How to Deploy a Multi-Agent System for SR 11-7 Model Risk Management in Banking

Understanding SR 11-7 and OCC 2011-12 Requirements for Model Risk Management The Federal Reserve’s SR 11-7 and the OCC’s 2011-12 bulletin establish comprehensive expectations for model risk management in banking institutions. Key pillars include: - Model Identification & Documentation: Every model must have a clear inventory entry, complete with purpose, inputs, assumptions, and limitations. - Validation & Testing: Independent evaluation of conceptual soundness, data quality, outcomes analysis, and stability. - Ongoing Monitoring: Continuous assessment of model performance and drift. - Governance & Audit Trails: Clear records of approvals, changes, and review cycles. Multi-agent systems like LUMOS can automate large portions of these workflows, reducing manual effort and ensuring consistency. The framework described here assists compliance but does not replace legal or regulatory review.

Setting Up the LUMOS Multi-Agent Framework for Banking Operations LUMOS (Language Models Operating System) is an open-source platform hosted by the Eclipse Foundation. It provides a runtime for defining and orchestrating AI agents using an Agent Definition Language (ADL). To begin: 1. Install LUMOS Runtime – Use the official Docker image or deploy on a Kubernetes cluster. Example: 2. Enable Required Plugins – LUMOS supports extensions for document processing, Python code execution, and API connectivity. 3. Define an Orchestrator Agent – This agent coordinates specialized sub-agents. Use the ADL to specify agent roles, tools, and escalation rules. Configuring the Documentation Extraction Agent for Model Inventories The Documentation Extraction Agent automatically parses model documentation (e.g., Word docs, PDFs, Confluence pages) and populates or updates the model inventory. - Agent Def

inition (ADL snippet): - Integration Example: Use a webhook trigger when new model documents are uploaded to a shared drive. The agent reads fields like model name, owner, risk tier, and last validation date, then syncs them to the inventory database via REST API. Building the Quantitative Validation Agent with Automated Threshold Testing This agent executes statistical tests against model outputs to validate performance against SR 11-7 benchmarks. - Validation Steps: - Backtesting for predictive models (e.g., KS test, population stability index) - Sensitivity analysis - Benchmark comparison against challenger models - Agent Script Example (Python within LUMOS): - Integration: The agent calls the model inventory API to retrieve the test data path and stores results back, triggering escalation if thresholds are breached. Implementing Drift Monitoring and Automated Alerts Drift monitoring

agents periodically compare current model predictions against baseline distributions. - Drift Detection Techniques: - Population Stability Index (PSI) for categorical outputs - Kullback-Leibler divergence for probability distributions - Feature drift using Wasserstein distance - Configuration via ADL: Creating the Audit Trail Agent for End-to-End Compliance Records Every action taken by the multi-agent system must be logged immutably. The audit trail agent captures: - Agent identity and version - Timestamps of runs - Inputs and outputs - Decisions and escalations - Implementation: - Use a blockchain-based log or append-only database. - LUMOS can emit structured JSON events to a Kafka topic, which the audit agent consumes and stores. - Example output record: Integrating with Your Existing Model Inventory System Most banks have a model inventory database (e.g., SQL Server, AWS DynamoDB). L

UMOS agents interact via REST APIs. - API Contract Example (Python requests): - Sync Strategies: - Webhook-based: Inventory pushes events when a model is added or updated; agents pull documentation. - Polling-based: Agents check for new entries every hour using a list endpoint. Human-in-the-Loop Escalation Workflows for Failed Validations Not all failures should automatically block model releases. The framework routes critical issues to human reviewers. - Escalation Steps: 1. Validation agent detects failure (e.g., p-value < threshold). 2. Agent creates a ticket in the bank’s issue tracker (e.g., Jira) with severity level. 3. A designated model risk analyst reviews the findings and either overrides or confirms the failure. 4. System logs the decision and updates the model inventory status. - ADL Implementation: Conclusion Deploying a LUMOS multi-agent system for model risk management pro

vides a reusable, auditable, and automated approach to complying with SR 11-7 and OCC 2011-12. By configuring specialized agents for documentation, validation, monitoring, and audit trails, your institution can reduce manual cycles, ensure up-to-date compliance documentation, and adopt new LLMs with