecdotal comments made by Jon and other panelists.
Jon is an excellent speaker, relaxed in manner, very knowledgeable about his subject, humourous but also sensibly reserved in coming up with immediate answers to audience questions. He started by saying that his talk was not going to be long on philosophy, but very pragmatic in nature. Before going into detail, he outlined that the area of credit risk can and will be improved, but that this improvement becomes easier as more data is collected, and inevitably that this data collection process may need to run for many years and decades yet before the data becomes statistically significant.
Which Formula is Simpler? Jon showed two formulas for estimating LGD, one a relatively complex looking formula (the Vasicek distribution mentioned his working paper) and the other a simple linear model of the a + b.x. Jon said that looking at the two formulas, then many would hope that the second formula might work best given its simplicity, but he wanted to convince us that the first formula was infact simpler than the second. He said that the second formula would need to be regressed on all loans to estimate its parameters, whereas the first formula depended on two parameters that most banks should have a fairly good handle on. The two parameters were Default Rate (DR) and Expected Loss (EL). The fact that these parameters were relatively well understood seemed to be the basis for saying the first formula was simpler, despite its relative mathematical complexity. This prompted an audience question on what is the difference between Probability of Default (PD) and Default Rate (DR). Apparently it turns out PD is the expected probability of default before default happens (so ex-ante) and DR is the the realised rate of default (so ex-post).
Default and LGD over Time. Jon showed a graph (by an academic called Altman) of DR and LGD over time. When the DR was high (lots of companies failing, in a likely economic downtown) the LGD was also perhaps understandably high (so high number of companies failing, in an economic background that is both part of the causes of the failures but also not helping the loss recovery process). When DR is low, then there is a disconnect between LGD and DR. Put another way, when the number of companies failing is low, the losses incurred by those companies that do default can be high or low, there is no discernable pattern. I guess I am not sure in part whether this disconnect is due to the smaller number of companies failing meaning the sample space is much smaller and hence the outcomes are more volatile (no averaging effect), or more likely that in healthy economic times the loss given a default is much more of random variable, dependent on the defaulting company specifics rather than on general economic background.
Conclusions Beware: Data is Sparse. Jon emphasised from the graph that the Altman data went back 28 years, of which 23 years were periods of low default, with 5 years of high default levels but only across 3 separate recessions. Therefore from a statistical point of view this is very little data, so makes drawing any firm statistical conclusions about default and levels of loss given default very difficult and error-prone.
The Inherent Risk of LGD. Jon here seemed to be focussed not on the probability of default, but rather on the conditional risk that once a default has occurred then how does LGD behave and what is the risk inherent from the different losses faced. He described how LGD affects i) Economic Capital - if LGD is more variable, then you need stronger capital reserves, ii) Risk and Reward - if a loan has more LGD risk, then the lender wants more reward, and iii) Pricing/Valuation - even if the expected LGD of two loans is equal, then different loans can still default under different conditions having different LGD levels.
Models of LGD.
Jon showed a chart will LGC plotted against DR for 6 models (two of which I think he was involved in). All six models were dependent on three parameters, PD, EL and correlation, plus all six models seemed to produce almost identical results when plotted on the chart. Jon mentioned that one of his models had been validated (successfully I think, but with a lot of noise in the data) against Moody's loan data taken over the past 14 years. He added that he was surprised that all six models produced almost the same results, implying that either all models were converging around the correct solution or in total contrast that all six models were potentially subject to "group think" and were systematically all wrong in the ways the problem should be looked at.
Jon took one of his LGD models and compared it against the simple linear model, using simulated data. He showed a graph of some data points for what he called a "lucky bank" with the two models superimposed over the top. The lucky bit came in since this bank's data points for DR against LGD showed lower DR than expected for a given LGD, and lower LGD for a given DR. On this specific case, Jon said that the simple linear model fits better than his non-linear one, but when done over many data sets his LGD model fitted better overall since it seemed to be less affected by random data.
There were then a few audience questions as Jon closed his talk, one leading Jon to remind everyone of the scarcity of data in LGD modelling. In another Jon seemed to imply that he would favor using his model (maybe understandably) in the Dodd-Frank Annual Stress Tests for banks, emphasising that models should be kept simple unless a more complex model can be justified statistically.
Steve Bennet and the Data Scarcity Issue
Following Jon's talk, Steve Bennet of PECDC picked on Jon's issue of scare data within LGD modelling. Steve is based in the US, working for his organisation PECDC which is a cross border initiative to collect LGD and EAD (exposure at default) data. The basic premise seems to be that in dealing with the scarce data problem, we do not have 100 years of data yet, so in the mean time lets pool data across member banks and hence build up a more statistically significant data set - put another way: let's increase the width of the dataset if we can't control the depth.
PECDC is a consortia of around 50 organisations that pool data relating to credit events. Steve said that capture data fields per default at four "snapshot" times: orgination, 1 year prior to default, at default and at resolution. He said that every bank that had joined the organisation had managed to improve its datasets. Following an audience question, he clarified that PECDC does not predict LGD with any of its own models, but rather provides the pooled data to enable the banks to model LGD better.
Steve said that LGD turns out to be very different for different sectors of the market, particularly between SMEs and large corporations (levels of LGD for large corporations being more stable globally and less subject to regional variations). But also there is great LGD variation across specialist sectors such as aircraft finance, shipping and project finance.
Steve ended by saying that PECDC was orginally formed in Europe, and was now attempting to get more US banks involved, with 3 US banks already involved and 7 waiting to join. There was an audience question relating to whether regulators allowed pooled data to be used under Basel IRB - apparently Nordic regulators allow this due to needing more data in a smaller market, European banks use the pooled data to validate their own data in IRB but in the US banks much use their own data at the moment.
Following Steve, Til Schuermann added his thoughts on LGD. He said that LGD has a time variation and is not random, being worse in recession when DR is high. His stylized argument to support this was that in recession there are lots of defaults, leading to lots of distressed assets and that following the laws of supply and demand, then assets used in recovery would be subject to lower prices. Til mentioned that there was a large effect in the timing of recovery, with recovery following default between 1 and 10 quarters later. He offered words of warning that not all defaults and not all collateral are created equal, emphasising that debt structures and industry stress matter.
The evening closed with a few audience questions and a general summation by the panelists of the main issues of their talks, primarily around models and modelling, the scarcity of data and how to be pragmatic in the application of this kind of credit analysis.
Posted by Brian Sentance | 20 October 2013 | 6:16 pm
Andrew Delaney introduced the final panel of the day, involving Steve Cheng of Rimes, Jonathan Clark of Tech Mahindra, Tom Dalglish of UBS and Martijn Groot of Euroclear. Main points:
- Andrew started by asking the panel for their definitions of managed data services and data utilities
- Martijn said that a managed data service was usually the lifting out of a data process from in a company to be run by somebody else whereas a data utility had many users.
- Tom put it another way saying that a managed service was run for you whereas a utility was run for them. Tom suggested that there were some concerns around data utilities for the industry in terms of knowing/being transparent about data vendor affinity and any data monopoly aspects.
- When asked why past attempts at data utilities had failed, Tom said that it must be frustrating to be right but at wrong time, but in addition to the timing being right just now (costs/regulations being drivers) then the tech stack available is better and the appreciation of data usage importance is clearer.
- Steve added a great point on the tech stack, in that it now made mass customisation much easier.
- Jonathan made the point that past attempts at data utilities were built on product platforms used at clients, whereas the latest utilities were built on platforms specifically designed for use by a data utility.
- Looking at the cost savings of using a data utility, Martijn said that the industry spends around $16-20B on data, and that with his Euroclear data utility they can serve 2000 clients with a staff level that is less than any one client employs directly.
- Tom said that the savings from collapsing the data silos were primarily from more efficient/reduced usage of people and hardware to perform a specific function, and not data.
- Steve suggested that some utilities take an incremental data services and not take all data as in the old utility model, again coming back to his earlier point of mass customisation.
- Tom mentioned it was a bit like cable TV, where you can subscribe to a set of services of your choice but where certain services cost more than others.
- Martijn said that there were too many vested interests to turn data costs around quickly. He said that data utilities could go a long way however.
- Tom concluded by saying that it was about content not feeds, licensing was important as was how to segregate data.
Good panel - additionally one final audience question/discussion was around data utilities providing LEI data, and it was argued that LEI without the hierarchy is just another set of data to map and manage.
Posted by Brian Sentance | 7 October 2013 | 11:28 am
The first panel of the afternoon touched on a hot topic at the moment, the role of the Chief Data Officer (CDO). Andrew Delaney again moderated the panel, consisting of Rupert Brown of UBS, Patrick Dewald of Diaku, Colin Hall of Credit Suisse, Nigel Matthews of Barclays and Neill Vanlint of GoldenSource. Main points:
- Colin said that the need for the CDO role is that someone needs to sit at the top table who is both nerdy about data but also can communicate a vision for data to the CEO.
- Rupert said that role of CDO was still a bit nebulous covering data conformance, storage management, security and data opportunity (new functionality and profit). He suggested this role used to be called "Data Stewardship" and that the CDO tag is really a rename.
- Colin answered that the role did use to be a junior one, but regulation and the rate of industry change demands a CDO, a point contact for everyone when anything comes up that concerns data - previously nobody knew quite who to speak to on this topic.
- Patrick suggested that a CDO needs a long-term vision for data, since the role is not just an operational one.
- Nigel pointed out that the CDO needs to cover all kinds of data and mentioned recent initiatives like BCBS with their risk data aggregation paper.
- Neil said that he had seen the use of a CDO per business line at some of his clients.
- There was some conversation around the different types of CDO and the various carrots and sticks that can be employed. Neil made the audience laugh with his quote from a client that "If the stick doesn't work, I have a five-foot carrot to hit them with!"
- Patrick said that CDO role is about business not just data.
- Colin picked up on what Patrick said and illustrated this with an example of legal contract data feeding directly into capital calculations.
- Nigel said that the CDO is a facilitator with all departments. He added that the monitoring tools from market data where needed in reference data
Overall good debate, and I guess if you were starting from scratch (if only we could!) you would have to think that the CDO is a key role given the finance industry is primarily built on the flow of data from one organisation to another.
Posted by Brian Sentance | 7 October 2013 | 11:26 am
Andrew Delaney introduced the second panel of the day, with the long title of "The Industry Response: High Performance Technologies for Data Management - Big Data, Cloud, In-Memory, Meta Data & Big Meta Data". The panel included Rupert Brown of UBS, John Glendenning of Datastax, Stuart Grant of SAP and Pavlo Paska of Falconsoft. Andrew started the panel by asking what technology challenges the industry faced:
- Stuart said that risk data on-demand was a key challenge, that there was the related need to collapse the legacy silos of data.
- Pavlo backed up Stuart by suggesting that accuracy and consistency were needed for all live data.
- Rupert suggested that there has been a big focus on low latency and fast data, but raised a smile from the audience when he said that he was a bit frustrated by the "format fetishes" in the industry. He then brought the conversation back to some fundamentals from his viewpoint, talking about wholeness of data and namespaces/data dictionaries - Rupert said that naming data had been too stuck in the functional area and not considered more in isolation from the technology.
- John said that he thought there were too many technologies around at the moment, particularly in the area of Not Only SQL (NoSQL) databases. John seemed keen to push NoSQL, and in particular Apache Cassandra, as post relational databases. He put forward that these technologies, developed originally by the likes of Google and Yahoo, were the way forward and that in-memory databases from traditional database vendors were "papering over the cracks" of relational database weaknesses.
- Stuart countered John by saying that properly designed in-memory databases had their place but that some in-memory databases had indeed been designed to paper over the cracks and this was the wrong approach, exascerbating the problem sometimes.
- Responding to Andrew's questions around whether cloud usage was more accepted by the industry than it had been, Rupert said he thought it was although concerns remain over privacy and regulatory blockers to cloud usage, plus there was a real need for effective cloud data management. Rupert also asked the audience if we knew of any good release management tools for databases (controlling/managing schema versioning etc) because he and his group were yet to find one.
- Rupert expressed that Hadoop 2 was of more interest to him at UBS that Hadoop, and as a side note mentioned that map reduce was becoming more prevalent across NoSQL not just within the Hadoop domain. Maybe controversially, he said that UBS was using less data than it used to and as such it was not the "big data" organisation people might think it to be.
- As one example of the difficulties of dealing with silos, Stuart said that at one client it required the integration of data from 18 different system to a get an overall view of the risk exposure to one counterparty. Stuart advocated bring the analytics closer to the data, enabling more than one job to be done on one system.
- Rupert thought that Goldman Sachs and Morgan Stanley seem to do what is the right thing for their firm, laying out a long-term vision for data management. He said that a rethink was needed at many organisations since fundamentally a bank is a data flow.
- Stuart picked up on this and said that there will be those organisations that view data as an asset and those that view data as an annoyance.
- Rupert mentioned that in his view accountants and lawyers are getting in the way of better data usage in the industry.
- Rupert added that data in Excel needed to passed by reference and not passed by value. This "copy confluence" was wasting disk space and a source of operational problems for many organisations (a few past posts here and here on this topic).
- Moving on to describe some of the benefits of semantic data and triple stores, Rupert proposed that the statistical world needed to be added to the semantic world to produce "Analytical Semantics" (see past post relating to the idea of "analytics management").
Great panel, lots of great insight with particularly good contributions from Rupert Brown.
Posted by Brian Sentance | 7 October 2013 | 11:23 am
The first panel of the day opened with an introductory talk by Chris Johnson of HSBC. Chris started his talk by proudly announcing that he drives a Skoda car, something that to him would have been unthinkable 25 years ago but with investment, process and standards things can and will change. He suggested that data management needs to go through a similar transformation, but that there remained a lot to be done.
Moving on to the current hot topics of data unitilities and managed services, he said that reduced costs of managed services only became apparent in the long term and that both types of initiative have historically faced issues with:
- Logistical Challenges and Risks
Chris made the very good point that until service providers accept liability for data quality then this means that clients must always check the data they use. He also mentioned that in relation to Solvency II (a hot topic for Chris at HSBC Security Services), that EIOPA had recently mentioned that managed services may need to be regulated. Chris mentioned the lack of time available to respond to all the various regulatory deadlines faced (a recurring theme) and that the industry still lacked some basic fundamentals such as a standard instrument identifier.
Chris then joined the panel discussion with Andrew Delaney as moderator and with other panelists including Colin Gibson (see previous post), Matt Cox of Denver Perry, Sally Hinds of Data Management Consultancy Services and Robert Hofstetter of Bank J. Safra Sarasin. The key points I took from the panel are outlined below:
- Sally said that many firms were around Level 3 in the Data Management Maturity Model, and that many were struggling particularly with data integration. Sally added that utililities were new, as was the CDO role and that implications for data management were only just playing out.
- Matt thought that reducing cost was an obvious priority in the industry at the moment, with offshoring playing its part but progress was slow. He believed that data management remains underdeveloped with much more to be done.
- Colin said that organisations remain daunted by their data management challenges and said that new challenges for data management with transactional data and derived data.
- Sally emphasised the role of the US FATCA regulation and how it touches upon some many processess and departments including KYC, AML, Legal, Tax etc.
- Matt highlighted derivatives regulation with the current activity in central clearing, Dodd-Frank, Basel III and EMIR.
- Chris picked up on this and added Solvency II into the mix (I think you can sense regulation was a key theme...). He expressed the need and desirability of a Unique Product Identifier (UPI see report) as essential for the financial markets industry and how we need not just stand still now the LEI was coming. He said that industry associations really needed to pick up their game to get more standards in place but added that the IMA had been quite proactive in this regard. He expressed his frustration at current data licensing arrangements with data vendors, with the insistence on a single point of use being the main issue (big problem if you are in security services serving your clients I guess)
- Robert added that his main issues were data costs and data quality
- Andrew then brought the topic around to risk management and its impact on data management.
- Colin suggested that more effort was needed to understand the data needs of end users within risk management. He also mentioned that products are not all standard and data complexity presents problems that need addressing in data management.
- Chris mentioned that there 30 data fields used in Solvency II calculations and that if any are wrong this would have a direct impact on the calcualated capital charge (i.e. data is important!)
- Colin got onto the topic of unstructured data and said how it needed to be tagged in some way to become useful. He suggested that there was an embrionic cross-over taking place between structured and unstructured data usage.
- Sally thought that the merging of Business Intelligence into Data Management was a key development, and that if you have clean data then use it as much as you can.
- Robert thought that increased complexity in risk management and elsewhere should drive the need for increased automation.
- Colin thought cost pressures mean that the industry simply cannot afford the old IT infrastructure and that architecture needs to be completely rethought.
- Chris said that we all need to get the basics right, with LEI but then on to UPI. He said to his knowledge data management will always be a cost centre and standardisation was a key element of reducing costs across the industry.
- Sally thought that governance and ownership of data was wooly at many organisations and needed more work. She added this needed senior sponsorship and that data management was an ongoing process, not a one-off project.
- Matt said that the "stick" was very much needed in addition to the carrot, advising that the proponents of improved data management should very much lay out the negative consequences to bring home the reality to business users who might not see the immediate benefits and costs.
Overall good panel, lots of good debate and exchanging of ideas.
Posted by Brian Sentance | 7 October 2013 | 11:17 am
Great day on Thursday at the A-Team Data Management Summit in London (personally not least because Xenomorph won the Best Risk Data Management/Analytics Platform Award but more of that later!). The event kicked off with a brief intro from Andrew Delaney of the A-Team talking through some of the drivers behind the current activity in data management, with Andrew saying that risk and regulation were to the fore. Andrew then introduced Colin Gibson, Head of Data Architecture, Markets Division at Royal Bank of Scotland.
Data Architecture - Sticks or Carrots? Colin began by looking at the definition of "data architecture" showing how the definition on Wikipedia (now obviously the definitive source of all knowledge...) was not particularly clear in his view. He suggested himself that data architecture is composed of two related frameworks:
- Orderly Arrangement of Parts
He said that the orderly arrangement of parts is focussed on business needs and aims, covering how data is sourced, stored, referenced, accessed, moved and managed. On the discipline side, he said that this covered topics such as rules, governance, guides, best practice, modelling and tools.
Colin then put some numbers around the benefits of data management, saying that for every dollar spend on centralising data saves 20 dollars, and mentioning a resulting 80% reduction in operational costs. Related to this he said that for every dollar spent on not replicating data saved a dollar on reconcilliation tools and a further dollar saved on the use of reconcilliation tools (not sure how the two overlap but these are obviously some of the "carrots" from the title of the talk).
Despite these incentives, Colin added that getting people to actually use centralised reference data remains a big problem in most organisations. He said he thought that people find it too difficult to understand and consume what is there, and faced with a choice they do their own thing as an easier alternative. Colin then talked about a program within RBS called "GoldRush" whereby there is a standard data management library available to all new projects in RBS which contains:
- messaging standards
- standard schema
- update mechanisms
The benefit being that if the project conforms with the above standards then they have little work to do for managing reference data since all the work is done once and centrally. Colin mentioned that also there needs to be feedback from the projects back to central data management team around what is missing/needing to be improved in the library (personally I would take it one step further so that end-users and not just IT projects have easy discovery and access to centralised reference data). The lessons he took from this were that we all need to "learn to love" enterprise messaging if we are to get to the top down publish once/consume often nirvana, where consuming systems can pick up new data and functionality without significant (if any) changes (might be worth a view of this post on this topic). He also mentioned the role of metadata in automating reconcilliation where that needed to occur.
Colin then mentioned that allocation of costs of reference data to consumers is still a hot topic, one where reference data lags behind the market data permissioning/metering insisted upon by exchanges. Related to this Colin thought that the role of the Chief Data Officer to enforce policies was important, and the need for the role was being driven by regulation. He said that the true costs of a tactical, non-standard approach need to be identifiable (quantifying the size of the stick I guess) but that he had found it difficult to eliminate the tactical use of pricing data sourced for the front office. He ended by mentioning that there needs to be a coming together of market data and reference data since operations staff are not doing quantitative valuations (e.g. does the theoretical price of this new bond look ok?) and this needs to be done to ensure better data quality and increased efficiency (couldn't agree more, have a look at this article and this post for a few of my thoughts on the matter). Overall very good speaker with interesting, practical examples to back up the key points he was trying to get across.
Posted by Brian Sentance | 7 October 2013 | 11:12 am
Pleased to say that Xenomorph has been nominated in three categories in the A-Team DMS Data Management Awards. The categories are:
- Best Sell-Side Enterprise Data Management Platform
- Best Buy-Side Enterprise Data Management Platform
- Best Risk Data Management Analytics Platform
Please vote for Xenomorph by going to this link. Many thanks!
Posted by Brian Sentance | 19 September 2013 | 12:59 pm
Great event from PRMIA on Macro Stress Testing at Moody's last night. A few quick highlights:
- The role of the regulators is now not only to be sure that banks have enough capital to withstand a severe downtown, but that the banks have enough capital once the downturn has happened.
- The Fed have a new whitepaper coming out in July on "Effective Capital Adequacy Process" that covers 7 different aspects from risk management foundations through to governance.
- CCAR stress tests are thought by regulators to be easier to understand (e.g. this happens we get this loss) rather VAR/risk sensitivities that do not capture tail risk.
- Hedges that do not behave as hedges under times of stress are a major area of concern.
- Assumptions of the stress tests such as the second half of 2008 occuring instantaneously to the trading book is not reasonable/representative but hard to come up with credible/pragmatic alternatives.
- One of the speakers put forward the following lists of positives about the stress tests:
- Restoration of market/public confidence in banks
- Determination of the appropriate levels of capital adequacy
- Understanding of risk profile
- Identification of tail risks
- Curbing of risk taking
- Incentivising behaviours
- Whilst banks and regulators are often in conflict over capital adequacy, banks do implement their own internal stress tests and do have a commercial interest in doing this well.
- One panelist said that "the best hedge is to sell"
- Some banks have switched accountancy standards to game capital requirements, and there was some later debate that Risk Weighted Assets were a controversial part of the calculations when analyzed against the NYU Stern V-Lab stress testing.
- There is a danger that CCAR and stress testing drives or becomes an industry in itself, which is not good for markets, the banking system or the economy as a whole.
- There was some debate about liquidity risk as it relates to solvency, and that it should be much more integrated with the stress tests. The panel expressed interest at the forthcoming CLAR stress tests and how it relates to CCAR.
- The panel thought that the Federal Reserve is effectively challenging each bank to understand its own balance sheet better than the Fed can.
- Given the state of systems and data management at many banks, this was a big challenge.
- The panel thought that more open access to the data regulators are collecting would be great for academics to analyze given some of the big data technologies available to analyze such large datasets.
- One speaker put forward that only a subidized industry such as banking could an industry afford to treat data so poorly.
Great event, knowledgeable speakers with strong opinions and good wine/food afterwards (thanks Moody's!).
Posted by Brian Sentance | 26 June 2013 | 3:51 pm
Guest post today from Matthew Berry of Bedrock Valuation Advisors, discussing Libor vs OIS based rate benchmarks. Curves and curve management are a big focus for Xenomorph's clients and partners, so great that Matthew can shed some further light on the current debate and its implications:
New Benchmark Proposal’s Significant Implications for Data Management
During the 2008 financial crisis, problems posed by discounting future cash flows using Libor rather than the overnight index swap (OIS) rate became apparent. In response, many market participants have modified systems and processes to discount cash flows using OIS, but Libor remains the benchmark rate for hundreds of trillions of dollars worth of financial contracts. More recently, regulators in the U.S. and U.K. have won enforcement actions against several contributors to Libor, alleging that these banks manipulated the benchmark by contributing rates that were not representative of the market, and which benefitted the banks’ derivative books of business.
In response to these allegations, the CFTC in the U.S. and the Financial Conduct Authority (FCA) in the U.K. have proposed changes to how financial contracts are benchmarked and how banks manage their submissions to benchmark fixings. These proposals have significant implications for data management.
The U.S. and U.K. responses to benchmark manipulation
In April 2013, CFTC Chairman Gary Gensler delivered a speech in London in which he suggested that Libor should be retired as a benchmark. Among the evidence he cited to justify this suggestion:
- Liquidity in the unsecured inter-dealer market has largely dried up.
- The risk implied by contributed Libor rates has historically not agreed with the risk implied by credit default swap rates. The Libor submissions were often stale and did not change, even if the entity’s CDS spread changed significantly. Gensler provided a graph to demonstrate this.
Gensler proposed to replace Libor with either the OIS rate or the rate paid on general collateral repos. These instruments are more liquid and their prices more readily-observable in the market. He proposed a period of transition during which Libor is phased out while OIS or the GC repo rate is phased in.
In the U.K., the Wheatley Report provided a broad and detailed review of practices within banks that submit rates to the Libor administrator. This report found a number of deficiencies in the benchmark submission and calculation process, including:
- The lack of an oversight structure to monitor systems and controls at contributing banks and the Libor administrator.
- Insufficient use of transacted or otherwise observable prices in the Libor submission and calculation process.
The Wheatley Report called for banks and benchmark administrators to put in place rigorous controls that scrutinize benchmark submissions both pre and post publication. The report also calls for banks to store an historical record of their benchmark submissions and for benchmarks to be calculated using a hierarchy of prices with preference given to transacted prices, then prices quoted in the market, then management’s estimates.
Implications for data management
The suggestions for improving benchmarks made by Gensler and the Wheatley Report have far-reaching implications for data management.
If Libor and its replacement are run in parallel for a time, users of these benchmark rates will need to store and properly reference two different fixings and forward curves. Without sufficiently robust technology, this transition period will create operational, financial and reputational risk given the potential for users to inadvertently reference the wrong rate. If Gensler’s call to retire Libor is successful, existing contracts may need to be repapered to reference the new benchmark. This will be a significant undertaking. Users of benchmarks who store transaction details and reference rates in electronic form and manage this data using an enterprise data management platform will mitigate risk and enjoy a lower cost to transition.
Within the submitting banks and the benchmark administrator, controls must be implemented that scrutinize benchmark submissions both pre and post publication. These controls should be exceptions-based and easily scripted so that monitoring rules and tolerances can be adapted to changing market conditions. Banks must also have in place technology that defines the submission procedure and automatically selects the optimal benchmark submission. If transacted prices are available, these should be submitted. If not, quotes from established market participants should be submitted. If these are not available, management should be alerted that it must estimate the benchmark rate, and the decision-making process around that estimate should be documented.
These improvements to the benchmark calculation process will, in Gensler’s words, “promote market integrity, as well as financial stability.” Firms that effectively utilize data management technology, such as Xenomorph's TimeScape, to implement these changes will manage the transition to a new benchmark regime at a lower cost and with a higher likelihood of success.
Posted by Brian Sentance | 25 June 2013 | 12:32 pm
Anyone has followed this blog for a while will know that I (and others) have charted the decline over recent years of the SIFMA Tech exhibition that takes place each June at the Hilton on 6th Avenue in New York. Take a look at this post from 2011, and then this one from 2012. I must admit that I was shocked to see the size of the exhibition this year, with two relatively small areas in direct contrast with the five soccer pitches of previous years filled with vendor stands, exhibits, lounges and bars.
Given this background it is with some surprise that I can say Xenomorph has had a really good SIFMA in terms of getting to speak to clients, potential clients and partners. It helped that people seem very interested in our TimeScape on the Windows Azure Cloud demos (more of which below), but I have no self-delusions that the fact that Microsoft had a large number of Microsoft Surface RT tablets to give away to clients and partners was a strong driver of attendance in our part of the exhibition hall. So it seems that it takes a lot more to persuade people to come to a fintech exhibition in these days of social media and online video (As a long time iPad fan, I was quite impressed by the Surface, the GUI is better than iOS but it still has a few flakey things that need addressing, not least of which that I think that I am not allowed to use my corporate ID with Skype but only my personal email ID - I just love these user policy decisions from on high...)
Xenomorph was on the Microsoft booth, demoing TimeScape running on the Windows Azure Cloud containing market and reference data from Interactive Data, Numerix pricing analytics and using the "visual landscapes" from our new partner Aqumin. There was a lot of interest shown in our example demos on Azure of performance attribution, correlation matrix calculation, spread curve analysis, option instrument and portfolio pricing analytics - I think the penny was beginning to drop for a number of people that none of the (relatively) complex analytics was going on locally and that they could access the analysis from anywhere on any device that had an internet connection i.e. without any software to install. I also didn't hear so many people raise security concerns around cloud computing - maybe the pressure on operational costs in the market is driving some re-assessment of cloud computing? We also had a good panel discussion at the event with Microsoft and some of the above partners - as I was speaking I wasn't able to take notes but broadly the Numerix event from last week will give you a feel for what was said.
Final thoughts go out to the Microsoft staff whose email addresses appeared in the SIFMA Tech literature - seeing some of the emails sent to them by people who wanted to get a free Surface but didn't get one (because, for example, they couldn't be bothered to actually come to the Microsoft booth...) are greatly revealing about human nature. There are still a lot of pushy people out there!
Posted by Brian Sentance | 21 June 2013 | 4:32 pm
Quick note to say that Xenomorph will be exhibiting at this week's SIFMA Tech 2013 event in New York. You can find us on the Microsoft stand (booth 1507) on both Tuesday 18th and Wednesday 19th, and we can show you some of the work we have been doing with Microsoft on their Windows Azure cloud platform.
I am also speaking at the event with Microsoft and a few other partners on Wednesday 19th at 11:40am:
"Managing Data Complexity in Challenging Times" - a panel with the following participants:
- Rupesh Khendry – Head WW Capital Markets Industry Solutions, Microsoft Financial Services
- Marc Alvarez - Senior Director, Interactive Data
- Satyam Kancharla - SVP, Numerix
- Dushyant Shahrawat – Senior Reseacrh Director, CEB Towergroup
- Brian Sentance - CEO, Xenomorph
Hope to see you there!
Posted by Brian Sentance | 21 June 2013 | 4:29 pm
Numerix ran a great event on Thursday morning over at Microsoft's offices here in New York. "The Road to Achieving a Unified View of Risk" was introduced by Paul Rowady of the TABB Group. As at our holiday event last December, Paul is a great speaker and trying to get him to stop talking is the main (positive) problem of working with him (his typical ebullience was also heightened by his appearance in the Wall Street Journal on Thursday, apparently involving nothing illegal he assured me and even about which his mother phoned him during his presentation...). Paul started by saying that in their end of year review with his colleagues Larry Tabb and Adam Sussman, he suggested that Tabb Group needed to put more into developing the risk management thought leadership, which had led to today's introduction and the work Tabb Group have been doing with Numerix.
Having been involved in financial markets in Chicago, Paul is very bullish about the risk management capabilities of the funds and prop trading shops of the exchange traded options markets from days of old, and said that these risk management capabilities are now needed and indeed coming to the mainstream financial markets. Put another way, post crisis the need for a holistic view on risk has never been stronger. Considering bilateral OTC derivatives and the move towards central clearing, Paul said that he had been thinking that calculations such as CVA would eventually become as extinct as a dodo. However on using some data from the DTCC trade repository, he found that there are still some $65trillion notional of uncleared bilateral trades in the market, and that these will take a further 30 years to expire. Looking at swaptions alone the notional uncleared was $6trillion, and so his point was that bilateral OTC and their associated risks will be around for some time yet.
Paul put forward some slides showing back, middle and front-offices along different siloed business lines, and explained that back in the day when margins were fat and times were good, each unit could be run independently, with no overall view of risk possible given the range of siloed systems and data. In passing Paul also mentioned that one bank he had spoken two had 6,000 separate systems to support on just the banking side, let alone capital markets. Obviously post crisis this has changed, with pressures to reduce operational costs being a key driver at many institutions, and currently only valuation/reference data (+2.4%) and risk management (+1.2%) having increased budget spend across the market in 2013. Given operational costs and regulation such as CVA, risk management is having to move from being an end of day, post-trade process to being pre- and post-trade at intraday frequency. Paul said that not only must consistent approaches to data and analytics be taken across back, middle and front office in each business unit but now an integrated view of risk across business units must be taken (echos of an earlier event with Numerix and PRMIA). Considering consistent analytics, Paul mentioned his paper "The Risk Analytics Library" but suggested that "libraries" of everything were needed, so not just analytics, but libraries of data (data management anyone?), metadata, risk models etc.
Paul asked Ricardo Martinez of Deloite for an update on the regulatory landscape at the moment, and Ricardo responded by focusing down on the derivatives aspects Dodd-Frank. He first pointed out that even after a number of years the regulation was not yet finalized around collateral and clearing. A good point he made was that whilst the focus in the market at the moment is on compliance, he feels that the consequences of the regulation will ripple on over the next 5 years in terms of margining and analytics.
Some panel members disagreed with Paul over the premise that bilateral exotic trades will eventually disappear. Their point was that the needs of pension funds and other clients are very specific and there will always be a need for structured products, despite the capital cost incentives to move everything onto exchanges/clearing. Paul countered by saying that he didn't disagree with this, but the reason for suggesting that the exotics industry may die is trying to find institutions that can warehouse the risk of the trade.
Satyam Kancharla of Numerix spoke next. Satyam said that two main changes struck him in the market at the moment. One was the adjustment to a mandated market structure with clearing, liquidity and capital changes coming through from the regulators. The other was increased operating efficiency for investment banks. Whilst it is probable that no in investment bank would ever get to the operational efficiency of a retail business like Walmart, this was however the direction of travel with banks looking at how to optimize collateral, optimize trading venues etc.
Satyam put forward that computing power is still adhering to Moore's law, and that as a result some things are possible now that were not before, and that a centralized architecture built on this compute power is needed, but just because it is centralized does not mean that it is too inflexible to deal with each business units needs. Coming back to earlier comments made by the panel, he put forward that a lot of quants are involved in simply re-inventing the wheel, to which Paul added that quants were very experienced in using words like "orthogonal" to confuse mere mortals like him and justify the repetition of business functionality available already (from Numerix obviously, but more of that later). Satyam said that some areas of model development were more mature than others, and that quants should not engage in innovation for innovation's sake. Satyam also made a passing reference to the continuing use of Excel and VBA is the main tool of choice in the front office, suggesting that we still have some way to go in terms of IT maturity (hobby-horse topic of mine, for example see post).
Prompt by an audience question around data and analytics, Ricardo said that the major challenge towards sharing data was not technical but cultural. Against a background were maybe 50% of investment in technology was regulation-related, he said that there were no shortage of business ideas for P&L in the emerging "mandated" markets of the future, but many of these ideas required wholesale shifts in attitudes at the banks in terms of co-operation across departments and from front to back office.
Satyam said that he thought of data and analytics as two sides of the same coin (could not agree more, but then again I would say that) in that analytics generate derived data which needs just as much management as the raw data. He said that it should be possible to have systems and architectures that manage the duality of data and analytics well, and these architectures did not have to imply rigidity and inflexibility in meeting individual business needs.
There was then some debate of trade repositories for derivatives, where the panel discussed the potential conflict between the US regulators wanting competition in this area, but as Paul suggested having competition between DTCC, ICE, Bloomberg, LCH Clearnet etc also led to fragmentation. As such Paul put it that the regulators would need to "boil the ocean" to understand the exposures in the market. Ricardo also mentioned some of the current controversy over who owns the data in the trade repository. One of the panelists suggested that we should also keep an eye open to China and not necessarily get totally tied up in what is happening in "our" markets. The main point was that a huge economy such as China's could not survive without a sophisticated capital market to support it, and that China was not asleep in this regard.
A good audience question came from Don Wesnofske who asked how best to cope with the situation where an institution is selling derivatives based on one set of models, and the client is using another set of models to value the same trade. So the selling institution decides to buy/build a similar model to the client too, and Don wondered how the single analytic library practically helped this situation where I could price on one model and report my P&L using another. One panelist responded that it was mostly the assumptions behind each model that determined differences in price, and that heterogenious models and hence prices where needed for a market to function correctly. Another concurred on this and suggested there needed to be an "officially blessed" model with an institution against which valuations are compared. Amusingly for the audience, Steve O'Hanlon (CEO of Numerix) piped up that the problem was easy to resolve in that everyone should use Numerix's models.
Mike Opal of Microsoft closed the event with his presentation on data, analytics and cloud computing. Mike started by illustrating that the number of internet-enabled devices passed the human population of the world in 2008 and by 2020 the number of devices would be 50 billion. He showed that the amount of data in the world was 0.8ZB (zetabytes) in 2009, and is projected to reach 8ZB by 2015 and 35ZB by 2020, driven primarily by the growth in internet-enabled devices. Mike also said that the Prism project so in the news of late was involving the construction of a server fame near Salt Lake City of 5ZB in size, so what the industry (in this case the NSA) is trying to do is unimaginable if we were to go back only a few years. He said that Microsoft itself was utterly committed to cloud computing, with 8 datacenters globally but 20 more in construction, at a cost of $500million per center (I recently saw a datacentre in Redmond, totally unlike what I expected with racks pre-housed in lorry containers, and the containers just unloaded within a gigantic hanger and plugged in - the person showing me around asked me who the busiest person was a Microsoft data center and the answer was the truck drivers...)
Talking of "Big Data", he first gave the now-standard disclaimer (as I have I acknowledge) that he disliked the phrase. I thought he made a good point in the Big Data is really about "Small Data", in that a lot of it is about having the capacity to analyze at tiny granular level within huge datasets (maybe journalists will rename it? No, don't think so). He gave a couple of good client case studies, one for Westpac and one for Phoenix on uses of HPC and cloud computing in financial services. He also mentioned the Target retailing story about Big Data, which if you haven't caught it is worth a read. One audience question asked him again how committed Microsoft was to cloud computing given competition from Amazon, Apple and Google. Mike responded that he had only joined Microsoft a year or two back, and in part this was because he believed Microsoft had to succeed and "win" the cloud computing market given that cloud was not the only way to go for these competitors, whereas Microsoft (being a software company) had to succeed at cloud (so far Microsoft have been very helpful to us in relation to Azure, but I guess Amazon and others have other plans.)
In summary a great event from Numerix with good discussions and audience interaction - helped for me by the fact that much of what was said (centralization with flexibility, duality of data and analytics, libraries of everything etc) fits with what Xenomorph and partners like Numerix are delivering for clients.
Posted by Brian Sentance | 17 June 2013 | 7:23 pm
I went over to NYU Poly in Brooklyn on Friday of last week for their Big Data Finance Conference. To get a slightly negative point out of the way early, I guess I would have to pose the question "When is a big data conference, not a big data Conference?". Answer: "When it is a time series analysis conference" (sorry if you were expecting a funny answer...but as you can see, then what I occupy my time with professionally doesn't naturally lend itself to too much comedy). As I like time series analysis, then this was ok, but certainly wasn't fully "as advertised" in my view, but I guess other people are experiencing this problem too.
Maybe this slightly skewed agenda was due to the relative newness of the topic, the newness of the event and the temptation for time series database vendors to jump on the "Big Data" marketing bandwagon (what? I hear you say, we vendors jumping on a buzzword marketing bandwagon, never!...). Many of the talks were about statistical time series analysis of market behaviour and less about what I was hoping for, which was new ways in which empirical or data-based approaches to financial problems might be addressed through big data technologies (as an aside, here is a post on a previous PRMIA event on big data in risk management as some additional background). There were some good attempts at getting a cross-discipline fertilization of ideas going at the conference, but given the topic then representatives from the mobile and social media industries were very obviously missing in my view.
So as a complete counterexample to the two paragraphs above, the first speaker (Kevin Atteson of Morgan Stanley) at the event was on very much on theme with the application of big data technologies to the mortgage market. Apparently Morgan Stanley had started their "big data" analysis of the mortgage market in 2008 as part of a project to assess and understand more about the potential losses than Fannie Mae and Freddie Mac faced due to the financial crisis.
Echoing some earlier background I had heard on mortgages, one of the biggest problems in trying to understand the market according to Kevin was data, or rather the lack of it. He compared mortgage data analysis to "peeling an onion" and that going back to the time of the crisis, mortgage data at an individual loan level was either not available or of such poor quality as to be virtually useless (e.g. hard to get accurate ZIP code data for each loan). Kevin described the mortgage data set as "wide" (lots of loans with lots of fields for each loan) rather than "deep" (lots of history), with one of the main data problems was trying to match nearest-neighbour loans. He mentioned that only post crisis have Fannie and Freddie been ordered to make individual loan data available, and that there is still no readily available linkage data between individual loans and mortgage pools (some presentations from a recent PRMIA event on mortgage analytics are at the bottom of the page here for interested readers).
Kevin said that Morgan Stanley had rejected the use of Hadoop, primarily due write through-put capabilities, which Kevin indicated was a limiting factor in many big data technologies. He indicated that for his problem type that he still believed their infrastructure to be superior to even the latest incarnations of Hadoop. He also mentioned the technique of having 2x redundancy or more on the data/jobs being processed, aimed not just at failover but also at using the whichever instance of a job that finished first. Interestingly, he also added that Morgan Stanley's infrastructure engineers have a policy of rebooting servers in the grid even during the day/use, so fault tolerance was needed for both unexpected and entirely deliberate hardware node unavailability.
Other highlights from the day:
- Dennis Shasha had some interesting ideas on using matrix algebra for reducing down the data analysis workload needed in some problems - basically he was all for "cleverness" over simply throwing compute power at some data problems. On a humourous note (if you are not a trader?), he also suggested that some traders had "the memory of a fruit-fly".
- Robert Almgren of QuantitativeBrokers was an interesting speaker, talking about how his firm had done a lot of analytical work in trying to characterise possible market responses to information announcements (such as Friday's non-farm payroll announcement). I think Robert was not so much trying to predict the information itself, but rather trying to predict likely market behaviour once the information is announced.
- Scott O'Malia of the CFTC was an interesting speaker during the morning panel. He again acknowledged some of the recent problems the CFTC had experienced in terms of aggregating/analysing the data they are now receiving from the market. I thought his comment on the twitter crash was both funny and brutally pragmatic with him saying "if you want to rely solely upon a single twitter feed to trade then go ahead, knock yourself out."
- Eric Vanden Eijnden gave an interesting talk on "detecting Black Swans in Big Data". Most of the examples were from current detection/movement in oceanography, but seemed quite analogous to "regime shifts" in the statistical behaviour of markets. Main point seemed to be that these seemingly unpredictable and infrequent events were predictable to some degree if you looked deep enough in the data, and in particular that you could detect when the system was on a possible likely "path" to a Black Swan event.
One of the most interesting talks was by Johan Walden of the Haas Business School, on the subject of "Investor Networks in the Stock Market". Johan explained how they had used big data to construct a network model of all of the participants in the Turkish stock exchange (both institutional and retail) and in particular how "interconnected" each participant was with other members. His findings seemed to support the hypothesis that the more "interconnected" the investor (at the centre of many information flows rather than add the edges) the more likely that investor would demonstrate superior return levels to the average. I guess this is a kind of classic transferral of some of the research done in social networking, but very interesting to see it applied pragmatically to financial markets, and I would guess an area where a much greater understanding of investor behaviour could be gleaned. Maybe Johan could do with a little geographic location data to add to his analysis of how information flows.
So overall a good day with some interesting talks - the statistical presentations were challenging to listen to at 4pm on a Friday afternoon but the wine afterwards compensated. I would also recommend taking a read through a paper by Charles S. Tapiero on "The Future of Financial Engineering" for one of the best discussions I have so far read about how big data has the potential to change and improve upon some of the assumptions and models that underpin modern financial theory. Coming back to my starting point in this post on the content of the talks, I liked the description that Charles gives of traditional "statistical" versus "data analytics" approaches, and some of the points he makes about data immediately inferring relationships without the traditional "hypothesize, measure, test and confirm-or-not" were interesting, both in favour of data analytics and in cautioning against unquestioning belief in the findings from data (feels like this post from October 2008 is a timely reminder here). With all of the hype and the hope around the benefits of big data, maybe we would all be wise to remember this quote by a certain well-known physicist: "No amount of experimentation can ever prove me right; a single experiment can prove me wrong."
Posted by Brian Sentance | 22 May 2013 | 5:37 pm
Background - I went along to my first PRMIA event in Stamford, CT last night, with the rather grandiose title of "The Anthropology, Sociology, and Epistemology of Risk". Stamford is about 30 miles north of Manhattan and is the home to major offices of a number of financial markets companies such as Thomson Reuters, RBS and UBS (who apparently have the largest column-less trading floor in the world at their Stamford headquarters - particularly useful piece of trivia for you there...). It also happens to be about 5 minutes drive/train journey away from where I now live, so easy for me to get to (thanks for another useful piece of information I hear you say...). Enough background, more on the event which was a good one with five risk managers involved in an interesting and sometimes philosophical discussion on fundamentally what "risk management" is all about.
Introduction - Marc Groz who heads the Stamford Chapter of PRMIA introduced the evening and started by thanking Barry Schwimmer for allowing PRMIA to use the Stamford Innovation Centre (the Old Town Hall) for the meeting. Henrik Neuhaus moderated the panel, and started by outlining the main elements of the event title as a framework for the discussion:
- Anthropology - risk management is to what purpose?
- Sociology - how does risk management work?
- Epistemology - what knowledge is really contained within risk management?
Henrik started by taking a passage about anthropology and replacing human "development" with "risk management" which seemed to fit ok, although the angle I was expecting was much more about human behaviour in risk management than where Henrik started. Henrik asked the panel what results they had seen from risk management and what did that imply about risk management? The panelists seemed a little confused or daunted by the question prompting one of them to ask "Is that the question?".
Business Model and Risk Culture - Elliot Noma dived in by responding that the purpose of risk management obviously depended very much on what are the institutional goals of the organization. He said that it was as much about what you are forced to do and what you try to do in risk management. Elliot said that the sell-side view of risk management was very regulatory and capital focused, whereas mutual funds are looking more at risk relative to benchmarks and performance attribution. He added that in the alternatives (hedge-fund) space then there were no benchmarks and the focus was more about liquidity and event risk.
Steve Greiner said that it was down to the investment philosophy and how risk is defined and measured. He praised some asset managers where the risk managers sit across from the portfolio managers and are very much involved in the decision making process.
Henrik asked the panel whether any of the panel had ever defined a “mission statement” for risk management. Marc Groz chipped in that he remember that he had once defined one, and that it was very different from what others in the institution were expecting and indeed very different from the risk management that he and his department subsequently undertook.
Mark Szycher (of GM Pension Fund) said that risk management split into two areas for him, the first being the symmetrical risks where you need to work out the range of scenarios for a particular trade or decision being taken. The second was the more asymmetrical risks (i.e. downside only) such as those found in operational risk where you are focused on how best to avoid them happening.
Micro Risk Done Well - Santa Federico said that he had experience of some of the major problems experienced at institutions such as Merrill Lynch, Salomen Brothers and MF Global, and that he thought risk management was much more of a cultural problem than a technical one. Santa said he thought that the industry was actually quite good at the micro (trade, portfolio) risk management level, but obviously less effective at the large systematic/economic level. Mark asked Santa what was the nature of the failures he had experienced. Santa said that the risks were well modeled, but maybe the assumptions around macro variables such as the housing market proved to be extremely poor.
Keep Dancing? - Henrik asked the panel what might be done better? Elliot made the point that some risks are just in the nature of the business. If a risk manager did not like placing a complex illiquid trade and the institution was based around trading in illiquid markets then what is a risk manager to do? He quote the Citi executive who said “ whilst the music is still playing we have to dance”. Again he came back to the point that the business model of the institution drives its cultural and the emphasis of risk management (I guess I see what Elliot was saying but taken one way it implied that regardless of what was going on risk management needs to fit in with it, whereas I am sure that he meant that risk managers must fit in with the business model mandated to shareholders).
Risk Attitudes in the USA - Mark said that risk managers need to recognize that the improbable is maybe not so improbable and should be more prepared for the worst rather than risk management under “normal” market and institutional behavior. Steven thought that a cultural shift was happening, where not losing money was becoming as important to an organization as gaining money. He said that in his view, Europe and Asia had a stronger risk culture than in the United States, with much more consensus, involvement and even control over the trading decisions taken. Put another way, the USA has more of a culture of risk taking than Europe. (I have my own theories on this. Firstly I think that the people are generally much more risk takers in the USA than in UK/Europe, possibly influenced in part by the relative lack of underlying social safety net – whilst this is not for everyone, I think it produces a very dynamic economy as a result. Secondly, I do not think that cultural desire in the USA for the much admired “presidential” leader necessarily is the best environment for sound, consensus based risk management. I would also like to acknowledge that neither of my two points above seem to have protected Europe much from the worst of the financial crisis, so it is obviously a complex issue!).
Slaves to Data? - Henrik asked whether the panel thought that risk managers were slaves to data? He expanded upon this by asking what kinds of firms encourage qualitative risk management and not just risk management based on Excel spreadsheets? Santa said that this kind of qualitative risk management occurred at a business level and less so at a firm wide level. In particular he thought this kind of culture was in place at many hedge funds, and less so at banks. He cited one example from his banking career in the 1980's, where his immediate boss was shouted off the trading floor by the head of desk, saying that he should never enter the trading floor again (oh those were the days...).
Sociology and Credibility - Henrik took a passage on the historic development of women's rights and replaced the word "women" with "risk management" to illustrate the challenges risk management is facing with trying to get more say and involvement at financial institutions. He asked who should the CRO report to? A CEO? A CIO? Or a board member? Elliot responded by saying this was really a issue around credibility with the business for risk managers and risk management in general. He made the point that often Excel and numbers were used to establish credibility with the business. Elliot added that risk managers with trading experience obviously had more credibility, and to some extent where the CRO reported to was dependent upon the credibility of risk management with the business.
Trading and Risk Management Mindsets - Elliot expanded on his previous point by saying that the risk management mindset thinks more in terms of unconditional distributions and tries to learn from history. He contrasted this with a the "conditional mindset' of a trader, where the time horizon forwards (and backwards) is rarely longer than a few days and the belief is strong that a trade will work today given it worked yesterday is high. Elliot added that in assisting the trader, the biggest contribution risk managers can make is more to be challenging/helpful on the qualitative side rather than just quantitative.
Compensation and Transactions - Most of the panel seemed to agree that compensation package structure was a huge influencer in the risk culture of an organisation. Mark touched upon a pet topic of mine, which is that it very hard for a risk manager to gain credibility (and compensation) when what risk management is about is what could happen as opposed to what did happen. A risk manager blocking a trade due to some potentially very damaging outcomes will not gain any credibility with the business if the trading outcome for the suggested trade just happened to come out positive. There seemed to be concensus here that some of the traditional compensation models that were based on short-term transactional frequency and size were ill-formed (given the limited downside for the individual), and whilst the panel reserved judgement on the effectiveness of recent regulation moves towards longer-term compensation were to be welcome from a risk perspective.
MF Global and Busines Models - Santa described some of his experiences at MF Global, where Corzine moved what was essentially a broker into taking positions in European Sovereign Bonds. Santa said that the risk management culture and capabilities were not present to be robust against senior management for such a business model move. Elliot mentioned that he had been courted for trades by MF Global and had been concerned that they did not offer electronic execution and told him that doing trades through a human was always best. Mark said that in the area of pension fund management there was much greater fidiciary responsibility (i.e. behave badly and you will go to jail) and maybe that kind of responsibility had more of a place in financial markets too. Coming back to the question of who a CRO should report to, Mark also said that questions should be asked to seek out those who are 1) less likely to suffer from the "agency" problem of conflicts of interest and on a related note those who are 2) less likely to have personal biases towards particular behaviours or decisions.
Santa said that in his opinion hedge funds in general had a better culture where risk management opinions were heard and advice taken. Mark said that risk managers who could get the business to accept moral persuasion were in a much stronger position to add value to the business rather than simply being able to "block" particular trades. Elliot cited one experience he had where the traders under his watch noticed that a particular type of trade (basis trades) did not increase their reported risk levels, and so became more focussed on gaming the risk controls to achieve high returns without (reported) risk. The panel seemed to be in general agreement that risk managers with trading experience were more credible with the business but also more aware of the trader mindset and behaviors.
Do we know what we know? - Henrik moved to his third and final subsection of the evening, asking the panel whether risk managers really know what they think they know. Elliot said that traders and risk managers speak a different language, with traders living in the now, thinking only of the implications of possible events such as those we have seen with Cyprus or the fiscal cliff, where the risk management view was much less conditioned and more historical. Steven re-emphasised the earlier point that risk management at this micro trading level was fine but this was not what caused events such as the collapse of MF Global.
Rational argument isn't communication - Santa said that most risk managers come from a quant (physics, maths, engineering) background and like structured arguments based upon well understood rational foundations. He said that this way of thinking was alien to many traders and as such it was a communication challenge for risk managers to explain things in a way that traders would actually put some time to considering. On the modelling side of things, Santa said that sometimes traders dismissed models as being "too quant" and sometimes traders followed models all too blindly without questioning or understanding the simplifying assumptions they are based on. Santa summarised by saying that risk management needs to intuitive for traders and not just academically based. Mark added that a quantitative focus can sometimes become too narrow (modeler's manifesto anyone?) and made the very profound point that unfortunately precision often wins over relevance in the creation and use of many models. Steven added that traders often deal with absolutes, so as knowing the spread between two bonds to the nearest basis point, whereas a risk manager approaching them with a VaR number really means that this is the estimated VaR which really should be thought to be within a range of values. This is alien to the way traders think and hence harder to explain.
Unanticipated Risk - An audience member asked whether risk management should focus mainly on unanticipated risks rather than "normal' risks. Elliot said that in his trading he was always thinking and checking whether the markets were changing or continuing with their recent near-term behaviour patterns. Steven said that history was useful to risk management when markets were "normal", but in times of regime shifts this was not the case and cited the example of the change in markets when Mario Dragi announced that the ECB would stand behind the Euro and its member nations.
Risky Achievements - Henrik closed the panel by asking each member what they thought was there own greatest achievement in risk management. Elliot cited a time when he identified that a particular hedge fund had a relatively inconspicuous position/trade that he identified as potentially extremely dangerous and was proved correct when the fund closed down due to this. Steven said he was proud of some good work he and his team did on stress testing involving Greek bonds and Eurozone. Santa said that some of the work he had done on portfolio "risk overlays" was good. Mark ended the panel by saying that he thought his biggest achievement was when the traders and portfolio managers started to come to the risk management department to ask opinions before placing key trades. Henrik and the audience thanked the panel for their input and time.
An Insured View - After the panel closed I spoke with an actuary who said that he had greatly enjoyed the panel discussions but was surprised that when talking of how best to support the risk management function in being independent and giving "bad" news to the business, the role of auditors were not mentioned. He said he felt that auditors were a key support to insurers in ensuring any issues were allowed to come to light. So food for thought there as to whether financial markets can learn from other industry sectors.
Summary - great evening of discussion, only downside being the absence of wine once the panel had closed!
Posted by Brian Sentance | 22 May 2013 | 4:26 pm
There are (occasionally!) some good questions and conversations going on within some of the LinkedIn groups. One recently was around what use cases there are for unstructured data within banking and finance, and I found this comment from Tom Deutsch of IBM to be quite insightful and elegant (at least better than I could I have written it...) on what the main types of unstructured data analysis there are:
- Listening for the first time
- Listening better
- Adding context
Listening for the first time is really just making use of what you already probably capture to hear what is being said (or navigated)
Listening better is making sure you are actually both hearing and understanding what is being said. This is sometimes non-trivial as it involves accuracy issues and true (not marketing hype) NLP technologies and integrating multiple sources of information
Adding context is when you either add structured data to the above or add the above to structured data, usually to round out or more fully inform models (or sometimes just build new ones).
Posted by Brian Sentance | 22 May 2013 | 4:09 pm
Today is the last day of voting in the FTF News Technology Innovation Awards 2013. Our TimeScape solution has been nominated under the Best Enterprise Data Management (EDM) category. If you can spare a minute of your time today, then Xenomorph would very much appreciate your vote!
Posted by Brian Sentance | 30 April 2013 | 1:37 pm