Agentic AI for Financial Crime Compliance
This addresses the problem of opaque and ineffective AI solutions in financial crime compliance for fintech firms and regulators, offering an incremental improvement through a structured, accountable approach.
The paper tackled the rising cost and complexity of financial crime compliance by designing and deploying an agentic AI system that automates onboarding, monitoring, investigation, and reporting, emphasizing explainability and compliance-by-design, with a real-world prototype developed through Action Design Research involving a fintech firm and regulators.
The cost and complexity of financial crime compliance (FCC) continue to rise, often without measurable improvements in effectiveness. While AI offers potential, most solutions remain opaque and poorly aligned with regulatory expectations. This paper presents the design and deployment of an agentic AI system for FCC in digitally native financial platforms. Developed through an Action Design Research (ADR) process with a fintech firm and regulatory stakeholders, the system automates onboarding, monitoring, investigation, and reporting, emphasizing explainability, traceability, and compliance-by-design. Using artifact-centric modeling, it assigns clearly bounded roles to autonomous agents and enables task-specific model routing and audit logging. The contribution includes a reference architecture, a real-world prototype, and insights into how Agentic AI can reconfigure FCC workflows under regulatory constraints. Our findings extend IS literature on AI-enabled compliance by demonstrating how automation, when embedded within accountable governance structures, can support transparency and institutional trust in high-stakes, regulated environments.