Technology and Features
Built entirely on Google Cloud Platform with unsupervised machine learning, privacy-preserving technology, and explainable AI for transparent governance.
Key Capabilities
Powerful features for intelligent welfare monitoring
Anomaly Detection Without Labeled Fraud Data
JanAvlokan uses unsupervised learning to identify deviations from normal behavior, eliminating the dependency on pre-labeled fraud datasets which are rare and delayed in public finance systems.
Privacy-Safe Collusion Detection
The platform detects patterns such as shared bank accounts or devices using irreversibly hashed identifiers, enabling detection of coordinated misuse while fully preserving beneficiary privacy.
Policy-Aware Risk Calibration
Risk thresholds dynamically adapt based on scheme type, region, and time period. Seasonal surges and policy-driven variations are accounted for to reduce false positives.
Explainable Audit Narratives
Each flagged case is accompanied by a human-readable explanation outlining contributing behavioral signals designed for administrative review, audits, and legal defensibility.
Geographic Risk Heatmaps
Aggregated risk scores are visualized at district or block levels, allowing administrators to identify regional concentrations of anomalous behavior and allocate audit resources efficiently.
Real-Time Processing
Built on Google Cloud Platform for scalability and reliability, JanAvlokan can process 100M+ transactions using distributed computing and optimized data pipelines.
CSV Quick Scan
Upload beneficiary transaction data in CSV format for instant anomaly scoring. The system validates columns, runs ML inference via the Vertex AI endpoint, and returns per-row risk levels and flags within seconds.
Audit Panel with ML Feedback Loop
Each flagged beneficiary can be reviewed through an integrated Audit Panel. Officers can verify fraud, dismiss false positives, or escalate cases. Feedback is stored and used to retrain models, enabling Active Learning.
Report Builder & Transaction Linker
Generate audit-ready reports with a collaborative rich-text editor. Link specific flagged transactions as evidence, add findings with severity ratings, and export reports to PDF or DOCX for official submission.
Automated Email Alerts
Automatically notify district-level officials via email when high-risk anomalies are detected in their jurisdiction. Includes risk summary, flagged beneficiary details, and direct links to the dashboard for immediate action.
Cloud-Native Architecture
Scalable, secure, and designed for enterprise-grade deployments
| S.No. | Service | Description |
|---|---|---|
| 1 | Cloud Storage | Secure storage of anonymized raw datasets with encryption at rest |
| 2 | Dataflow | Distributed ETL and feature engineering pipelines for scalable processing |
| 3 | BigQuery | Analytics warehouse handling 100M+ transactions with partitioning and clustering |
| 4 | Vertex AI | Model training, versioning, batch prediction, and inference endpoints |
| 5 | Cloud Run | Serverless backend APIs for dashboard and real-time data access |
| 6 | Gmail API | Automated email alerts to district officials when high-risk transactions are detected |
| 7 | Web Dashboard | Interactive interface for anomalies, explanations, and regional insights |
Machine Learning Approach
Autoencoder-based unsupervised anomaly detection with BigQuery ML
Autoencoders
Neural networks trained in BigQuery ML that learn to compress and reconstruct normal beneficiary behavior patterns. High reconstruction error (MSE) indicates anomalous behavior.
Rule-Based Detection
Deterministic rule engine that generates human-readable flags (high recent activity, multiple dealers, cross-district usage) for audit explanations.
Ensemble Output
The hybrid ensemble combines outputs from all three models to produce:
(0-1 normalized)
(Low/Medium/High)
(Explainable)
Feature Signals Analyzed
- 1Rolling claim frequency patterns
- 2Deviation from personal baselines
- 3Deviation from scheme-level baselines
- 4Cross-scheme overlap detection
- 5Hashed shared identifier analysis
- 6Temporal spike indicators
- 7Geographic clustering signals
- 8Behavioral sequence modeling
Alert System & Risk Categorization
Real-time fraud pattern detection and intelligent alert prioritization
Five Key Fraud Risk Categories
JanAvlokan identifies and categorizes anomalous behavior into five distinct risk patterns, each representing a different mode of potential fraud or system misuse:
Unusual Activity
Abnormal transaction patterns, spikes, or irregular claim timing
Suspicious Locations
Geographic inconsistencies or claims from unexpected regions
Scheme Overlaps
Multiple scheme enrollments with conflicting eligibility criteria
Beneficiary Clusters
Groups sharing bank accounts, devices, or other identifiers
Repeat Withdrawals
Excessive claim frequency beyond normal beneficiary behavior
High-Priority Alert System
The platform continuously monitors transactions and generates real-time alerts when high-risk patterns are detected. Each alert includes:
Beneficiary Identifier
Anonymized hash for tracking while preserving privacy
Risk Score & Category
Numerical score (0-1) and HIGH/MEDIUM/LOW classification
Alert Type Description
Human-readable explanation (e.g., "Multiple dealers detected")
Timestamp
Exact detection time for audit trail purposes
Trend Analysis & Weekly Monitoring
Temporal trend analysis tracks weekly changes in fraud patterns, enabling proactive policy adjustments and resource allocation.
Privacy-First Design
Privacy is central to JanAvlokan's architecture. The system ensures compliance with data protection principles while maintaining analytical effectiveness.
This approach enables powerful anomaly detection while fully preserving beneficiary privacy and maintaining the trust essential for government systems.
No PII in Cloud
No personally identifiable information enters the cloud infrastructure
Irreversible Hashing
All sensitive identifiers are irreversibly hashed before processing
Location Generalization
Location data is generalized into regional clusters for privacy
Human-in-the-Loop
Outputs are strictly advisory with human decision-making
Scalability and Performance
Designed for national-scale deployment
Access System Dashboard
Monitor risk assessments and generate audit reports