1. AI in AML and compliance

Banks have been using cloud automation solutions to conduct sophisticated anti-financial crime monitoring and risk detection for a few years. Now, risk and compliance officers recognize the potential of AI agents to improve the effectiveness and cost efficiency of compliance operations. A 2025 survey report revealed that 6% of financial institutions have implemented agentic AI to improve compliance operations, while 93% plan to in the next two years.ⁱⁱ This is critical in an environment of increasing global strife, sanctions, and new technologies. The Department of Treasury’s 2024 report on AI in financial services simultaneously warned against the potential of criminals using AI for illicit activity and touted the potential of AI to fortify anti-money laundering (AML) compliance and more effectively identify illicit finance patterns, risks, trends, and typologies.ⁱⁱⁱ

Real-time fraud detection

AI in fraud detection is a high-tech sentry at a busy border crossing. While a human guard can only check one passport at a time, the AI sentry can scan thousands of cars simultaneously, detecting hidden compartments (mapping criminal networks) and verifying identities in seconds (KYC). Banks are deploying agents to monitor for money laundering activities more effectively than traditional systems. It’s a good thing, since banks are facing a surge in financial crime complexity, leading to a record-breaking 2.6 million Suspicious Activity Reports (SARs) in a single year in 2024.^iv AI agents give the police a leg up, as they provide real-time detection for cash controls and compliance monitoring across transactions, identifying errant patterns that require immediate attention.

2. AI in credit and lending operations

Banks have been clawing back some of their lost credit business from non-bank private credit providers. Agentic AI helps banks navigate the high-volume, document-heavy requirements of credit and lending. The technology’s capacity to process interminable loan tapes and normalize loan data lacking a uniform standard coming from loan documents, term sheets, and mortgage servicers are changing the game for credit officers. Additionally, by automating parts of the workflow, banks can achieve faster execution of client agreements. For example, agents will accelerate legal reviews, allowing for the faster processing of complex documentation like covenants and promissory notes. Moreover, AI will go a long way to helping credit personnel engage in more informed discussions with borrowers in periodic client reviews by gathering higher quality intelligence regarding current client activities.

3. AI for bank operations and regulatory reporting

The operations bucket is one of the most impactful areas for agentic AI, focusing on reducing noise and increasing efficiency. AI tools are being used to reconcile disparate data types, executing complex data matching across currencies, asset classes, activity types, and time zones. AI agents streamline reconciliations to ensure regulatory compliance and prevent trade failures, especially crucial as retail investors, digital assets, and tokenization merge into the mainstream.

AI agents to process breaks before losses occur

AI agents that handle exceptions reporting and perform internal audits on both transpired and expected transactions achieve earlier detection of trade breaks, booking errors, and missed or misallocated fees, so the bank can process breaks before losses occur. These agents create a seamless workflow path for regulatory reporting by preserving a persistent audit trail and clear lineage, helping firms avoid the inaccurate data exposures that have led to significant fines at some institutions. In summary, a more efficient way of capturing and processing financial data and operational cost reductions lead to a material increase in banks’ capacity to do more business, as the institution becomes prepared to handle double, triple, or even quadruple their previous transaction volumes.