FinHarbor has introduced an AI co-investigator designed to support anti-money laundering work by handling routine parts of the investigation process. The company said the tool uses a self-hosted large language model tied directly to its platform data, including ledger activity, KYC and KYB records, KYT information, and audit trails.
The launch points to a growing push in financial compliance to use AI for repetitive review tasks without removing human accountability. In FinHarbor’s setup, the system is meant to take on the first layer of AML investigation work, while any decision with regulatory impact remains in human hands.
That balance is likely to be central for firms looking to speed up compliance operations without weakening controls. By connecting AI to core compliance records and transaction histories, the platform aims to help investigators move through casework more efficiently while preserving oversight and traceability.
The announcement positions FinHarbor within the expanding regtech market, where financial institutions are looking for tools that can reduce manual workloads and improve consistency in monitoring and review. Its emphasis on a self-hosted model also suggests a focus on data control and internal governance as companies evaluate how AI fits into regulated environments.
