We are witnessing a sea-change in the way data is managed by banks and financial institutions all over the world. Data being commoditized and, in some cases, even monetized by banks is the order of the day. Though this seems to be at a stage where some more push is required in terms of adoption in the risk management function. Traditional risk managers, by their job definition, are highly cautious of the result sets provided by the analytics teams. I have even heard the phrase “Please check the report, I don’t understand the models and hence trust the number”.
So, in the risk function, while this is a race for data aggregation, structured data, unstructured data, data quality, data granularity, news feeds, market overviews, its also a challenge from an acceptance perspective. The vision is that all of the data can be aggregated, harmonized and used for better, faster and more informed decision making for Financial and Non Financial Risk Management. The interdependencies between the risks were factors that were not considered in the “Good Old Days” of risk management (pun intended).
Based on my experience, here are the common issues that are faced by banks running a risk of not having a good risk data strategy.
1. The IT-Business tussle (“YOU don’t know what YOU are doing”)
This according to me is the biggest challenge facing traditional banks, especially in the risk function. “The Business”, in traditional banks, is treated like a larger-than-life entity that needs to be supported by IT. This notion of IT being the service provider, whilst business is the “bread-earner”, especially in the traditional banks’ risk departments; does not hold good anymore. It has been proven time and again that the two cannot function without each other and that’s what needs to be cultivated as a management mindset for strategic data management effort as well. This is a culture change, but it’s happening slowly and will have to be adapted industry-wide. It has been proven that the financial institutions with the most organized data have a significant market advantage.
2. Data Overload (“Dude! where’s my Insight”)
The primary goal of data management, sourcing and aggregation effort will have to be converting data into informational insights. The team analyzing the data warehouses, the data lakes and aiding the analytics will have to have this one major organizational goal in mind. Banks have silos, these silos have been created due to mergers, regulations, entities, risk types, chinese walls, data protection, land laws or sometimes just technological challenges over time. The solution to most this is to start with a clean slate. The management mandate for getting the right people to talk and be vested in this change is crucial, challenging but crucial. Good old analysis techniques and brain storming sessions for weeding out what is unnecessary and getting the right set of elements is the key. This needs an overhaul in the way the banking business has been traditionally looking at data i.e. something that is needed for reporting. Understanding of the data lineage and touchpoint systems is most crucial.
3. The CDO Dilemma (“To meta or not to meta”)
The CDO’s role in most banks is now well defined. The risk and compliance analytics and reporting division almost solely depends on the CDO function for insights on regulatory reporting and other forms of innovative data analytics. The key success factor of the CDO organization lies in allocation of the right set of analysts to the business areas. A CDO analyst on the market risk side, for instance, will have to be well versed with market data, bank hierarchies, VaR Calculation engines, Risk not in VaR (RNiV); supporting reference data in addition to the trade systems data that these data elements will have a direct or indirect impact on. Notwithstanding the critical data elements. An additional understanding of how this would impact other forms of risk reporting, like credit risk and non-financial risk is definitely a nice to have. Defining a meta-data strategy for the full lineage, its touch-points and transformations is a strenuous effort in analysis of systems owned by disparate teams with siloed implementation patterns over time. One fix that I saw working is that every significant application group / team can have a senior representative for the CDO interaction. Vested stakeholder interest is turning out to be the one major success factor in the programs that have been successful. This ascertains completeness of the critical data elements definition and hence aid data governance strategy in a wholesome way.
4. The ever-changing nature of financial risk management (“What did they change now?”)
The Basel Committee recommendations have been consistent in driving the urge to reinvent processes in the risk management area. With Fundamental Review of the Trading Book (FRTB) the focus has been very clearly realigned to data processes in organizations. Whilst the big banks already had demonstrated a sound understanding of modellable risk factors based on scenarios, this time the Basel committee has also asked banks to focus on Non-Modellable Risk factors (NMRF). Add the standard approach (sensitivities defined by regulator) and internal models approach (IMA – Bank defined enhanced sensitivities), the change from entity based risk calculations to desk based is a significant paradigm shift. Single golden-source definition for transaction data along with desk structure validation seems to be a major area of concern amongst banks.
Add climate risk to the mix with the Paris accord, the RWA calculations will now need additional data points, additional models and additional investment in external data defining the physical and transition risk associated. Data-lake / Big Data solutions with defined critical data elements and a full log of transformations with respect to lineage is a significant investment but will only work in favor of any more changes that come through on the regulations side. There have always been banks that have been great at this consistently and banks that lag significantly.
All and all, risk management happens to be a great use case for a greenfield CDO data strategy implementation, and these hurdles have to be handled before the ultimate Zen goal of a perfect risk data strategy. Believe me, the first step is to get the bank’s consolidated risk data strategy right and everything else will follow.
This is a 2021 article, also published here – Risk Management Data Strategy – Insights from an Inquisitive Overseer | LinkedIn
