ItemConference Paper

Credit Card Fraud Detection with ADASYN Oversampling and SHAP-based Interpretability: A Comparative Ensemble Approach

Title
Credit Card Fraud Detection with ADASYN Oversampling and SHAP-based Interpretability: A Comparative Ensemble Approach
Creator
Malhotra, Amit; Hada, Bhupendra Singh; Mishra, Anchal; Chandan; Basha, Md Shaik Amzad
Description
Credit card fraud continues to be a significant threat to financial systems, exacerbated by the highly imbalanced nature of transaction datasets and the opaque decision-making of complex machine learning models. This paper proposes a hybrid fraud detection framework that integrates Adaptive Synthetic (ADASYN) oversampling to address class imbalance and SHAP (SHapley Additive exPlanations) to enhance model interpretability. Five machine learning classifiers Logistic Regression, Random Forest, XGBoost, LightGBM, and Multilayer Perceptron - are evaluated on the widely used Kaggle credit card fraud dataset. ADASYN significantly improves the minority class representation in the training set, enabling models to achieve higher fraud recall without overwhelming false positives. Among the models tested, Random Forest delivered the best trade-off between precision (85.7%) and recall (79.6%), achieving an F1-score of 82.5% and ROC-AUC of 0.9633. SHAP analysis provided granular insight into feature contributions, transforming black-box predictions into transparent and auditable decisions. Comparative analysis with eight state-of-the-art studies demonstrates that while recent approaches often report near-perfect results, the proposed model strikes a balance between predictive performance, computational efficiency, and interpretability qualities essential for practical deployment in financial fraud detection systems based on benchmark transactional data. The study highlights that integrating ADASYN with ensemble learning and SHAP can create a robust, explainable, and scalable fraud detection system suitable for deployment in dynamic financial environments. 2025 IEEE.
Source
Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.891-899
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
ADASYN; Class Imbalance; Credit Card Fraud Detection; Ensemble Learning; Explainable AI; Machine Learning; Random Forest; SHAP
Coverage
Malhotra A., Finance and Accountancy CHRIST (Deemed to Be University), School of Commerce, Ghaziabad, India; Hada B.S., Finance and Accountancy CHRIST (Deemed to Be University), School of Commerce, India; Mishra A., Institute of Management Studies, Ghaziabad, India; Chandan, Symbiosis International (Deemed University), Symbiosis Centre for Management Studies, Bengaluru, India; Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), GITAM School of Business, Hyderabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151175-3;
Format
online
Language
English
Type
Conference paper

Citation

Malhotra, Amit; Hada, Bhupendra Singh; Mishra, Anchal; Chandan; Basha, Md Shaik Amzad, “Credit Card Fraud Detection with ADASYN Oversampling and SHAP-based Interpretability: A Comparative Ensemble Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 22, 2026, https://archives.christuniversity.in/items/show/26031.

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