Detect Fraud Before
the Loss Is Confirmed
A 3-layer detection pipeline — rule-based controls, behavioral anomaly scoring, and supervised machine learning — that turns raw transactional data into an investigation-ready fraud case queue.
Detection Pipeline
Deterministic · 100% Traceable
Unsupervised · No Labels Required
Adaptive · Self-improving
Investigation-Ready Case Queue
The Problem We Solve
How the Fraud Happens
Premium collection leakage — agent collects but never remits
Premium Collected
Agent collects from client
Receipt Unverified
No payer ID recorded
Funds Diverted
Never remitted to company
Payment Overdue
Policy at risk, loan taken out
Policy Lapses
Loss discovered in audit
Our Approach
3-Layer Detection Pipeline
Rule-Based Engine
ExplainableDeterministic business rules derived from EDA and domain knowledge. Every alert is 100% traceable to a specific data point — fully auditable.
Anomaly Detection
No Labels NeededUnsupervised multi-signal scoring that catches unusual behavioral patterns — no labeled data required, works from day one.
Supervised Model
AdaptiveProbability-based fraud prediction trained on labeled cases. Learns continuously through a human-in-the-loop retraining cycle.
Data Foundation
What Data Does the System Use?
6 source tables — each contributing a different dimension of fraud signal
Policy Master
Core policy lifecycle — status, coverage period, premium amount, and assigned agent
Payment Receipts
Premium collection records — payment channel, amount, and payer identity verification
Policy Loans
Loans taken against active policies — amount, date, and whether the agent is the receiver
Payment Due Dates
Scheduled premium due dates used to calculate overdue gaps and reconciliation delays
Status History
Month-over-month policy status snapshots for detecting rapid In-force → Lapsed transitions
Agent Network
Family relationship graph among agents — used to detect coordinated network-level fraud
Key Advantages
Why This Approach Works
Fully Explainable
Every alert links back to a rule, field, and threshold. No black box — investigators always know why.
Works from Day One
Rule-based and anomaly layers need zero labeled fraud data. No cold-start problem.
Human-in-the-Loop
Investigators review and label cases. Verdicts feed back into the model — continuously improving accuracy.
Audit-Ready Evidence
Each case dossier contains receipts, loans, status history, and rule evidence for compliance reporting.
Platform Modules
Everything in One Place
Detection Pipeline
EDA Dashboard
Explore fraud signals from raw data
Rule-Based Engine
Run deterministic fraud rule checks
Anomaly Detection
Score agents by behavioral anomalies
Supervised Model
Predict fraud probability per agent-policy
Case Investigation
Unified investigator dossier and decision log
Controls, Operations & Reference
Ready to explore the platform?
Walk through each module with mock data — or import your own CSV files to validate against real agent portfolios.