by Martijn Groot, VP of Product Management, Asset Control
The Targeted Review of Internal Models (TRIM) is underway and significantly impacting banks across the eurozone. TRIM is an initiative of the European Central Bank (ECB), designed to assess whether the internal risk assessment models used by banks supervised by the ECB, comply with regulatory requirements and whether their results are reliable and comparable.
As part of the programme, the ECB is engaged in a process of reviewing the banks’ models, providing them with ‘homework’ to improve their processes, and then returning to inspect again. In carrying this out, however, the ECB understands that detailed discussions with the banks about their risk assessment models will be of little value if they can’t trust the data that is fed into them.
Data Quality Principles
TRIM can be said to build on the results of the Basel Committee for Banking Supervision’s BCBS 239 document, published in 2013. While BCBS 239 laid out 14 principles for risk data aggregation for banks to abide by, it was quite generic in nature. TRIM is more specific especially around data quality aspects and measurements.
In fact, TRIM provides a range of governance principles for developing a data quality framework that covers relevant data quality dimensions including completeness, timeliness, accuracy, consistency and traceability.
To comply with TRIM, banks need to show that they can trace back the price they have used historically for a model or for a financial instrument valuation through the data supply chain back to original sources. They also need to know what processes have been carried out on the data, including checks that have been conducted, what the sources are, what were the original parameters and data quality rules and have they been changed over time? Traceability is the term used to describe this in the TRIM document but data lineage, effectively the data lifecycle that includes the data’s origins and where it moves over time, is the broader term more widely used in the data management arena.
The TRIM document also contains important reporting guidelines – including that banks will need to report on how often they have proxied their market data inputs or risk calculations.
Doing this also effectively defines a process for how the bank has derived and validated this proxy. Is it really a comparable instrument? Does it behave similarly to the original instrument?
In other words, in line with the focus on data quality in TRIM, it is important that banks are regularly validating their proxies. Finally, also in line with this focus, and to ensure they have a better grasp of the quality of the market data they use in risk calculations, banks need to ensure they have a handle on how much data is stale per asset class.
Typically, today most banks would struggle to comply with many of the data quality guidelines that TRIM puts in place. Most have no data quality or control frameworks in place or, at best, assess quality in different isolated silos. As such, they don’t have the ability to report daily on key data and metrics. They may have implemented checks and controls but generally they have little real insight into data across the whole chain. Very few have a full audit trail in place that describes how data flows from sources through quality checks and workflows into the financial models, and that does not just track data values but also the rules and the rule parameters that are acted on it.
Finding a Way Forward
So how can banks effectively meet the TRIM guidelines? Banks first need to get the basic processes right. That means putting a robust data governance and data quality framework in place. To do that, they need to document their data management principles and policies. They also need to agree on a common data dictionary and understand more clearly exactly what they are measuring, including how they define financial products across the group and the control model for the whole lifecycle.
The next stage is putting in place the technology that enables banks to achieve this. Organisations first need a data management system that has the end-to-end capability to gather, integrate and master key data, derive risk factors and publish them to different groups. That should provide banks with a single funnel and consistent set of data and data quality metrics that support TRIM compliance.
It is worth highlighting too that there are benefits on offer for banks that achieve all this that go beyond simply complying with TRIM – important though that is. Some of the remediation that they will have to do to comply will also be required for key regulations, including the Fundamental Review of the Trading Book (FRTB). However, for many, TRIM is their current focus and with the programme expected to run 2020 only, banks know there is still much work to do to meet its guidelines.
