Part one of this blog discusses the context and emerging regulatory guidelines on the use of Generative AI (GenAI) in the banking, financial services and insurance (BFSI) space. This concluding part elaborates on the application of GenAI toward regulatory and compliance related uses in BFSI.
Globally, regulations such as the Basel Committee norms, the Dodd-Frank Act, and the European Union’s General Data Protection Regulation (GDPR) insist on strong governance arrangements for risk data aggregation and reporting to help financial institutions understand, manage, and trace metadata from origin to consumption.
This ability to trace the origin, transformation, and quality of data is a huge challenge for large financial institutions, which have undergone several changes over the years. Events such as mergers, acquisitions, divestments or enterprise initiatives such as data/legacy modernization can significantly alter the data contents and flows. Additionally, firms face challenges while implementing a labyrinth of data regulations on the ground.
Automating compliance in an industry with multiple systems and data sources would require embedding GenAI across the banking and financial services stack. It starts with the elemental data requirements, going up to the infrastructure, and the models to orchestrate compliance end to end.
Simplifying Data Lineage in Complex, Siloed Data Systems
Data lineage makes sure that data used in financial institutions is correct, whole, and dependable from its creation to its end. The first challenge to solve in establishing data lineage is “data discovery”. Here’s how GenAI can enable this:
- Automated Data Extraction: GenAI can extract data from different sources, even unstructured data. This makes it easier to find important information for analysis and decision-making.
- Simplifying Compliance and Reporting: GenAI can help condense regulatory reports and maintain compliance with financial regulations by monitoring and recording data origin.
A GenAI-driven system can solve this problem by automating, enhancing, and simplifying the data discovery, use, understanding, and governance processes through:
- Data crawling scans to collect data from various sources, such as regulatory inputs, reports, unstructured data, catalogues, documentation, and code and databases.
- Network graphs that represent data elements and their relationships and dependencies. GenAI searches, lists, and ranks the data elements based on their relevance, importance, and compliance, and flags personal and sensitive data.
- A human-in-the-loop to verify, validate, and refine data elements and their relationships, and to provide domain knowledge and expertise.
- Vectorization encodes data elements and their relationships into numerical representations that can be reused and compared across different data sets and contexts.
- Cataloguing stores, organizes, and manages the data elements and their relationships in a central repository that provides metadata and documentation.
- Aligning the data elements and their relationships with the regulatory requirements and standards, such as CCPA, GDPR, BCBS 239, and SEC rules.
Architectural & Procedural Compliance: GenAI-Powered Cloud Design and Compliance
In BFSI, there are various regulations, such as PCI/DSS, MiFid-III, PSD2, FINRA, and OCC guidelines, that guide the software technology architecture. These rules aim to ensure that the technology infrastructure is strong, safe, and able to meet complex needs.
GenAI is a powerful tool that can help firms design, deploy, and manage compliant architectures on the cloud with ease and efficiency. Combining data crawling and network graphs, GenAI has been used to create compliant architectures based on user prompts and regulatory inputs. GenAI can generate architecture diagrams, infrastructure as code (IaC) scripts, visual designs, compliance reports, and reusable artefacts for various cloud platforms and compliance frameworks.
For instance, a technology architect can specify the desired compliance framework, cloud platform, architectural pattern, and application type using natural language. GenAI can access and parse the relevant regulatory rules and standards from various sources. Leveraging data crawling and network graphs, it can identify and map data elements and their relationships in the catalogued repository to create an optimal architecture that satisfies the user prompts and regulatory inputs.
Further, a graphical interface can help visualize and edit the generated architecture. GenAI can incorporate human feedback and validation at any stage of the workflow. The system can store and document the generated architecture, IaC scripts, visual designs, and compliance reports in a central repository that can be accessed and reused by the user or other stakeholders for audits.
ModelOps: Generating Model Documentation
A supervisory letter issued by the U.S. Federal Reserve and the Office of the Comptroller of the Currency (OCC) gives detailed guidance for banks and financial institutions on how to handle risks arising from using quantitative models for financial decisions. Such regulations have ushered in a new perspective of industrial application in the form of MLOps & ModelOps frameworks.
The key aspects of such a framework include sound model development, implementation, and use; effective validation; and strong governance, policies, and controls. An effective framework also considers importance of accounting for model uncertainty and limitations such as model drift, biases and hallucinations.
A unique application of GenAI in this context would be to create documentation for statistical, financial and AI models. Using GenAI, especially large language models, for the model documentation process can greatly improve the speed and quality of document creation. LLMs can use advanced natural language processing skills to understand and summarize complex information, grasp context, and produce human-like content.
A “Model Documenting System” would involve information extraction, historical sample comparison, structure and task comprehension, prompting methods, table data extraction and analysis, narrative synthesis. Coupled with a human-expert review and fine-tuning would ensure the delivery of high-quality, contextually appropriate, and professionally written documents for financial and statistical models used at BFSI firms. Furthermore, the use of retrieval augmented generation, LLM orchestration, self-validation, and improved contextual knowledge leveraging historical organizational documents can significantly improve the quality of such an automated process.
In conclusion, GenAI has the potential to revolutionize the BFSI industry by simplifying data discovery, automating compliance, and generating model documentation. By leveraging the power of GenAI, firms can elegantly handle challenges posed by changing regulations and continue to comply with an evolving regulatory landscape.
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