Açıklanabilir yapay zeka ile finansal dolandırıcılık tespiti

Fraud detection is a critical challenge in the financial industry, as the growing volume of transactions increases the risk of fraudulent activities, leading to significant financial and reputational harm. This thesis aims to address this challenge by evaluating advanced machine learning and deep learning models designed to enhance the accuracy, efficiency, and scalability of fraud detection systems, ultimately contributing to more secure financial operations. The study evaluates 14 machine and deep learning models encompassing both supervised and unsupervised learning techniques across four sampling techniques: imbalanced data, random undersampling, SMOTE, and majority voting with random undersampling on a private and a public dataset. Each model's effectiveness is compared and analyzed, highlighting their suitability for fraud detection tasks. The study also integrates explainability techniques to uncover the most influential features contributing to fraud detection by using feature importance, SHAP, LIME and DeepLIFT. The results demonstrate the efficacy of the evaluated models in detecting fraudulent activities, with Random Forests achieving the best performance among machine learning models and TabTransformers leading among deep learning models. This research contributes to the financial industry's efforts in combating fraud by presenting a comprehensive evaluation of machine learning and deep learning models, identifying optimal strategies for data sampling, feature engineering, and enhancing model interpretability to support informed decision-making.