The last decade of regulatory edicts have commanded much larger data volumes than in the past to support thorough analysis and compliance.
Big data can be tricky. Diving into the details of it to explain it to a regulator or internal management can be even trickier.
Bank supervisors and their collective regulatory regimes would rest much easier knowing that the firms they manage have the ability to provide data insights on-demand, down to the trade level.
This means business users need better ways to analyze complex non-linear metrics. It is also both a challenge and an opportunity to examine business as usual and see how you can apply what you must do with what you need to do anyway on a daily basis.
The Basel Committee on Banking Supervision (BCBS)’s guidelines for the Fundamental Review of the Trading Book (FRTB) requirement, an overhaul to the global banking market risk framework presented banks with substantial challenges when updates were released in January.
ActiveViam’s FRTB Accelerator provides all the calculations necessary to meet the regulatory requirement, but also contains features that go beyond it (e.g. the ability to perform capital decomposition, what-if simulations and see incremental risk charges).
Descriptive Analytics
First, risk compliance does not consist only of reporting top-of-the-house or per-desk metrics. You have to explain how you got there, how a product or asset class fits in with the analysis and whether the capital charge is sufficient for the set of particular instruments. This requires a tool that provides more flexible data exploration to be able to look at those metrics at a granular level.
Second, the tool must be able to handle complex data and scale to the large volumes required to explain risk from the book, desk or trade level all the way up to the enterprise. Risk metrics need to be calculated on top of the raw data produced by the risk engines (e.g. sensitivities, PnL vectors).
Third, the bank should think about consistency in the approach to FRTB together with its current technology architecture and consider how implementing FRTB can integrate with broader internal risk management.
Predictive Analytics
Once you’ve dotted all the “I’s” and crossed all your FRTB “T”s, your tool should also allow you to perform predictive analysis. For example, you should be able to predict how your capital charge (and top-of-the-house metric) will be impacted if you remove a certain asset class or desk.
Markets move more swiftly than ever before. The ability to perform risk capital decomposition to view the cost of risk capital for a particular business line is a necessity. So we allow you to reallocate a capital charge to a different desk or trading book and track PnL to do a real-time cost-benefit analysis.. In this way you are looking forward into the business with an eye on controlling profit and loss, which is, after all, the name of the game.
You also need to run analytics in a changing environment (e.g. new transactions and/or revaluation of parts of your portfolio). As data comes in, your analytics system should indeed allow you to explain risk intraday as activity is happening, so that you can assess what adjustments are required, if and when necessary.
To Sum Up….
Banks need to understand that we are no longer in a world where one should consider just “the cheapest way” to fulfill regulatory compliance. Financial institutions need to create strategies and implement systems that solve for both regulations and internal risk management simultaneously with a view of achieving better risk mitigation while pursuing profitability targets.
ActiveViam creates synergies for clients by consistently furnishing them with tools that are geared towards complex data analytics, run at scale on moving data, as it’s updating. Business users can interact with the data in a “train-of-thought” manner which provides unique and singular benefits.
Watch Frederic Lebrun interviewed by Risk.net: The Data Challenges of FRTB Implementation
