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Journals / International Journal of Computers in Education / 2026 / Volume: 9 Issue: 1

Credit Card Fraud Detection with Machine Learning: An Integrated Use of Class Imbalance and Explainability Approaches

Makine Öğrenmesi ile Kredi Kartı Sahtekarlığı Tespiti: Veri Dengesizliği ve Açıklanabilirlik Yaklaşımlarının Bütünleşik Kullanımı

Soykan Gürdal(İzmir Demokrasi Üniversitesi)0009-0001-5902-8230
Mustafa Yurtsever(İzmir Demokrasi Üniversitesi)0000-0003-2232-0542
Pages
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Publication Date
2026-06-30
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AI Summary & Key Takeaways

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

Results suggest that a strong model architecture can simultaneously satisfy performance and explainability objectives.

📌 3 Key Takeaways

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Objective & Hypothesis:Credit card fraud poses a critical threat to financial institutions.
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Key Findings & Data:A publicly available European credit card dataset comprising 284,807 transactions (0.172% fraud) was used, with a stratified 80/20 train-test split.
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Conclusion & Impact:Results suggest that a strong model architecture can simultaneously satisfy performance and explainability objectives.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:credit card fraud · class imbalance · SMOTE

Abstract

Credit card fraud poses a critical threat to financial institutions. Existing studies rarely address class imbalance management, explainable AI, and counterfactual analysis within a unified framework; this study aims to fill that gap. A publicly available European credit card dataset comprising 284,807 transactions (0.172% fraud) was used, with a stratified 80/20 train-test split. Nine configurations combining three classifiers (Logistic Regression, Random Forest, XGBoost) with three balancing strategies (original, SMOTE, cost-sensitive) were evaluated using the F2 score, which prioritises false negative costs. XGBoost with the original strategy achieved F2=0.8524 and PR-AUC=0.8783; SMOTE and cost-sensitive strategies fell below the original strategy average (F2=0.7909). Multi-layer SHAP analysis consistently identified V14 (|SHAP|=1.74) and V4 (|SHAP|=1.72) as primary determinants across global, local, and interaction levels. Counterfactual analysis showed that shifting only three features (V14, V10, V4) reversed the fraud decision. Results suggest that a strong model architecture can simultaneously satisfy performance and explainability objectives.

Özet

Kredi kartı sahtekarlığı finansal kurumlar için kritik bir tehdit oluşturmaktadır. Literatürde veri dengesizliği, açıklanabilir yapay zekâ ve karşı-olgusal analiz bütünleşik ele alınmamaktadır; bu çalışma bu boşluğu gidermeyi amaçlamaktadır. 284.807 işlem içeren Avrupa kredi kartı veri seti (%0,172 sahtekarlık) üzerinde tabakalı örneklemeyle %80/20 eğitim-test ayrımı uygulanmıştır. Üç sınıflandırıcı (Lojistik Regresyon, Rastgele Orman, XGBoost) üç dengeleme stratejisiyle (orijinal, SMOTE, maliyet duyarlı) birleştirilen dokuz konfigürasyon, hatalı negatiflere öncelik veren F2 skoru üzerinden değerlendirilmiştir. XGBoost orijinal stratejiyle F2=0,8524 ve PR-AUC=0,8783 elde etmiş; SMOTE ve maliyet duyarlı stratejiler ortalama F2 açısından (0,7909) gerisinde kalmıştır. Çok katmanlı SHAP analizi V14 (|SHAP|=1,74) ve V4 (|SHAP|=1,72)’ü global, lokal ve etkileşim düzeyinde tutarlı biçimde birincil belirleyiciler olarak ortaya koymuştur. Karşı-olgusal analiz, yalnızca üç özelliğin (V14, V10, V4) kaydırılmasının fraud kararını tersine çevirdiğini göstermiştir. Bulgular, güçlü model seçiminin performansı ve açıklanabilirliği eş zamanlı karşılayabileceğine işaret etmektedir.

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Cite This Article

Gürdal, S., Yurtsever, M. (2026). Credit Card Fraud Detection with Machine Learning: An Integrated Use of Class Imbalance and Explainability Approaches. International Journal of Computers in Education, Volume: 9 Issue: 1. https://doi.org/10.64693/ijce.1955891

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