AI in banking: Use cases, explainable AI, and how to deploy it in regulated environments

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AI programs in banking can struggle to pass control reviews when they cannot explain how they reached a particular decision or which factors influenced it. That can delay deployment, and every delay adds to the cost of manual fraud reviews, underwriting, and other processes AI was meant to improve. While those issues are being resolved, competitors may already be putting similar systems into production. This blog explains how to avoid those bottlenecks when adopting AI in banking and finance. It covers the key considerations for implementing AI in banking, explores practical use cases, and takes a closer look at where generative AI fits into the financial services landscape. AI in banking means using machine learning, language models, and automated decision systems to do work that banks used to handle with static rules and people: scoring credit, spotting fraud, answering customers, pricing risk, and reading large volumes of documents. A gradient-boosted fraud model retrained weekly and a retrieval-based assistant that answers policy questions are both artificial intelligence banking systems, but they sit under completely different control regimes. Where the system lands depends on who it affects and how reversible the decision is. In general, the use of AI and ML in banking splits into two jobs: systems that rank and flag, and systems that decide.

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