TY - GEN
T1 - Explainable AI supported Evaluation and Comparison on Credit Card Fraud Detection Models
AU - Kotrachai, Chanisara
AU - Chanruangrat, Praewsuphang
AU - Thaipisutikul, Tipajin
AU - Kusakunniran, Worapan
AU - Hsu, Wen Chin
AU - Sun, Yi Chun
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This research investigates credit card fraud detection through the lens of machine learning and explainable AI techniques. We employed four distinct models: K-Nearest Neighbors (KNN), Random Forests, Extreme Gradient Boosting (XGBoost), and Logistic Regression. The SHapley Additive exPlanations (SHAP) method was used to enhance the interpretability of our models. Model performance was assessed using key metrics such as accuracy, precision, recall, and F1 score before and after feature selection. Notably, despite a decrease in some performance metrics post feature selection, high precision scores were maintained, underscoring the robustness of our models. Our findings lay the groundwork for future research in this field, highlighting the potential of a broader range of models, advanced explainable AI techniques, and innovative feature selection methods in the ongoing pursuit of robust and interpretable fraud detection systems.
AB - This research investigates credit card fraud detection through the lens of machine learning and explainable AI techniques. We employed four distinct models: K-Nearest Neighbors (KNN), Random Forests, Extreme Gradient Boosting (XGBoost), and Logistic Regression. The SHapley Additive exPlanations (SHAP) method was used to enhance the interpretability of our models. Model performance was assessed using key metrics such as accuracy, precision, recall, and F1 score before and after feature selection. Notably, despite a decrease in some performance metrics post feature selection, high precision scores were maintained, underscoring the robustness of our models. Our findings lay the groundwork for future research in this field, highlighting the potential of a broader range of models, advanced explainable AI techniques, and innovative feature selection methods in the ongoing pursuit of robust and interpretable fraud detection systems.
KW - Credit Card Fraud Detection
KW - Imbalanced Classification
KW - Machine Learning
KW - SHAP
KW - Under-sampling
UR - http://www.scopus.com/inward/record.url?scp=85185838251&partnerID=8YFLogxK
U2 - 10.1109/InCIT60207.2023.10413100
DO - 10.1109/InCIT60207.2023.10413100
M3 - 會議論文篇章
AN - SCOPUS:85185838251
T3 - 7th International Conference on Information Technology, InCIT 2023
SP - 86
EP - 91
BT - 7th International Conference on Information Technology, InCIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Information Technology, InCIT 2023
Y2 - 15 November 2023 through 17 November 2023
ER -