Too many TLAs (Three Letter Acronyms), I agree. Earlier this week the Financial Conduct Authority (FCA) published the results of a pilot programme called Digital Regulatory Reporting. It was an exploratory effort to understand the feasibility of using Distributed Ledger Technology (DLT) and Natural Language Processing (NLP) to automate regulatory reporting at scale.
Let me describe the regulatory reporting process that banks and regulators go through. That will help understand the challenges (hence the opportunities) with regulatory reporting.
- Generally, on a pre-agreed date, the regulators release templates of the reports that banks need to provide them.
- Banks have an army of analysts going through these templates, documenting the data items required in the reports, and then mapping them to internal data systems.
- These analysts also work out how the bank’s internal data can be transformed to arrive at the report as the end result.
- These reports are then developed by the technology teams, and then submitted to the regulators after stringent testing of the infrastructure and the numbers.
- Everytime the regulators change the structure or the data required on the report, the analysis and the build process have to be repeated.
I have super simplified the process, so it would help to identify areas where things could go wrong in this process.
- Regulatory reporting requirements are often quite generic and high level. So interpreting and breaking them down into terms that Bank’s internal data experts and IT teams understand is quite a challenge, and often error prone.
- Even if the interpretation is right, data quality in Banks is so poor that, analysts and data experts struggle to identify the right internal data.
- Banks’ systems and processes are so legacy that even the smallest change to these reports, once developed, takes a long time.
- Regulatory projects invariably have time and budget constraints, which means, they are just built with one purpose – getting the reports out of the door. Functional scalability of the regulatory reporting system is not a priority of the decision makers in banks. So, when a new, yet related reporting requirement comes in from the regulators, banks end up redoing the entire process.
- Manual involvement introduces errors, and firms often incur punitive regulatory fines if they get their reports wrong.
- From a regulator’s perspective, it is hard to make sure that the reports coming in from different banks have the right data. There are no inter-bank verification that happens on the data quality of the report.
Source/More: FCA pioneers digitising regulatory reporting using DLT and NLP – Daily Fintech
