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
Key terms
Modules
Covers core AI applications in lending, personalization, support, and compliance within regulated finance.
Covers identifying AI use cases, evaluating vendors, and modelling ROI and success metrics.
Covers AI regulation, model risk management, and checks that keep fintech products safe.
Latest articles
Recent articles from the blog that apply to Fintech.
- 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 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.
- Bloomberg's bet on fine-tuning: what it teaches every enterprise about the RAG-vs-fine-tune decisionBloomberg built a domain-specific large language model from scratch rather than retrieving over generic ones, and the results clarified a decision that still confuses most enterprise AI teams. The logic behind that choice, and where it breaks down for other organizations, is more instructive than the model itself.
- 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.
- Where AI agents help and where they break: lessons from KlarnaKlarna ran one of the most cited enterprise deployments of AI agents in financial services, and the results were genuinely mixed. Here is what actually happened, what the numbers mean, and what any organization should take from it before committing to agent-based automation.