Adapting to Regulatory Changes with
Advanced AI
In the ever-evolving financial landscape, regulatory changes require continuous monitoring, interpretation, and alignment of internal policies and procedures. Financial institutions face challenges in keeping up with updates from enforcement actions, exam priorities, and regulatory pronouncements, all while ensuring risk management and compliance obligations are met. The Risk Analyst Co-Pilot leverages Generative AI to streamline regulatory compliance, reducing the time and resources needed to ensure alignment with new standards.
Problem Statement
Financial institutions must:
- Continuously review and assess their policies against updates like LRRs (Legal Regulatory Requirements), enforcement actions, and regulatory pronouncements.
- Identify risk and control gaps to ensure compliance with new regulatory standards.
- The manual review process is time-consuming, error-prone, and often inconsistent, making it difficult to manage timely updates and effective risk mitigation.
Solution
The Risk Analyst Co-Pilot leverages advanced AI to automate the regulatory change review process, ensuring policies and procedures are updated efficiently and accurately in response to new regulations. By automatically comparing internal documents with recent regulatory updates, this AI-driven tool highlights compliance gaps, enabling financial institutions to make timely adjustments to their policies and controls. The solution also supports batch processing for handling large volumes, making it suitable for financial institutions of all sizes.
Key Features
Application
Fine-tuned Meta Foundation model (LLM3) on internal documents such as policies, procedures, training materials, and internal audit reports, deployed on internal servers for security.
Input/Output:
- Users interact with the LLM by providing details of new regulatory events.
- The model compares current practices with the new standards and provides an analysis of how well current procedures align with updated reporting requirements.
- Supports batch execution for handling scalable workloads.
Process
- The custom LLM reviews and compares internal documents against regulatory events, identifying any gaps in compliance within predefined thresholds.
- These gaps are highlighted, allowing for timely updates to policies and risk controls.
Guardrails
Input and output guardrails are implemented to protect sensitive information such as NER (Named Entity Recognition) and PII (Personally Identifiable Information), ensuring data privacy and security.
Technology Stack
The solution utilizes:
- Fine-Tuned Meta Foundation Model: LLM3
- Hugging Face
- LLAMA3
- PEFT (Parameter-Efficient Fine-Tuning), LoRA (Low-Rank Adaptation), QLoRA
- Langchain
- guardrails.io
Benefits
Improved Regulatory Compliance
Automating the comparison of internal policies with new regulatory changes ensures that compliance gaps are quickly identified and addressed.
Efficient Document Management and Comparison
By automating the review of internal documents, the system ensures consistency and accuracy across policies, procedures, and reports.
Enhanced Policy Alignment
The Co-Pilot ensures that internal policies are promptly updated to align with new regulatory standards, reducing the risk of non-compliance.
Streamlined and Scalable Document Generation
The solution supports batch processing, enabling financial institutions to handle large-scale updates efficiently and at scale.