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Research Article

JCR. 2021; 8(3): 261-270


A FEDERATED MODEL FOR INSURANCE CLAIMS FRAUD DETECTION

STEPHEN KATIECHI OKENO, DR. ENG. LAWRENCE MUCHEMI.

Abstract
Practical insurance fraud detection solutions require sufficient quality data from insurers to build effective models. However, insurance data is generally proprietary information for specific insurance companies and thus not publicly available. Also, the Insurance datasets are often imbalanced, making it challenging to develop fraud detection models that are not biased. Data privacy and class imbalance are two significant challenges when developing artificial intelligence applications in the insurance setup. In this research study, we tackle these challenges and propose a decentralized and privacy-preserving federated approach using an adjusted random forest model. The method is asynchronous federated learning of the traditional adjusted random forest classifier, i.e., achieving a higher performance and accuracy level than the traditional centralized learning approach. Based on it, we achieved secure collaborative machine learning that allows the training of quality federated fraud detection models from imbalanced data without sharing data. Experiments on Kaggle and Oracle insurance datasets demonstrate that the federated adjusted random forest classifier is more accurate and efficient than the non-federated counterpart. Our model is verified to be practical, efficient and scalable for real-life insurance fraud detection tasks.

Key words: Insurance Fraud Detection, Federated Learning, Adjusted Random Forests



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The articles in Bibliomed are open access articles licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
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