AI in fintech
AI in fintech: underwriting and fraud, personalization, support automation, and the fairness/regulatory constraints.
AI is reshaping fintech across credit decisioning, fraud detection, trading, underwriting, and customer engagement, but hype often outpaces deployable value. This block builds working fluency in how AI actually functions inside financial products and workflows, where it creates measurable returns versus where it adds cost without benefit, and what governance a regulated, high-stakes sector demands. You will move from core mechanics of machine learning and generative AI to a disciplined view of use cases across the fintech value chain, then to the risk, model governance, and regulatory frameworks (including AI-specific rules layered on financial regulation) that determine whether a solution can be safely and profitably deployed. The goal is sector fluency, not hands-on model building.
What you'll master
- Explain core AI and ML concepts using fintech-specific examples like credit scoring and fraud models
- Map where AI genuinely adds value across the fintech value chain versus where adoption is hype-driven
- Evaluate a proposed AI solution's ROI, feasibility, and adoption risks using realistic, sector-relevant criteria
- Identify model risk, key AI risks, and regulatory requirements, and apply pre-deployment governance checks