Insurance Fraud Detection Using Machine Learning and XAI Algorithms

Bolukbasi Z., Yurtoglu H., Kuğu E., Cukurtepe H.

2025 IEEE International Conference on Electro Information Technology, eIT 2025, Indiana, Amerika Birleşik Devletleri, 29 - 31 Mayıs 2025, ss.463-468, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/eit64391.2025.11103653
  • Basıldığı Şehir: Indiana
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.463-468
  • Anahtar Kelimeler: insurance fraud, machine learning, SMOTE, XAI
  • TED Üniversitesi Adresli: Evet

Özet

Insurance fraud poses a major challenge for the industry, raising expenses and premiums for insurers and policyholders alike. Deceptive claims raise operational expenses and cause elevated premiums, ultimately affecting the trustworthiness and effectiveness of the insurance sector. This study aims to investigate insurance fraud using 10 different machine learning models, namely Random Forest, Support Vector Machines, Gradient Boosting, K-nearest Neighbors, GaussianNaive Bayes, Extreme Gradient Boosting, Voting Classifier, AdaBoost, Decision Tree, and Logistic Regression. Among the models tested, the Voting Classifier model achieved the best performance, achieving both the F1-Score and the classification accuracy of 87%. Explainable Artificial Intelligence techniques, LIME and SHAP, are applied to explain the models' decisions. Both LIME and SHAP results show that incident-severity is the most significant feature in detecting insurance fraud.