Revolutionizing Regulatory Reporting in the US: Solving Challenges in Data Processing and Ongoing Liquidity Risk Regulatory Changes
The recent collapse of Silicon Valley Bank (SVB), Signature Bank, and Credit Suisse, as well as the rising liquidity risk and bank run on the regional banks, has highlighted the crucial role of robust risk management and regulation in all organizations. In May 2023, the Federal Reserve is set to provide a comprehensive review behind SVB's downfall and mandate crucial changes to safeguard the future of US financial institutions. It's highly probable that mid-sized banks ranging from $100B to $250B will face a significant overhaul of their capital and liquidity rules. Another recommendation may be to lower the threshold for Dodd-Frank law mandated stress testing for banks, which was raised in 2018 to $250B from $50B. This would put previously exempt banks back on notice and ensure they're held accountable. Additionally, regulators will increase examinations on regional banks and request more details on what is already reported for further scrutiny.
Even before these recent events, there were many challenges for these organizations, with liquidity risk being a prime example. Firms are required to report a large amount of data, including the Net Stable Funding Ratio (NSFR), Liquidity Coverage Ratio (LCR), balance sheet, and derivative supplementary data, which has led to bottlenecks in data processing. This makes it challenging for financial institutions to comply with regulatory changes. Hence, there is a need for organizations to be able to address the upcoming regulatory changes promptly while reducing implementation and maintenance costs. In this article, we explore the role of cloud computing and AI in managing data and improving the generation speed of liquidity and stress testing regulatory reports.
The Data Processing Challenge
One of the most significant challenges banks face is processing large volumes of data, especially as they expand their operations, and regulatory demands increase. Even when automated report generation or outsourcing solutions are utilized, manual interventions during the process often lead to errors, making the issue lengthy and error prone. Reporting frequencies tend to increase in line with the firm's size, and systems that can only handle monthly submissions become obsolete for organizations that require daily reports. It becomes overwhelming to handle adjustments and reconciliation between the bank's GL and other regulatory reports during tight deadlines, and even if a manual approach is used, the reports cannot guarantee 100% accuracy. Data lineage requires audibility for internal and external validation.
Potential Solutions
Moving to a cloud computing platform and leveraging technologies such as Artificial Intelligence (AI) to optimize data management, processing, and reporting is essential for any organization.
Not only does the cloud allow quicker deployments of software changes and increased processing speed amid scalability, but it also reduces the implementation cost and timelines during a regulatory change.
AI, on the other hand, can be used to provide insights into data used for reporting to help identify issues, suggest fixes, and flag potential errors. Predictive ‘what-if’ functionalities let firms simulate potential outcomes and make calculations ahead of time. It doesn’t stop there either. AI also provides better alignment between balance sheets and other regulatory reports, making it easier to ensure report reconciliation in an increasingly complex environment, resulting in better data audibility.
Aspirations for the future
Ideally, reports should go from ingestion to full report generation in minutes instead of hours, allowing organizations to spend more time with analytics. This can be done through AI and analytics tools while identifying potential areas of risks (and growth).
Impacts on Business Decisions:
Cost-efficiencies - Adopting a cloud-based platform can accelerate the implementation of system updates to comply with regulatory changes, minimizing the resources required for system maintenance and regulatory reporting.
Accurate reporting - Automated report generation can reduce the risk of manual errors, ensuring auditability and reconciliation among regulatory reports, while providing decision-makers with precise data and analysis for strategic decision-making.
First-mover advantage - Embracing technological advancements is inevitable, and financial institutions that invest in cutting-edge technology will gain a competitive edge as the technology continues to evolve.
Key Takeaways and Learnings:
With an understanding of the challenges banks face in generating Regulatory Liquidity Risk Reports, there is a clear industry demand for next-generation solutions that can effectively generate and manage the comprehensive scope of regulatory requirements. Firms should leverage such solutions to prepare for future growth and adapt to the evolving regulatory landscape, utilizing the advanced analytics capabilities that only AI can offer.
Three key takeaways to focus on are:
1. Speed of change
In a traditional liquidity risk implementation, meeting regulatory changes demands significant time and investment to ensure accurate implementation within the deadline. AI technology fundamentally addresses this challenge by accelerating development timelines, while cloud infrastructure enables rapid deployment. What previously took months to implement can now be reduced to weeks, and deployment timelines can be shortened from days to hours.
2. Audibility
As the number of records being processed and the complexity of the calculation grows, it is important for the system to track all changes and provide accurate descriptions of the processes that were applied to generate the numbers on the reports. Being able to identify the source of data irregularities, rectify them, and re-run reports, while maintaining accuracy and cross-reconciliation between reports, becomes increasingly critical as the time window for making changes narrows in the transition to daily reporting.
3. Report Generation Speed
Report accuracy and auditability must be maintained even when time constraints are present. As firms expand, careful consideration is necessary when upgrading to a new system that can handle large data volumes, perform accurate calculations, and meet condensed deadlines.
Conclusion
Automating data management and reporting processes has become standard practice in the industry due to its numerous benefits. Next-generation solutions leveraging cutting-edge technologies such as AI and cloud computing are poised to revolutionize the regulatory reporting landscape. Firms must carefully consider these advancements when planning upgrades to comply with upcoming regulatory changes imposed by the Federal Reserve in response to the fallout from SVB.