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Fraud Detection in Health Insurance Claims Based on Artificial Intelligence (AI)

Aditya Kurniawan

2025enhealth insurancefraud detectionartificial intelligencemachine learningdeep learningdecision support system

Abstract

Language:

Health insurance claim fraud, involving providers, beneficiaries, and insurance companies, significantly disrupts healthcare systems worldwide. This study aims to develop an artificial intelligence–based decision support system that automatically detects fraudulent health insurance claims. Using deep learning as the primary machine learning technique, the model is trained on financial transaction data (BankSim/Kaggle) to identify anomalous claim patterns. The system is designed to integrate knowledge representation, business intelligence, and fraud detection models within a user-friendly graphical interface. Experimental results show that the proposed deep learning approach achieves rapid, stable convergence and an accuracy of 86% in detecting fraudulent claims. The work also compares existing methods such as K-Means, Naïve Bayes, C4.5, and Support Vector Machines, highlighting their limitations and motivating the choice of deep learning for improved detection performance.

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Cite This Work

@article{17e6666b-2f01-4917-b30a-f32974e85bed,
  title={Fraud Detection in Health Insurance Claims Based on Artificial  Intelligence (AI)  },
  author={Aditya Kurniawan},
  year={2025},
  language={en}
}
TY  - JOUR
TI  - Fraud Detection in Health Insurance Claims Based on Artificial  Intelligence (AI)  
AU  - Aditya Kurniawan
PY  - 2025
LA  - en
ER  -

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