Machine Learning For Fraud Detection Insurance Claims

Machine Learning For Fraud Detection Insurance Claims

InsurTech · Claims Integrity

Python · Feature Engineering · Cross-Validation · Model Serving

Operational Focus: identifying inflated and fraudulent insurance settlement claims using multimodal tabular and textual narratives.

Project Abstract & Technical Scope

This project introduces an end-to-end, production-ready machine learning framework for Machine Learning For Fraud Detection Insurance Claims. Within real-world operational environments, systems face severe obstacles including delayed claim verification ground truth, deliberate collusion rings, and high investigation costs. The objective of this work is to implement a robust, leak-free computational pipeline that translates raw inputs into deterministic, high-confidence decision metrics.

The system ingests and processes records sourced from historical claim adjuster reports, policyholder incident histories, repair bill itemizations, and collision telemetry. Raw attributes undergo automated data sanitization, multivariate imputation, distribution rebalancing, and outlier filtering. Continuous numerical features are scaled using robust statistical scaling techniques, while categorical, spatial, and temporal attributes receive cyclical encoding, high-cardinality target transforms, or dense embeddings.

Modeling evaluates diverse algorithmic paradigms: CatBoost Classifier, Cost-Sensitive XGBoost, TF-IDF + Ridge Classifier, Isolation Forest. Rigorous validation protocols employ stratified, temporal, or grouped cross-validation to prevent train-test contamination. Hyperparameter optimization is systematically executed via Bayesian search strategies (Optuna), targeting optimization of Recall on Severe Fraud, Cost-Weighted F-Beta, False Audit Investigation Ratio rather than uninformative global accuracy.

To ensure practical viability and regulatory transparency, global and local feature contributions are derived using TreeSHAP and Partial Dependence profiles. The winning configuration is serialized into portable ONNX format and served via an asynchronous FastAPI microservice equipped with telemetry logging for real-time concept drift detection.

Tools & Technologies

The standard modern data science stack utilized for feature extraction, model tuning, and REST deployment:

Modular Machine Learning Workflow

Candidate Algorithms Benchmarked

• • CatBoost Classifier
• • Cost-Sensitive XGBoost
• • TF-IDF + Ridge Classifier
• • Isolation Forest

Final production selection is based on cross-validated Pareto efficiency balancing Recall on Severe Fraud, Cost-Weighted F-Beta, False Audit Investigation Ratio against inference latency.