Makine Öğrenmesi Kullanarak Riskli Dolandırıcılık Tespiti: Sigorta Talepleri

Year 2024, Volume: 5 Issue: 1, 39 - 56, 30.04.2024

Hakan Kaya

Abstract

Sigorta sektöründe dolandırıcılık önemli ve yaygın olarak kabul edilen bir sorundur. Sahte iddiaların sigortacılara önemli bir mali yük getirdiği göz önüne alındığında, meşru ve sahte iddialar arasında ayrım yapmak çok önemlidir. İlgili zaman ve maliyet nedeniyle her iddiayı manuel olarak incelemenin pratik olmadığı göz önüne alındığında, gelişmiş teknolojinin kullanılması zorunlu hale gelmektedir. Bu çalışmanın amacını, sigorta endüstrisinde dolandırıcılığı tespit etmek için makine öğrenimi algoritmalarıyla tahmin modellerinin kullanıldığı bir çerçeve oluşturmaktır. Çalışma için özel bir sigorta şirketinin hasar kayıtlarından bir veriseti hazırlanmıştır. Dolandırıcılık tespiti mekanizması geliştirmek için on bir tahmin modeli (Ada Boost, Cat Boost, Decision Tree, Extremely Randomized Tree, Gradient Boosting, KNN, LightGBM, Random Forest, Stochastic Gradient Boosting (SGB), Support Vector Classification (SVC) ve Voting Classifiers) uygulanmaktadır. Algoritmalar doğruluk değeri açısından karşılaştırılacak, en iyi değerleri veren algoritma belirlenecektir. Tüm metrikleri hesaplamak ve görüntülemek için GridSearchCV, Karmaşıklık Matrisi ve Sınıflandırma Raporu yöntemleri (Doğruluk, Kesinlik, Geri Çağırma ve F1-Puanı) kullanılmıştır. Bu çalışmanın sonucunda, Random Forest ve Decision Tree algoritmaları %75,6 ile en yüksek sınıflandırma doğruluğuna sahip olarak diğer modellerden daha iyi performans göstermiştir. Bu çalışmanın bulguları, sigorta sektöründe dolandırıcılık tespiti için faydalı ve temel çerçeve, sigorta sektöründe gerçek zamanlı problem çözme için bir işlevselliğe sahiptir.

Keywords

Sigorta, Dolandırıcılık Tespiti, Makine Öğrenimi, Makine Öğrenimi Algoritmaları

Thanks

I would like to thanks everyone who contributed to the publication process, especially the referees and the editorial board.

Riskified Fraud Detection Using Machine Learning: Insurance Claims

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

Insurance, Fraud Detection, Machine Learning, Machine Learning Algorithms

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