by Ajay Katara
Large financial institutions (LFI), defined as firms with $100 billion or more in assets, are supervised to enhance their resilience and reduce the likelihood of failure. This oversight also aims to limit the broader economic and financial system impact if such a firm experiences failure or significant weakness. Large banking organisations are classified into four categories, each with distinct regulatory requirements.
Category I applies to U.S. global systemically important banks (G-SIBs), determined by the FRB’s G-SIB surcharge methodology. Category II includes firms with over $700 billion in assets or at least $100 billion in assets and $75 billion in cross-jurisdictional activity. Category III applies to institutions with over $250 billion in assets or exceeding certain thresholds in short-term funding, nonbank assets, or off-balance sheet exposure. Category IV encompasses firms with at least $100 billion in assets not falling under the previous categories.
Large financial reporting is inherently complex due to the scale of operations, diverse financial activities, and stringent regulatory requirements. It involves consolidating vast amounts of data from multiple sources, adhering to dynamic accounting standards, and ensuring accuracy and transparency for stakeholders. Generative AI (Gen AI) can significantly enhance Large Financial Institution (LFI) reporting by automating, streamlining, and improving the accuracy of processes while addressing regulatory compliance. Below are some key areas where Gen AI can help in LFI reporting:
- Automating Report Generation – Large financial institutions face challenges in preparing complex reports like financial statements, capital adequacy reports, and stress test results, which require analysing extensive datasets. A general solution involves automating the generation of both standard and ad hoc reports by extracting data from various sources and converting it into regulatory-compliant formats. Additionally, natural language processing (NLP) can be used to transform structured data into clear, coherent narratives for reporting.
- Enhancing Data Accuracy and Quality – Errors in data aggregation, processing, or reporting can result in regulatory penalties. A general solution involves using advanced data validation models to identify and correct inconsistencies, detecting anomalies or unusual patterns in datasets to flag potential errors before submission. Additionally, generating real-time insights on data trends helps institutions proactively address compliance issues.
- Compliance Monitoring and Regulatory Updates – Regulatory requirements frequently change, requiring LFIs to stay current and adjust their reporting processes. A general solution is to automatically analyse and summarise regulatory updates, providing actionable recommendations for reporting standards. Additionally, creating compliance checklists and audit trails ensures adherence to new rules.
- Natural Language Processing for Unstructured Data – LFI’s handle vast amounts of unstructured data, such as emails, PDFs, and contracts, which must be included in reports. A general solution involves extracting key data points from these unstructured sources, like loan agreements or customer records, for regulatory filings. Additionally, executive summaries can be generated from large volumes of textual data to streamline reporting.
- Stress Testing and Scenario Analysis – Stress testing involves simulating various scenarios to assess their impact on capital, liquidity, and operations. A general solution uses AI-driven modelling to generate multiple hypothetical stress scenarios and assists in analysing the results by identifying trends and potential areas of concern.
- Operational Efficiencies – Reporting processes are often manual and time-consuming, reducing operational efficiency. A general solution is to automate repetitive tasks like data extraction, formatting, and validation. Additionally, conversational AI tools, such as chatbots, can be used to allow staff to query financial data or access compliance guidance in real-time.
Generative AI can greatly improve the efficiency, accuracy, and compliance of LFI reporting by automating tasks like data extraction, report generation, and regulatory updates. It also helps manage unstructured data, perform stress testing, and ensure adherence to evolving regulations. However, organisations must ensure that AI models comply with data protection regulations such as GDPR (General Data Protection Regulation) and CCPA (Central Consumer Protection Authority), provide transparent reasoning for outputs, and integrate seamlessly with existing systems to avoid operational disruptions. Ultimately, while Gen AI offers significant benefits, its successful implementation requires careful attention to data security, model explainability, and system integration.
The views and opinions expressed in this article belong solely to the authors and do not represent those of the authors’ employer organisation.
The author, Ajay Katara, serves as a consulting partner and leads the Reg Tech portfolio within the banking risk management domain at Tata Consultancy Services. With over 19 years of expertise in business consulting transformation and solution design, he navigates regulatory compliances in the areas of Regulatory Capital Management, Credit Risk, Climate Risk, Stress testing and Anti Money Laundering. Operating across diverse geographies, Ajay has collaborated with numerous financial institutions and enterprises. His substantial contributions to conceptualizing strategic offerings in risk management and his impactful role in driving successful consulting engagements underscore his influence. He has also been awarded the Risk Management Professional of the Year award by CIRM Magazine UK in 2023.
