AI agents tasked with lending compliance monitoring cannot access underwriter narrative context or timing metadata in real time, forcing pattern detection to operate on incomplete snapshots rather than the full decision-making timeline. This means actionable signals — such as adverse-action notice timing concentrated in minority applicant cohorts — are only surfaced at quarterly review cadences rather than in-loop. A shared compliance data layer with standardized timing provenance and narrative context would enable continuous monitoring but currently does not exist.

Compliance agents in lending can't access underwriter decision timelines or narrative context in real-time, so disparate impact signals (e.g., adverse-action timing clustering by demographic cohort) only surface at quarterly reviews instead of continuously.

Chief Compliance Officers and fair-lending teams at mid-to-large banks and fintechs deploying automated or semi-automated underwriting pipelines.

Banks already spend $50K-$500K+ per quarter on fair-lending audits and face 8-figure DOJ/CFPB penalties for violations discovered late; a real-time compliance data layer that catches disparate impact patterns in-loop converts a massive liability into a continuous, defensible monitoring posture that regulators actively reward.

MVP is a streaming data layer (Kafka/Flink-based) that ingests LOS events, underwriter notes (via LLM-extracted structured narratives), and timing metadata into a standardized provenance graph, with pre-built anomaly-detection agents for ECOA/HMDA patterns; deploy as a SOC2-compliant SaaS connector to Encompass, Blend, and custom LOS systems.

US banks and credit unions spend ~$30B/yr on compliance; fair-lending monitoring is a $2-4B sub-segment growing fast as regulators scrutinize AI-assisted decisioning.