Fraud Analytics & Predictive Monitoring for Credit Unions

This project focused on designing a predictive fraud analytics and monitoring framework for small and community credit unions. The primary objective was to detect potentially fraudulent activity early, minimize financial losses, and reduce dependency on manual fraud reviews.

The solution analyzed fraud patterns across multiple channels including debit cards, credit cards, ACH transactions, and account takeover scenarios. By identifying high-risk transaction behaviors and member activity patterns, the framework enabled early fraud detection and faster response, shifting fraud management from reactive reporting to proactive prevention.

Analyzed historical fraud and transaction data across multiple payment channels

Identified abnormal spending, velocity spikes, location mismatches, and behavioral anomalies

Built early-warning fraud indicators using rule-based logic and predictive analytics

Designed real-time fraud monitoring dashboards for operational teams

Focused on reducing false positives while maintaining strong fraud detection coverage

Analyzed transaction-level data for:

Identified fraud indicators such as:

Developed risk-scoring logic to flag suspicious transactions early

Assisted in fraud rule optimization to balance detection accuracy and false positives

Designed early-warning indicators for emerging fraud trends

Built fraud risk monitoring dashboards to provide:

Provided actionable recommendations for: