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