AI in banking
AI in banking: fraud detection, credit decisioning, customer service, and the heavy constraints of explainability, fairness and regulatory model governance.
AI is reshaping banking across credit decisioning, fraud detection, trading, customer service, and compliance, but separating genuine capability from vendor hype requires structured judgment. This block builds fluency in how core AI concepts translate to banking's specific constraints: regulated decisions, legacy data infrastructure, and high-stakes model outcomes. You will map where AI creates real value across the banking value chain, learn to evaluate solutions and their ROI without falling for inflated claims, and understand the regulatory and risk frameworks (model risk management, fair lending, explainability) that govern deployment. The goal is sector-specific fluency: enough technical grounding to challenge vendors, assess business cases, and ask the right governance questions, without requiring you to build models yourself.
What you'll master
- Explain core AI and machine learning concepts using banking-relevant examples (credit scoring, fraud detection, forecasting)
- Identify where AI applications genuinely add value across the banking value chain versus where they overpromise
- Evaluate AI vendor solutions and business cases using realistic ROI and adoption criteria
- Apply governance checklists and model risk frameworks to assess AI deployment readiness in a regulated banking context
Key terms
Modules
Applies core AI concepts to concrete banking functions under sector-specific rules.
Covers mapping AI use cases and building ROI, TCO, and build-buy-partner decisions.
Covers the regulatory landscape, model risk, AI risk taxonomy, and pre-deployment checks.
Latest articles
Recent articles from the blog that apply to Banking.
- SR 11-7 still bites, and gradient boosting just made the wound worseSR 11-7 was written for logistic regression, but banks are now deploying gradient boosting, neural networks, and foundation model-powered scoring into production. This piece unpacks what explainability actually means under the guidance, why examiners are pushing harder on it in 2026, and where the governance frameworks genuinely break down.
- Building credit decisioning models that survive fair-lending scrutinyAI-driven credit models can cut decisioning time and expand credit access, but a single fair-lending violation can trigger enforcement actions that dwarf any efficiency gain. This playbook shows banking AI leaders how to build, document, and defend models that hold up when the OCC, CFPB, or DOJ come knocking.
- How Goldman Sachs built an AI usage policy that employees actually followedMost corporate AI policies sit in a shared drive and change nothing. Goldman Sachs took a different path, and the mechanics of how they did it offer a transferable model for any team serious about governing AI in practice.
- Right context, wrong assumption: what Morgan Stanley learned about prompting at scaleMorgan Stanley's deployment of an AI assistant for its financial advisors exposed a problem most teams overlook: feeding the model more information does not produce better answers. The real discipline is selecting which context matters, and why that distinction changes how you build prompts entirely.
- How Klarna turned customer service triage into a durable AI workflowKlarna rebuilt one of its highest-volume, most repetitive operations around an AI agent rather than bolting AI onto an existing process. The decisions they made, and the ones they got wrong initially, offer a practical template for any team facing a similar problem.
- How Klarna rewired its support operations with disciplined prompt engineeringKlarna's AI deployment in customer support became one of the most cited cases of LLMs producing measurable operational results. The prompt discipline behind it offers concrete lessons that transfer well beyond fintech.