Skip to content

Overview

The Insurance Fraud Detection system uses a multi-layered approach to identify potentially fraudulent claims.

Detection Philosophy

Healthcare fraud detection requires balancing:

  1. Precision: Minimizing false positives that waste investigator time
  2. Recall: Catching as much actual fraud as possible
  3. Explainability: Providing clear reasons for each flag

Our approach combines multiple detection methods, each contributing to a composite fraud score.

Detection Methods

1. Rule-Based Detection

Explicit business rules based on domain knowledge:

  • Billing patterns: Daily procedure limits, claim frequency
  • Geographic rules: Provider-patient distance, state mismatches
  • Temporal patterns: Weekend billing, suspicious timing
  • Amount patterns: Round numbers, unusual charge ratios

Strengths: Interpretable, no training data required, domain expert input

Weaknesses: Can be circumvented, may miss novel fraud patterns

2. Statistical Detection

Data-driven anomaly detection:

  • Z-score outliers: Values far from the mean
  • IQR outliers: Values outside the interquartile range
  • Benford's Law: First-digit distribution analysis

Strengths: Catches unusual patterns without explicit rules

Weaknesses: Requires sufficient data, may flag legitimate outliers

3. Duplicate Detection

Identifies claim resubmissions:

  • Exact duplicates: Identical key fields
  • Near-duplicates: High similarity with minor changes

Strengths: High precision for a common fraud type

Weaknesses: Sophisticated fraud may evade detection

Fraud Score Calculation

Each claim receives a composite fraud score (0-1):

fraud_score = (rule_score * 0.30) +
              (statistical_score * 0.25) +
              (duplicate_score * 0.45)

Where:

  • rule_score: Fraction of rule violations (0-1)
  • statistical_score: Fraction of statistical flags (0-1)
  • duplicate_score: 1.0 if duplicate, 0.0 otherwise

These weights are somewhat arbitrary

Risk Categories

Based on fraud score, claims are categorized:

Category Score Range Recommended Action
High Risk > 0.7 Immediate investigation
Medium Risk 0.3 - 0.7 Queued for review
Low Risk ≤ 0.3 Standard processing

Output Schema

The detection output includes:

Field Type Description
claim_id string Original claim identifier
fraud_score double Composite score (0-1)
fraud_reasons array All triggered flags
rule_violations array Rule-based flags
statistical_flags array Statistical flags
is_duplicate boolean Duplicate detection result
duplicate_of string Reference claim ID if duplicate
processed_at timestamp Processing timestamp

Data Flow

graph TD
    A[Raw Claims] --> B[Apply Rules]
    B --> C[Statistical Analysis]
    C --> D[Duplicate Detection]
    D --> E[Score Calculation]
    E --> F[Flagged Claims]

    B --> G[Rule Violations]
    C --> H[Statistical Flags]
    D --> I[Duplicate Flags]

    G --> E
    H --> E
    I --> E

Extensibility

The system is designed for extension:

  • Add custom rules by extending BillingPatternRules
  • Implement new statistical methods in statistics/
  • Adjust scoring weights via DetectionConfig

See Development Guide for details.

ML Integration

Most of the weights and thresholds are somewhat arbitrary and can be optimized using machine learning techniques. For instance, the rule-based detection can be enhanced by training a model to predict fraud based on historical data. Similarly, the statistical detection methods can be refined to better capture fraudulent patterns.

This is just for demonstration purposes, and the actual implementation would involve more detailed steps and considerations.