Claim Fraud Detection (ML): Framing Report

1. Context: Why Claim Fraud Detection Exists at All

Insurance fraud is not a rare anomaly — it is a structural cost of the insurance business.

Claims processes are designed to be fast, customer-friendly, and scalable. Fraud exploits exactly those properties. The result is a persistent tension between:

• speed vs. scrutiny
• customer trust vs. loss prevention
• human judgment vs. operational scale

Fraud does not appear as a single, well-defined pattern. It ranges from deliberate fabrication to exaggeration, opportunistic timing, and inconsistencies that may also arise from stress, error, or misunderstanding.

From a product and operations perspective, the challenge is not to eliminate fraud, but to manage uncertainty efficiently.

2. Business Problem Statement

The core business problem addressed in this project is:

This reframing is intentional.

• The goal is not automatic rejection of claims.
• The goal is decision support.

A useful system must help humans decide where to look, not decide what is true.

3. Operational Reality & Constraints

In real insurance operations:

• Fraudulent claims represent a small minority of total claims
• Manual investigation is expensive, slow, and capacity-limited
• Many signals emerge only after initial claim submission
• Investigators must justify decisions to customers, regulators, and courts

This imposes hard constraints:

• Every false positive has a real cost (customer friction, legal risk)
• Every false negative has a real cost (financial loss, precedent)
• Investigators cannot review everything — triage is unavoidable

Therefore, the problem is best understood as a ranking and prioritization task under capacity constraints, not a pure binary classification problem.

4. System Objective (Product View)

The system developed in this project aims to:

• Assign a risk score to incoming insurance claims
• Enable early prioritization for human review
• Support threshold-based workflows aligned with investigation capacity
• Provide signals that are interpretable and auditable

This aligns with how fraud detection systems are actually deployed in production environments.

5. Dataset Context

This project is based on the publicly available dataset:

insurance_claims.csv
Published: 22 August 2023
Version: 2
DOI: 10.17632/992mh7dk9y.2
Contributor: Abdelrahim Aqqad

Each row represents a single insurance claim. Columns describe customer history, policy attributes, incident details, and administrative signals.

The target variable fraud_reported indicates whether a claim was ultimately classified as fraudulent following a combination of manual review and automated checks.

Key contextual characteristics:

• Data aggregated from multiple insurance providers
• Covers vehicle, property, and personal injury claims
• Sensitive identifiers are anonymized
• Labels reflect post-hoc decisions, not objective ground truth

This framing has direct implications for modeling and evaluation.

6. Important Assumptions & Limitations

This dataset supports supervised learning, but only under explicit assumptions:

• Label uncertainty: Fraud labels reflect institutional decisions, not absolute truth.
• Temporal ambiguity: Some features may encode information unavailable at claim submission time.
• Human bias: Historical fraud decisions may reflect procedural or demographic biases.
• Class imbalance: Fraud cases are rare by nature, shaping both modeling strategy and metrics.

These issues are acknowledged at the framing stage to prevent methodological errors later.

7. Formal Problem Definition

For the purposes of this project, the task is defined as:

This definition intentionally excludes:

• Automated claim rejection
• Post-investigation features
• Assumptions of perfect labels

8. Success Criteria (Non-Technical)

A successful system should:

• Rank higher-risk claims above lower-risk ones
• Allow threshold tuning based on investigation capacity
• Reduce average investigation effort per detected fraud
• Remain explainable to investigators and auditors

Raw accuracy alone is insufficient and potentially misleading in this context.

9. Ethical & Governance Considerations

Even at the framing stage, several risks are explicit:

• Disparate impact through proxy variables
• Over-reliance on probabilistic outputs
• Misuse as an automated decision authority

This project treats these as design constraints, not afterthoughts. Bias, leakage, and explainability will be addressed in dedicated stages.

10. Project Roadmap

This report establishes the conceptual and operational foundation. Subsequent reports will cover:

• Exploratory Data Analysis (EDA)
• Feature Engineering & Representation
• Data Leakage Prevention
• Bias & Fairness Analysis
• Model Selection & Evaluation
• Calibration & Threshold Strategy
• Human-in-the-Loop Integration

Each stage builds directly on the framing decisions documented here.