Oritogun Femi

I’ve spent 6+ years in fintech and banking working across fraud detection, credit risk, AML/KYC reporting, and customer analytics for 2M+ users. I rely on hands-on SQL and Python to build churn modelling, CLTV scoring, and behavioral anomaly detection systems.

At Raenest Technologies, I built a real-time Behavioral Spike Intelligence System that flags anomalies in transaction velocity, user behavior, and funnel activity—helping improve treasury risk monitoring by 30% and enabling early fraud detection. I also uncovered $100K+ in high-risk transactions by integrating Stripe, Kredi, and internal wallet data into a fraud and chargeback analytics framework.

Previously at Sterling Bank Plc, I reactivated 43% of dormant users using a retention intelligence framework built on behavioral drop-off patterns and hyper-personalized re-engagement campaigns. I improved onboarding completion by analyzing the end-to-end KYC funnel and reduced incident response time by 37% with automated real-time alerts for high-value transaction failures and irregular behavior.

Before that, I worked on data products in both banking and operations—building self-service reporting and automating risk monitoring pipelines, plus real-time inventory and logistics dashboards in a remote supply chain role. My focus stays on making complex data actionable for product, risk, and executive stakeholders.