The MRM Memo Your GC Can Audit: Logging Contracts for Prompt + Retrieval Bundles

An investment bank reduced AI-driven compliance decision costs by 72% and cut audit preparation time from six weeks to two days by shifting from model-centric MLOps to a version-controlled "MRM Contract" framework. This approach treats every AI component—prompt, data, rules, and model—as a single, auditable unit, forcing P&L ownership and eliminating the risk of unversioned prompts. The framework moved accountability from a central AI team to the business unit P&L, directly linking cost, performance, and risk management.

The operator, a mid-market investment bank, faced mounting pressure in its trade surveillance function. A team of 25 compliance officers was tasked with reviewing alerts generated by a legacy, rules-based system flagging potential market manipulation under regulations like the Market Abuse Regulation (MAR). The system, while deterministic, was brittle. It generated a high volume of false positives—over 98% of all alerts—consuming over 80% of the team's time and driving operational costs up. The daily reality for a compliance officer was a deluge of low-value work, manually cross-referencing trade data with market news and internal communications, leading to high burnout and the constant risk of a true positive being missed due to fatigue.

The Chief Operating Officer (COO), facing both rising headcount costs and pressure from regulators to modernize surveillance capabilities, sponsored a project to deploy a Large Language Model (LLM) to automate the initial analysis of these alerts. The goal was to reduce manual review overhead and allow the expert team to focus on genuinely complex investigations.