Cross-Program Fraud Detection - IBR-033

IBR ideally should implement a Cross-Program Fraud Detection capability with advanced analytics to identify potential fraud or errors across multiple social protection programs, using real-time data exchange to prevent duplicate or fraudulent claims.

Enhances program integrity and prevents improper benefit allocation by leveraging both integrated data access and analytical tools.

Implement a Cross-Program Fraud Detection capability with advanced analytics to identify potential fraud or errors across multiple social protection programs.

Beneficiary ID, Fraud Detection Data, Program Participation Data

• Interface: REST API as an example interface for fraud data exchange.

• Analytics: Rule-based detection using Python scripts.

• Data Sharing: Batch processing for fraud alerts across programs.

• Interface: GraphQL as an example interface for real-time fraud detection data sharing.

• Analytics: Machine Learning-based anomaly detection to identify potential fraud.

• Data Sharing: Real-time alerts using Apache Kafka.

• Interface: Federated GraphQL as an example interface for real-time AI-driven fraud detection.

• Analytics: Deep Learning for pattern recognition across programs.

• Data Sharing: Streaming analytics with Apache Pulsar and AI-driven proactive alerts.

Secure interface access for fraud data, encryption for fraud data sharing.

Machine learning-based fraud detection for scalability.

Integration with multiple program data sources for fraud detection.

Compliance with GDPR for fraud detection data sharing.

N/A