Agentic AI for Banking: Key Use Cases and Implementations
At the end of 2024, how many banks predicted that the industry would have its best year ever in 2025? And all of it happened during a period of radical reinvention. The regulatory supercycle has turned in favor of the banks; a new wave of partnerships has banks regaining private credit ground; and US policy has cleared the way for stablecoin integration. The KBW Nasdaq Bank Index (^BKX) has soared into heights, sitting at 172.12 as of this writing, up 32% in the last year.i The financial services sector is squarely in front of all others in terms of AI spending and the potential for automation. Everybody I know uses Claude or ChatGPT, so every institution has already rolled out AI, right?
AI is not your typical shiny new object, however. As CTOs push AI and LLM initiatives, they may be asking what is the use case or function I can revolutionize next? At the same time, they must proceed sensibly. We are about to see platoons of AI agents sent into the field across institutions. Here are the use cases and implementations garnering the keenest interest for sell-siders in Q1 2026.
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.
“The rise of agentic AI is paving the way for an entirely new organizational paradigm, one in which teams of interoperable AI agents are deployed across a range of banking areas, including not only front-office coverage but also middle- and back-office transaction processing, onboarding, settlements, surveillance, portfolio optimization, and more. For example, for transaction banking and treasury, monthly or daily workflows could become continuous, as AI agents monitor balances and exposures across accounts, currencies, and rails, and then execute sweep, hedge, and settle in real time.” — McKinsey, CIB in an era of volatility, AI, and nonbank challengersv
4. The modern streamlined client experience
Sell side institutions have shifted from a transactional to a relational model to compete with non-bank and fintech challengers.vi Agentic AI acts as a sophisticated interface between the bank's internal systems and its clients, creating a faster and more efficient onboarding process and allowing banks to customize their offerings. They are integrating AI into the client-facing journey to provide white-glove service at scale, executing value-adds like customized portfolio recommendations and tailored reporting based on individual customer needs.
AI does your client service homework
In the old days of client service — probably the beginning of last year — an operations person would call three different desks to understand what's happening and why, and then hunt down the problem for a client, research the fix, and revert to the client at some point. AI agents execute automated research, simultaneously connecting to back-end operational systems to do the homework, identifying if anything out of the ordinary has occurred, so that when a human representative eventually intervenes, they are already fully informed and can resolve the issue on a much faster timeline.
From AI experiments to measurable ROI in 2026
Naturally, all these functional AI priorities are interrelated. AI-driven compliance in AML and KYC produces a much swifter, more satisfying onboarding experience for clients. AI-powered data quality monitoring supports precise reconciliation operations, onboarding, and pretty much everything else in a bank. The cumulative impact will be monumental, the successful adoption of these agents leading to greater operational efficiencies which results in lower costs.
While tier one banks are currently operating at the healthiest they have been in generations, they are not unconcerned about potential recessions and liquidity events in 2026, particularly in private markets or real estate. Despite major investments in digital transformation, banks still suffer from Swiss cheese architecture, where countless business units remain under-serviced by enterprise platforms and billion-dollar business units still rely on spreadsheet-based workarounds.
The success of agentic AI at financial institutions depends on a layer of modern data infrastructure – which should an institutionalwide mandate. Those banks that are still at this stage are playing catch-up as the industry’s tone regarding AI has shifted from exploring potential to demanding ROI and real operational efficiencies in 2026. If banking tech leaders maximize agentic AI, they will not only turn the tide on a decade of persistently high costs and low margins but also become more trusted, compliant, resilient enterprises.
Authored By
Ted O’Connor
Ted is a Senior Vice President focused on Business Development at Arcesium. In this role, Ted works with leading financial institutions in the capital markets to optimize data, technology, and operational needs.
Share This post
[i] Yahoo Finance, January 5, 2025. https://finance.yahoo.com/quote/%5EBKX/
[ii] Fenergo, July 24, 2025. https://resources.fenergo.com/newsroom/fenergo-survey-over-25-of-financial-services-firms-forecast-4m-in-annual-compliance-operations-savings-through-agentic-ai
[iii] DOT, December 2024. https://home.treasury.gov/system/files/136/Artificial-Intelligence-in-Financial-Services.pdf
[iv] FinCEN, 2025. https://www.fincen.gov/system/files/shared/FinCEN-Infographic-Public-2025-508.pdf
[v] McKinsey, December 11, 2025. https://www.mckinsey.com/industries/financial-services/our-insights/cib-in-an-era-of-volatility-ai-and-nonbank-challengers
[vi] IBCA, December 6, 2024. https://www.investmentbankingcouncil.org/blog/how-investment-banks-are-adapting-to-client-centric-trends
