Riskified Fraud Detection Using Machine Learning: Insurance Claims

Authors : Hakan Kaya

Pages : 39-56

View : 65 | Download : 102

Publication Date : 2024-04-30

Article Type : Research Paper

Abstract :In the insurance industry, fraud presents a significant and widely recognized challenge. With fraudulent claims posing a substantial financial burden on insurers, it\'s crucial to distinguish between legitimate and false claims. Given the impracticality of manually scrutinizing every claim due to the associated time and cost, employing advanced technology becomes imperative. This article delves into utilizing predictive models powered by machine learning algorithms to analyze claim data. For the study, a dataset was prepared from the damage records of a private insurance company. Eleven predictive models (Ada Boost, Cat Boost, Decision Tree, Extremely Randomized Tree, Gradient Boosting, KNN, LightGBM, Random Forest, Stochastic Gradient Boosting (SGB), Support Vector Classification (SVC), and Voting Classifiers) are applied for developing a fraud detection mechanism. Algorithms will be compared in terms of score the algorithm that gives the best values will be determined. GridSearchCV, Confusion Matrix and Classification Report methods (Accuracy, Precision, Recall, and F1-Score) of the used to calculate and display all metrics. As a result of this study, the Random Forest and Decision Tree Classifiers outperformed the other models with have the highest classification accuracy of 75.6%. The findings of this study are beneficial for fraud detection and the underlying framework holds a functionality for real-time problem-solving in the insurance sector.
Keywords : Sigorta, Dolandırıcılık Tespiti, Makine Öğrenimi, Makine Öğrenimi Algoritmaları