Vol. 5 No. 1 (2025): Issue 5
Articles

MDD-based Domain Adaptation Algorithm for Improving the Applicability of the Artificial Neural Network in Vehicle Insurance Claim Fraud Detection

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  • Alan Wilson,
  • Jiahuai Ma
Alan Wilson
Intact Financial Corporation; Toronto, Ontario M5H 2N2; Canada
Jiahuai Ma
Corresponding, University of Florida; Gainesville; Florida 32611; USA
DOI: https://doi.org/10.71070/oaml.v5i1.76

Published 2025-02-13

Keywords

  • Vehicle insurance claim fraud detection,
  • Machine learning,
  • Neural network,
  • Domain adaptation

How to Cite

Wilson, A., & Ma, J. (2025). MDD-based Domain Adaptation Algorithm for Improving the Applicability of the Artificial Neural Network in Vehicle Insurance Claim Fraud Detection. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.76
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

Insurance fraud detection is a critical task for insurance companies, as fraudulent claims result in financial losses and increased premiums for honest policyholders. Traditional fraud detection methods rely on rule-based approaches and manual investigation, which are limited in their ability to adapt to evolving fraud patterns. In this study, we propose a novel approach using an artificial neural network (ANN) combined with Margin Disparity Discrepancy (MDD)-based domain adaptation to improve the generalization ability of fraud detection models across different datasets. We first preprocess the data by applying K-Means clustering to segment source and target domains based on distribution differences. We then compare multiple machine learning models, including decision trees, random forests, k-nearest neighbors, and gradient-boosted decision trees, finding that ANN achieves the best performance. To further enhance generalizability, we introduce MDD-based domain adaptation, aligning feature distributions between the source and target domains. Experimental results demonstrate that the adapted ANN significantly improves fraud detection accuracy, achieving a higher F1-score and recall while reducing the false negative rate. These findings highlight the effectiveness of domain adaptation in addressing distributional shifts in fraud detection, making the proposed model a promising solution for real-world insurance fraud detection systems.

 

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