By Mohan Bhatia – Global Head, Risk & Compliance Practice, Dharpan Koul – Practice Partner – Risk & Compliance Practice, Richard Thornton – Head, Risk & Compliance Europe and Sunil Pai – Practice Partner
The financial crisis of 2008 had huge implications across the world, some of which are still being felt over a decade later, not least in the way the finance industry is now regulated.
Banking regulators are constantly overhauling requirements as well as forcing compliance and transparency from financial institutions. This is deemed to be the best way to restore confidence in the financial system and avoid another crisis.
Major ongoing changes in the regulatory reporting space present a huge challenge for banking firms. In 2004, a bank would have to deal with around 10 regulatory changes per day; in 2017 that had increased to 185. Tracking and analysing regulatory changes is a hugely time-consuming task, with Thomson Reuters finding that more than a third of financial firms spend at least one whole day per week doing it. However, the real issue is the ever-increasing amount of data involved and ensuring the accuracy and traceability of that data.
The introduction of regulations such as SOX and BCBS-239 means that financial organisations are now expected to produce accurate and verifiable numbers consistently for every reporting period. The amount of data and new data fields that banks have to submit are growing all the time while 20% of required data elements are redefined every year. This pace of change is set to accelerate, making the challenge of managing data even harder.
There are various factors that determine how much a bank or financial institution has to spend on compliance: its size, the businesses it’s involved in, the regulatory jurisdictions it operates in, and its data architecture. As a result, large banks are spending between 10 and 15 per cent of their overall IT budget on Finance, Risk and Regulatory Reporting (FRRR) technology, with the total expenditure close to US$70 billion every year. Currently, regulatory reporting IT spend makes up just 5-10% of that overall total. However, the amount spent on regulatory reporting IT is set to grow by 20-25% over the next 3-5 years, rising from $4-7 billion to $10-15 billion by 2024. This significant rise highlights the importance of investment in this area.
Regulatory reporting falls under five key areas: preparation of financial statements, risk measurement, line items in regulatory reporting, performance measurement and statistical reporting. Each one requires a high level of accuracy and consistency across periods, but that’s especially true of the first two. As a result, many banks have extensive manual processes to ensure these requirements are being met.
A huge number of financial institutions have invested in risk, finance and performance measurement systems over the past decade. However, many of these systems are built on technologies that just cannot keep up with the ever-increasing reporting demands set out by regulators. All of this means that banks are struggling to consistently produce, segregate, and consolidate timely data for both internal decision makers and external regulators.
The vast increase and proliferation of data caused by ongoing regulatory changes represents a huge challenge for financial organisations. However, new technologies hold the key to making regulatory reporting simpler and less resource-hungry, while ensuring accuracy and consistency. Banks that have invested in mature finance and risk technologies are already seeing savings when it comes to regulatory reporting.
In order to meet ever-increasing regulator demands, one solution is to switch to industry standard data models, such as BIAN and FIBO. These offer consistency to regulatory bodies and enable the use of advanced analytics to detect risks in far less time than would be necessary if the process was done manually. With a semantic layer built across internal data sources, machine learning could be used for data quality, recon, analysis, visualisation and management of changes. These methods can also help to identify redundant or overlapping regulations that banks and financial institutions waste time and resources ensuring that they adhere to.
Embracing new technologies could help financial organisations to meet current regulatory reporting while also preparing for future requirements. There is a general agreement in the industry that advancements in Artificial Intelligence (AI) and machine learning can benefit regulatory reporting in financial institutions. In particular, AI can help businesses to manage the quality of their data to ensure that it is 100% accurate.
Another area of focus is machine-readable reporting which could help to eliminate inconsistencies in regulatory reporting caused by different interpretations of the rules. The UK’s Financial Conduct Authority (FCA) recently developed a proof of concept designed to explore how technology can help businesses to meet their regulatory reporting requirements and improve the quality of the data they provide. This technology could make requirements machine readable and executable which could enable the process to be automated in future. This would make it easier for firms to meet regulations with maximum accuracy, and potentially even lower their compliance costs.
As regulations evolve, financial businesses are gearing up to manage the massive demand of data that these changes bring. The solution is to adopt industry-wide standards and build intelligence into data management technology. Industrializing regulatory reporting technology can help businesses to meet the challenge of ensuring that data is complete, accurate and traceable.