Detect Fraud.
Prevent Loss.
Protect Welfare.
ML-powered system analyzing beneficiary data, transactions, and behavioral patterns to identify anomalies and prevent welfare fraud.
System Performance
Real-time metrics from our AI fraud detection infrastructure
Eliminating Fraud in Welfare Systems
Traditional systems fail to detect complex fraud patterns. Our AI-driven approach ensures transparency and accuracy.
The Problems
Duplicate Beneficiaries
Same individual registered multiple times under different identities, draining resources.
Ghost / Fake Identities
Fabricated identities created to funnel welfare funds to non-existent people.
Multiple Scheme Abuse
Individuals exploiting loopholes to receive benefits from multiple welfare programs simultaneously.
Unusual Transaction Patterns
Suspicious fund movements that go undetected by traditional rule-based monitoring systems.
Our Solutions
AI-Based Duplicate Detection
Machine learning algorithms identify potential duplicates with 99.2% accuracy using fuzzy matching.
Identity Verification via Data Patterns
Cross-references government databases to validate authentic identities in real-time.
Cross-Scheme Linking Detection
Analyzes relationships between beneficiaries across all schemes to flag potential fraud rings.
Behavioral Anomaly Detection
Continuous monitoring with ML models that learn normal patterns and flag deviations instantly.