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Journals / Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi / 2026 / Cilt: 5 Sayı: 1

Riskified Fraud Detection Using Machine Learning: Insurance Claims

Makine Öğrenmesi Kullanarak Riskli Dolandırıcılık Tespiti: Sigorta Talepleri

Hakan Kaya(İstanbul Okan Üniversitesi)0000-0002-0812-4839
Pages
39–56
Publication Date
2024-04-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

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Objective & Hypothesis:In the insurance industry, fraud presents a significant and widely recognized challenge.
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Key Findings & Data:GridSearchCV, Confusion Matrix and Classification Report methods (Accuracy, Precision, Recall, and F1-Score) of the used to calculate and display all metrics.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sigorta · Dolandırıcılık Tespiti · Makine Öğrenimi

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.

Özet

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.

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Cite This Article

Kaya, H. (2024). Riskified Fraud Detection Using Machine Learning: Insurance Claims. Malatya Turgut Özal Üniversitesi İşletme ve Yönetim Bilimleri Dergisi, Cilt: 5 Sayı: 1, 39–56. https://ajindex.com/articles/1595824

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