The immediate scope, which includes tools for underwriting reviews, financial modeling, know your customer (KYC) checks and pitchbook preparation, tells only part of the story. The larger one is that AI vendors are no longer selling into banking but embedding inside of it.
Anthropic introduced AI agents intended for financial services tasks, including pitchbook preparation, underwriting support and compliance-related work. The company also said its Claude model now integrates more closely with Microsoft products and financial data providers including Moody’s and Dun & Bradstreet.
Anthropic’s move arrives amid intensifying competition with OpenAI, Google and Microsoft for enterprise financial services business. Anthropic’s newest tools are already being adopted by institutions including Goldman Sachs, Visa, Citi and AIG, while the company positions financial services as its second-largest business segment after technology.
OpenAI is advancing along a similar path. On Tuesday (May 5), the company partnered with PwC on AI systems focused on forecasting, procurement, reporting, treasury and finance operations. OpenAI separately described the effort as an attempt to reimagine the office of the chief financial officer through AI-driven workflow coordination and decision support.
The contest is solidifying around operational positioning. AI firms are attempting to become integrated layers inside banking infrastructure, compliance operations, fraud systems and treasury management. That positioning is arguably deeper than retail chatbot adoption because it touches the systems institutions rely on to manage risk, capital allocation and regulatory obligations.
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Accelerating AI Deployment
Financial institutions are confronting two competing realities. Consumer and commercial demand for AI-enabled financial services continues to rise, while governance concerns remain a central topic.
PYMNTS Intelligence data provides a snapshot of banking’s embrace of AI, where, for example, 73% of top-performing credit unions are developing new payment features with external partners.
The practical challenge is not simply technical integration. Banks must determine whether external AI systems can operate inside environments governed by audit requirements, cybersecurity controls, model-risk standards and supervisory review.
The issue is drawing attention from regulators. On Friday (May 1), Federal Reserve Vice Chair for Supervision Michelle Bowman warned that AI capabilities are advancing quickly enough to require updated supervisory approaches. Additionally, in February, the Federal Reserve introduced broader internal AI systems for drafting, summarization and analytical support across its own operations.
Operational Dependence and Risk
Meanwhile, FIS announced a partnership with Anthropic Monday (May 4) to build AI-driven financial crime monitoring systems for banks.
Early deployments focused largely on customer service and employee productivity. The newer phase involves automating internal review layers that historically required large operations staffs, including transaction monitoring, sanctions screening, commercial loan documentation, dispute resolution and treasury reconciliation.
Testing AI systems that can assemble credit memoranda, summarize regulatory filings, flag unusual account behavior and monitor software code changes for security vulnerabilities represents an even broader remit. The attraction is partly financial. Large banks continue to face pressure to contain operating costs while maintaining compliance standards that have become more demanding, labor-intensive and continuous.
Those initiatives may improve operational efficiency, but they also deepen institutional dependence on a relatively narrow group of AI and cloud providers.
For financial institutions, the central question is shifting away from whether AI can improve productivity. The more consequential issue is the embedding of those systems inside regulated financial environments where cybersecurity failures, operational interruptions and compliance lapses carry immediate supervisory and financial consequences.
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