Exploring Machine and Deep Learning Approaches in Credit Card Fraud Prevention and Detection

Abstract

Establishing the foundation for the use of deep learning and artificial intelligence in the detection of credit card fraud is the primary objective of this study. A more accurate and improved fraud detection system is necessary to meet the primary goal of enhancing financial security. In order to facilitate this study, we collect a sizable dataset of credit card payment records that encompasses all relevant variables. Data pretreatment enhances analysis by normalising numerical values, eliminating superfluous characteristics, and verifying data consistency. Exploratory data analysis, or EDA, must be used to find connections and trends in the dataset before choosing a model. Random Forest, gradient boost, SVM, Logistic Regression, LSTM, and GRU are among the machine learning models that we combine. With a 90% F1 score, 90% recall, 90% specificity, and 90.3% accuracy, the Random Forest model outperforms all others. The deep learning model GRU outperforms LSTM by a small margin with an accuracy rate of 90.04% and strong recall and precision measures. Furthermore, compared to earlier models, the Artificial Neural Network\'s accuracy is 89%. The findings demonstrate the effectiveness of the suggested methods in detecting fraudulent transactions. The study\'s findings open the door for fascinating new consumer protection research and provide insight into the issue of credit card fraud.

Introduction

Credit card usage has surged in e-commerce, increasing fraud risks. Traditional fraud detection methods, which rely heavily on manual rules, are no longer sufficient due to the rising complexity and volume of fraudulent transactions. Machine Learning (ML) offers an effective alternative by analyzing vast datasets to identify anomalies and adapt to evolving fraud tactics in real time.

Key ML Techniques:

• Supervised learning (e.g., logistic regression, decision trees, SVM) uses labeled data to classify transactions.

• Unsupervised learning (e.g., clustering) detects emerging fraud patterns without labeled data.

• Ensemble learning combines multiple models to boost accuracy.

Advanced ML models like deep learning and neural networks are increasingly used to identify complex fraud patterns. Continuous retraining with new data ensures systems remain effective against tactics like account takeover and synthetic identity fraud.

Literature Review Highlights:

• Azim (2024): Soft voting with sampling techniques improves detection accuracy on imbalanced datasets.

• Y?lmaz (2024): Combines feature selection and classification for high fraud detection performance.

• Zhu (2024): Uses neural networks with SMOTE to handle skewed data effectively.

• Islam (2024): Introduces a rule-based model outperforming many ML techniques.

• Sani (2024): Python-based model using logistic regression achieves 99.87% accuracy.

Other studies (Aghware, Tank, Sruthi, Planinic, Noviandy) further explore Random Forest, LightGBM, CatBoost, and XGBoost models, emphasizing ensemble techniques and real-time detection.

Methodology:

• Data Collection & Preprocessing: Kaggle dataset with ~1.3M entries; irrelevant features dropped; PCA used to reduce dimensionality.

• Exploratory Data Analysis (EDA): Visualizations show fraud trends by gender, transaction category, and city population.

• Model Implementation: Deep learning (LSTM, GRU) and ML (Random Forest, Gradient Boosting) models are used to build a robust fraud detection system.

Conclusion

This study demonstrates the systematic application of state-of-the-art machine learning and deep learning algorithms for credit card fraud detection. With meticulous data collection, a comprehensive dataset containing all relevant transaction details was produced. Strict preprocessing procedures were employed to provide appropriate analytical circumstances and improve data integrity by eliminating superfluous features and standardising numerical values. The selection of the model was informed by the significant patterns and relationships discovered via the use of exploratory data analysis (EDA). Support Vector Machines, Random Forest, Logistic Regression, GRU, Gradient Boosting, and Long Short-Term Memory are some of the models that have demonstrated promise in detecting fraud. The most remarkable of all performance indicators, the Random Forest model\'s accuracy rate of 90.3%, was attained by aligning the recall, F1, and precision scores. GRU distinguished itself from the other deep learning models with an accuracy rate of 90.04% and above-average recall and precision. While LSTM demonstrated competence, it was not optimal. Additionally, its 89% accuracy was comparable to other well-known models, such as the ANN. By highlighting the use of realistic strategies to improve financial stability, this thorough investigation lays the foundation for future studies on credit card fraud detection. Systems for detecting fraud can be greatly enhanced by developments in machine and deep learning.

References

[1] S. Sruthi, S. Emadaboina, and C. Jyotsna, “Enhancing Credit Card Fraud Detection with Light Gradient-Boosting Machine: An Advanced Machine Learning Approach,” 2024 Int. Conf. Knowl. Eng. Commun. Syst. ICKECS 2024, 2024, doi: 10.1109/ICKECS61492.2024.10616809. [2] G. Airlangga, “Evaluating the Efficacy of Machine Learning Models in Credit Card Fraud Detection Journal of Computer Networks , Architecture and High Performance Computing,” J. Comput. Networks, Archit. High Perform. Comput., vol. 6, no. 2, pp. 829–837, 2024. [3] X. Feng and S. K. Kim, “Novel Machine Learning Based Credit Card Fraud Detection Systems,” Mathematics, vol. 12, no. 12, 2024, doi: 10.3390/math12121869. [4] D. Hove, O. Olugbara, and A. Singh, “Bibliometric Analysis of Recent Trends in Machine Learning for Online Credit Card Fraud Detection,” J. Scientometr. Res., vol. 13, no. 1, pp. 43–57, 2024, doi: 10.5530/jscires.13.1.4. [5] C. G. Tekkali and K. Natarajan, “Assessing CNN’s Performance with Multiple Optimization Functions for Credit Card Fraud Detection,” Procedia Comput. Sci., vol. 235, pp. 2035–2042, 2024, doi: 10.1016/j.procs.2024.04.193. [6] M. H. Chagahi, N. Delfan, S. M. Dashtaki, B. Moshiri, and M. J. Piran, “An Innovative Attention-based Ensemble System for Credit Card Fraud Detection,” pp. 1–9, 2024. [7] M. Kanchana, V. Chadda, and H. Jain, “Credit card fraud detection,” Int. J. Adv. Sci. Technol., vol. 29, no. 6, pp. 2201–2215, 2020, doi: 10.55041/ijsrem35776. [8] M. Kong et al., “CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentation,” Neural Comput. Appl., vol. 36, no. 15, pp. 8607–8623, 2024, doi: 10.1007/s00521-024-09546-9. [9] V. Chang, B. Ali, L. Golightly, M. A. Ganatra, and M. Mohamed, “Investigating Credit Card Payment Fraud with Detection Methods Using Advanced Machine Learning,” Inf., vol. 15, no. 8, pp. 1–20, 2024, doi: 10.3390/info15080478. [10] I. D. Mienye, “A Hybrid Deep Learning Approach with Generative Adversarial Network for Credit Card Fraud Detection,” 2024. [11] S. S. Sulaiman, I. Nadher, and S. M. Hameed, “Credit Card Fraud Detection Using Improved Deep Learning Models,” Comput. Mater. Contin., vol. 78, no. 1, pp. 1049–1069, 2024, doi: 10.32604/cmc.2023.046051. [12] A. R. Jaiswal and A. Krishna, “Credit Shield Solutions?: Credit Card Fraud Detection System Using Machine Learning Approach,” vol. 10, no. 5, pp. 2111–2115, 2024. [13] K. Maithili et al., “Development of an efficient machine learning algorithm for reliable credit card fraud identification and protection systems,” MATEC Web Conf., vol. 392, p. 01116, 2024, doi: 10.1051/matecconf/202439201116. [14] D. P. Prabha and C. V. Priscilla, “A combined framework based on LSTM autoencoder and XGBoost with adaptive threshold classification for credit card fraud detection,” Sci. Temper, vol. 15, no. 02, pp. 2216–2224, 2024, doi: 10.58414/scientifictemper.2024.15.2.34. [15] O. G. Abdulateef, “Fraud Guard: A Comprehensive Comparative Analysis of Machine Learning Approaches to Enhance Credit Card Fraud Detection,” J. Inf. Eng. Appl., vol. 14, no. 2, pp. 14–22, 2024, doi: 10.7176/jiea/14-2-02. [16] S. Jhansi Ida, K. Balasubadra, R. R. Skandarsini, and T. Lakshmi Narayanaa, “Enhancing Credit Card Fraud Detection through LSTM-Based Sequential Analysis with Early Stopping,” Proc. 2nd IEEE Int. Conf. Netw. Commun. 2024, ICNWC 2024, no. June, 2024, doi: 10.1109/ICNWC60771.2024.10537550. [17] M. Azim Mim, N. Majadi, and P. Mazumder, “A soft voting ensemble learning approach for credit card fraud detection,” Heliyon, vol. 10, no. 3, p. e25466, 2024, doi: 10.1016/j.heliyon.2024.e25466. [18] A. A. Y?lmaz, “A machine learning-based framework using the particle swarm optimization algorithm for credit card fraud detection,” Commun. Fac. Sci. Univ. Ankara Ser. A2-A3 Phys. Sci. Eng., vol. 66, no. 1, pp. 82–94, 2024, doi: 10.33769/aupse.1361266. [19] M. Zhu, Y. Zhang, Y. Gong, C. Xu, and Y. Xiang, “Enhancing Credit Card Fraud Detection: A Neural Network and SMOTE Integrated Approach,” J. Theory Pract. Eng. Sci., vol. 4, no. 02, pp. 23–30, 2024, doi: 10.53469/jtpes.2024.04(02).04. [20] S. Islam, M. M. Haque, and A. N. M. R. Karim, “A rule-based machine learning model for financial fraud detection,” Int. J. Electr. Comput. Eng., vol. 14, no. 1, pp. 759–771, 2024, doi: 10.11591/ijece.v14i1.pp759-771. [21] A. Sani, Z. L. Hassan, and A. T. Balarabe, “A Logistic Regression-based Model for Identifying Credit Card Fraudulent Transactions,” Asian J. Res. Comput. Sci., vol. 17, no. 7, pp. 41–54, 2024, doi: 10.9734/ajrcos/2024/v17i7476. [22] F. O. Aghware et al., “Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection,” J. Comput. Theor. Appl., vol. 1, no. 4, pp. 407–420, 2024, doi: 10.62411/jcta.10323. [23] E. Tank and M. Das, “On Credit Card Fraud Detection Using Machine Learning Techniques,” Lect. Notes Networks Syst., vol. 966 LNNS, no. September, pp. 293–303, 2024, doi: 10.1007/978-981-97-2004-0_21. [24] D. Planinic and V. Popovic-Bugarin, “Credit Card Fraud Detection Using Supervised Learning Algorithms,” 2024 28th Int. Conf. Inf. Technol. IT 2024, vol. 9, no. 10, pp. 2–5, 2024, doi: 10.1109/IT61232.2024.10475768. [25] T. R. Noviandy, G. M. Idroes, A. Maulana, I. Hardi, E. S. Ringga, and R. Idroes, “Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques,” Indatu J. Manag. Account., vol. 1, no. 1, pp. 29–35, 2023, doi: 10.60084/ijma.v1i1.78. [26] MD RASHED MOHAIMIN, Md Sumsuzoha, Md Amran Hossen Pabel, and Farhan Nasrullah, “Detecting Financial Fraud Using Anomaly Detection Techniques: A Comparative Study of Machine Learning Algorithms,” J. Comput. Sci. Technol. Stud., vol. 6, no. 3, pp. 01–14, 2024, doi: 10.32996/jcsts.2024.6.3.1. [27] M. A. Gill, M. Quresh, A. Rasool, and M. M. Hassan, “Detection of Credit Card Fraud Through Machine Learning In Banking Industry,” vol. 05, no. 01, pp. 1–10, 2023.

Copyright

Copyright © 2025 Shayna Bano, Dr. Pharindara Kumar Sharma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.