Real-time personalization is reshaping financial services, from fraud prevention and credit decisions to customer engagement and pricing. This session breaks down the end-to-end data and AI architecture required to deliver low-latency, high-confidence personalization at scale, while balancing accuracy, cost, and regulatory risk. We’ll explore key tradeoffs across streaming data pipelines, feature stores, model serving, and human-in-the-loop controls, with a practical lens on governance, fairness, and explainability in highly regulated environments.

What attendees will gain

A clear understanding of how real-time personalization systems in finance are architected end to end

Practical tradeoffs between latency, accuracy, cost, and compliance when operating at scale

Concrete patterns for streaming data, feature engineering, and model serving in regulated environments

A risk-aware framework for deploying personalization responsibly, including governance, fairness, and explainability

Actionable insights attendees can apply to their own fraud, credit, or customer experience use cases