Predictive Analytics in Banking Fraud Detection

Challenge

• High volume of false positives in anti-fraud alerts
• Manual case reviews slowing down transaction approvals
• Lack of real-time behavioral profiling

Solutions Delivered by Netlogg

• Developed a real-time anomaly detection engine using supervised ML algorithms
• Ingested transaction, device, and behavioral data across mobile and web platforms
• Integrated model outputs with fraud analyst workflows in the core banking system
• Continuously tuned model based on live feedback loops

Results

• False positives reduced by 57% in 3 months
• Fraud detection lead time improved by 70%
• Transaction approval rates increased with no added risk
• Team productivity increased via case prioritization and intelligent alerting