A Novel Hybrid Algorithms for Credit Card Fraud Detection in Banking- A Review

Abstract

Fraud detection in the financial sector, particularly in credit card transactions, is a critical issue that requires efficient and accurate models to safeguard against financial losses. Traditional methods of fraud detection are often limited by their ability to process complex and large datasets in real-time. Hybrid algorithms, which combine multiple techniques like machine learning, deep learning, and ensemble methods, have emerged as a solution to this challenge. This paper reviews the application of hybrid models in credit card fraud detection, highlighting their advantages, including improved accuracy, better generalization, and the ability to handle imbalanced data. It discusses deep learning architectures such as neural networks, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks, and how these models capture intricate patterns in fraud detection tasks. The paper also compares hybrid algorithms with traditional machine learning models, emphasizing the enhanced performance and operational efficiency of hybrid approaches. Furthermore, it explores the importance of data preprocessing, feature engineering, and performance metrics in evaluating fraud detection models. The findings indicate that hybrid algorithms have significant potential in improving fraud detection systems, making them a promising avenue for future research and application.

Introduction

Credit card fraud is a growing global problem driven by the rapid expansion of digital payments and online transactions. Fraud occurs through methods such as card-not-present fraud, card-present fraud, and identity theft, using techniques like phishing, skimming, hacking, and data breaches. The financial impact is severe, costing billions annually and significantly affecting banks, merchants, and consumers, while also damaging customer trust and institutional reputation.

Traditional rule-based fraud detection systems are increasingly ineffective due to their inability to adapt to evolving fraud patterns, leading to high false positives and false negatives. As a result, banks are shifting toward real-time fraud detection systems powered by machine learning (ML) and artificial intelligence (AI). These systems can learn complex patterns from large datasets, though challenges such as imbalanced data, overfitting, and lack of interpretability remain.

The literature shows a clear evolution from rule-based and statistical methods to machine learning, hybrid algorithms, and deep learning approaches. Hybrid models—such as ensembles, ML–deep learning combinations, and clustering-classification approaches—improve accuracy, robustness, and adaptability by leveraging the strengths of multiple techniques. Deep learning models, including neural networks, CNNs, and LSTMs, further enhance fraud detection by automatically learning intricate, non-linear, and sequential patterns from data.

Effective fraud detection also depends on high-quality datasets, thorough data preprocessing (normalization, feature selection, and class imbalance handling), and strong feature engineering based on transaction behavior, time, location, and user patterns. Model performance is evaluated using metrics such as precision, recall, F1-score, and AUC-ROC, which are more suitable than accuracy alone for imbalanced fraud data.

Overall, while AI-driven, hybrid, and deep learning-based fraud detection systems have significantly improved detection performance, ongoing challenges related to data imbalance, evolving fraud strategies, and model interpretability persist. Future research is expected to focus on adaptive, transparent, and advanced techniques such as reinforcement learning, federated learning, and multi-modal data integration to further strengthen fraud detection systems.

Conclusion

In conclusion, hybrid algorithms offer a promising approach to enhancing fraud detection in the financial sector. By combining multiple techniques such as machine learning, deep learning, and ensemble methods, these models can leverage the strengths of different algorithms, resulting in improved accuracy and better handling of complex, imbalanced datasets. Deep learning models, such as neural networks, CNNs, and LSTMs, have shown significant potential in capturing intricate patterns in large datasets, often outperforming traditional models. Additionally, the integration of machine learning and deep learning methods enables models to continuously adapt and improve, leading to better detection and prevention of fraudulent activities. However, challenges such as data preprocessing, feature engineering, and evaluating model performance persist, emphasizing the importance of continuous research and development in this field. Ultimately, the use of hybrid algorithms in fraud detection holds great promise for improving financial security, reducing fraudulent activities, and optimizing operational efficiency in real-time transaction monitoring.

References

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Copyright

Copyright © 2026 Shashank Poddar, Dr. Anshu Tiwari. 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.