A Machine Learning and Explainable Artificial Intelligence approach for Insurance Fraud Classification

Abstract

This study addresses the critical issue of fraud in the vehicle
insurance market by introducing a comprehensive framework that employs advanced detection models, including
explainable artificial intelligence (XAI) and heterogeneous
classifiers to effectively identify fraudulent activities. The
incorporation of XAI is particularly noteworthy, as it provides a set of frameworks and resources that enhance the
interpretability and transparency of machine learning algorithm detections, a crucial advancement for the integrity
and trustworthiness of insurance operations.
Our methodology demonstrates the application of three
distinct XAI techniques—Shapley Additive Values (SHAP),
Explain Like I’m 5 (ELI5), QLattice, and Local Interpretable Model-agnostic Explanations (LIME)—to elucidate the machine learning model’s decision-making process.
This approach not only ensures that our detection models
are interpretable but also allow us to identify the most significant factors influencing fraud detection: the age of the
vehicle, the base policy, fault, deductible, and the policy
holder’s age.
The standout contribution of our research is the development and validation of a multi-stack machine-learning
model configuration that achieves an unprecedented accuracy rate of 96%, markedly surpassing the performance of
conventional classifiers also convey our model interpretation through XAI. This achievement highlights the efficacy
and the potential of our proposed framework to revolutionize
fraud detection practices in the vehicle insurance sector.
By providing a robust, accurate, and interpretable solution to combat fraud, this study makes a significant contribution to the field, offering valuable insights and tools
for insurance providers seeking to enhance their fraud detection capabilities. The integration of machine learning
and XAI in our framework not only addresses a pressing
challenge within the insurance industry but also sets a new
benchmark for the development of advanced, reliable fraud
detection systems.