Credit Card Fraud Detection: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Ibomoiye Domor Mienye
,
Yanxia Sun

With the rapid developments in electronic commerce and digital payment technologies, credit card transactions have increased significantly. Machine learning (ML) has been vital in analyzing customer data to detect and prevent fraud.

  • credit card
  • feature selection
  • fraud detection

1. Introduction

Over the years, electronic payments (e-payments) have been the most common payment option due to technological advancements and the development of several electronic funding methods [1]. E-payment systems are essential to the present competitive financial sector and are mostly performed using credit cards [2]. The introduction of credit cards has resulted in convenient and seamless e-payments. A recent study stated that in the second quarter of 2021, Mastercard and Visa issued 1131 million and 1156 million cards, respectively [3]. However, the rise of credit card usage globally has increased the fraud rate, affecting consumers and merchants [4]. For instance, a report stated that financial losses due to credit and debit cards are among the leading causes of losses in the financial sector [3]. Therefore, developing efficient credit card fraud-detection systems is necessary to reduce such losses.
Machine learning algorithms have been widely employed to detect credit card fraud [5][6][7]. Meanwhile, there have been enormous datasets with very high dimensions due to the advent of big data and the Internet of Things (IoT) [8][9]. Furthermore, some features in these datasets might be redundant or less significant to the response variable. Using such features for machine learning could increase the complexity of the model and lead to overfitting [10]. Therefore, to handle the high dimensionality issue, an approach containing dimensionality reduction, such as feature selection, is necessary to obtain valuable insights and make accurate predictions [11].
Feature-selection techniques aim to identify the most important attributes needed to develop a well-performing machine learning model [12][13], ensuring improved classification performance and reduced computational complexity by removing irrelevant and redundant features. Feature selection techniques are usually grouped into three methodological groups: filters, wrappers, and embedded methods [10][14]. The internal workings and configuration of the various feature-selection methods make them suitable for different applications. Filter methods employ attribute ranking to determine the most informative features. Features that attain scores above a given threshold are selected, and those below the threshold are discarded. After identifying the most important features, they can be fed as input to the learning algorithm. Filter methods vary from wrapper and embedded methods as they are not dependent on a classifier and are, therefore, independent of the classifier’s bias [15].
However, wrapper methods use an ML classifier’s performance as the evaluation metric in selecting the most relevant feature set. Wrapper methods usually lead to better classification performance than filter techniques because the feature-selection procedure is optimized for the chosen classification algorithm [16][17]. Generally, wrapper methods employ a search strategy to identify the candidate subsets. The classifier’s performance on the various feature subsets is measured, and the subset that leads to the highest performance is selected as the most informative subset. Examples of wrapper-based feature selection techniques include the Boruta algorithm, forward selection, backward elimination, and the genetic algorithm. Embedded methods select the features that enhance the model’s performance during training. The feature selection is incorporated into the learning procedure [13]. Unlike wrapper methods, this type of feature selection aims to reduce the time used in training different subsets. Embedded methods include random forest, decision tree, gradient boosting, elastic net, and LASSO [10].
Meanwhile, the GA wrapper is an effective method for feature selection, with applications in diverse domains, including natural language processing (NLP) [18], fraud detection [19], sentiment analysis [20], and medical diagnosis [21]. This study proposes a hybrid feature-selection approach, combining the IG-based filter and GA-based wrapper techniques. The main contributions and objectives of the work include the following:
  • Using the information gain technique for initial feature selection to rank the features in the credit card dataset, only the top-ranked features are fed into the GA wrapper to reduce the search space and enhance the classification performance.
  • Secondly, the GA wrapper is employed to select the best feature subset that results in optimal classification performance, and the ELM is employed as the learning algorithm in the GA wrapper.
  • Additionally, this study employs the G-mean as the fitness function in the GA wrapper instead of the conventional accuracy evaluation criterion, ensuring the recognition rate of the minority samples is considered and improved.
The rationale behind this approach is that the initial IG-based feature selection and ELM’s ability to produce promising performance while converging faster than traditional neural networks could reduce the computational complexity of the GA and improve the classification performance. The ELM is chosen as the learning algorithm in the GA wrapper because it converges far more rapidly and achieves higher generalization performance than conventional neural networks. At the same time, its learning process is thousands of times quicker than neural networks trained via backpropagation [22]. Furthermore, for convenience, the proposed hybrid approach is called IG-GAW. It would be compared with the conventional ELM classifier, an ELM classifier with IG-based feature selection (IG-ELM), the GA wrapper (GAW), and well-performing methods in related literature.

2. Credit Card Fraud Detection

Recently, ML algorithms have been widely applied for credit card fraud detection [23][24][25]. Researchers have used both traditional ML and deep learning (DL) algorithms to predict credit card fraud efficiently. For example, Alarfaj et al. [26] conducted a study using ML and DL techniques for detecting credit card fraud, while Van Belle et al. [27] employed inductive graph representation learning, Esenogho et al. [28] used a neural network ensemble, and Zhang et al. [29] employed an ensemble classifier based on isolation forest and adaptive boosting.
Some problems encountered when dealing with credit card datasets include high dimensionality and imbalance class [30][31], making it difficult for ML classifiers to learn and make accurate predictions. In addition, high dimensional data often make the learning process complex and computationally expensive, resulting in models with poor generalization ability [32]. Therefore, feature selection is essential in such datasets to reduce the computational burden and enhance the model’s generalization ability. For example, Chaquet-Ulldemolins et al. [33] recorded an increase in the classification performance of ML classifiers after introducing feature selection. Generally, feature-selection methods are useful in applications where the number of features affects the classifier’s performance.
The wrapper feature-selection methods have been widely applied in numerous applications [34][35]. They compute the importance of each feature based on its usefulness when training the ML model. The primary components of a wrapper method are the learning classifier and search strategy. The wrapper technique exists as a wrapper around the learning classifier and uses the same classifier to select the most relevant features. Therefore, a robust learning classifier could enhance the wrapper-based feature selection. Furthermore, the search strategy employed in the wrapper could affect the feature selection, and using the right search strategy for a given application is crucial in obtaining good performance.
Evolutionary search techniques such as genetic algorithms can avoid becoming stuck in local optima. Unlike deterministic algorithms, they can identify reduced feature sets that can effectively represent the original feature set [36]. The GA-based wrapper can easily identify feature redundancy and correlations. In addition, selecting a suitable classifier is vital in developing robust GA wrapper models since the wrapper procedure is tied to the selected classifier’s performance. However, there are specific issues to consider when selecting the classifier. Firstly, the classifier should be able to achieve good classification performance and have excellent generalization ability. Secondly, since the classifier would be used to train numerous subsets, it should have good training speed. Thirdly, the number of features in the various subsets might differ. Therefore, using the same model parameters might not be enough to obtain good performance in all the subsets [37]. Hence, it would be preferred to use a classifier that automatically updates the model parameters for every feature subset to achieve good performance.
Other recent methods for credit card fraud detection include a signal processing framework [38], signal processing on graphs [39], and a deep learning ensemble [40]. In addition, in the literature, several learning algorithms (such as decision tree [41], naïve Bayes [42], SVM [43], and random forest [44]) have been used as the classifier in the GA wrapper. However, these classifiers are not able to consider the issues mentioned above. Therefore, a hybrid wrapper approach that considers all the above-mentioned issues is proposed. The proposed approach employs the IG-based filter feature selection to rank the attributes, and only the top-ranked features would be used as input into the GA wrapper. Meanwhile, the GA wrapper employs the ELM as the learning classifier. The ELM can achieve excellent classification performance and generalization ability with an extremely fast learning speed compared to conventional training methods. Furthermore, unlike traditional neural networks based on backpropagation algorithms, the ELM’s training process is entirely automatic and does not require it to be tuned iteratively.

This entry is adapted from the peer-reviewed paper 10.3390/app13127254

References

  1. Femila Roseline, J.; Naidu, G.; Samuthira Pandi, V.; Alamelu alias Rajasree, S.; Mageswari, N. Autonomous credit card fraud detection using machine learning approach. Comput. Electr. Eng. 2022, 102, 108132.
  2. Alharbi, A.; Alshammari, M.; Okon, O.D.; Alabrah, A.; Rauf, H.T.; Alyami, H.; Meraj, T. A Novel text2IMG Mechanism of Credit Card Fraud Detection: A Deep Learning Approach. Electronics 2022, 11, 756.
  3. Bin Sulaiman, R.; Schetinin, V.; Sant, P. Review of Machine Learning Approach on Credit Card Fraud Detection. Hum.-Centric Intell. Syst. 2022, 2, 55–68.
  4. Wang, D.; Chen, B.; Chen, J. Credit card fraud detection strategies with consumer incentives. Omega 2019, 88, 179–195.
  5. Nandi, A.K.; Randhawa, K.K.; Chua, H.S.; Seera, M.; Lim, C.P. Credit card fraud detection using a hierarchical behavior-knowledge space model. PLoS ONE 2022, 17, e0260579.
  6. Ileberi, E.; Sun, Y.; Wang, Z. Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost. IEEE Access 2021, 9, 165286–165294.
  7. Rtayli, N.; Enneya, N. Enhanced credit card fraud detection based on SVM-recursive feature elimination and hyper-parameters optimization. J. Inf. Secur. Appl. 2020, 55, 102596.
  8. Oo, M.C.M.; Thein, T. An efficient predictive analytics system for high dimensional big data. J. King Saud Univ.-Comput. Inf. Sci. 2022, 34, 1521–1532.
  9. Huebner, J.; Fleisch, E.; Ilic, A. Assisting mental accounting using smartphones: Increasing the salience of credit card transactions helps consumer reduce their spending. Comput. Hum. Behav. 2020, 113, 106504.
  10. Pudjihartono, N.; Fadason, T.; Kempa-Liehr, A.W.; O’Sullivan, J.M. A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction. Front. Bioinform. 2022, 2, 927312.
  11. de-la-Bandera, I.; Palacios, D.; Mendoza, J.; Barco, R. Feature Extraction for Dimensionality Reduction in Cellular Networks Performance Analysis. Sensors 2020, 20, 6944.
  12. Bouaguel, W. A New Approach for Wrapper Feature Selection Using Genetic Algorithm for Big Data. In Intelligent and Evolutionary Systems; Springer: Cham, Switzerland, 2016; pp. 75–83.
  13. Chandrashekar, G.; Sahin, F. A survey on feature selection methods. Comput. Electr. Eng. 2014, 40, 16–28.
  14. Bashir, S.; Khattak, I.U.; Khan, A.; Khan, F.H.; Gani, A.; Shiraz, M. A Novel Feature Selection Method for Classification of Medical Data Using Filters, Wrappers, and Embedded Approaches. Complexity 2022, 2022, e8190814.
  15. Kumar, A.; Bhatia, M.P.S.; Sangwan, S.R. Rumour detection using deep learning and filter-wrapper feature selection in benchmark twitter dataset. Multimed. Tools Appl. 2022, 81, 34615–34632.
  16. Wang, F.; Lu, X.; Chang, X.; Cao, X.; Yan, S.; Li, K.; Duić, N.; Shafie-khah, M.; Catalão, J.P. Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data. Energy 2022, 238, 121728.
  17. Wang, Z.; Gao, S.; Zhou, M.; Sato, S.; Cheng, J.; Wang, J. Information-Theory-based Nondominated Sorting Ant Colony Optimization for Multiobjective Feature Selection in Classification. IEEE Trans. Cybern. 2022, 1–14.
  18. Rasool, A.; Tao, R.; Kamyab, M.; Hayat, S. GAWA–A Feature Selection Method for Hybrid Sentiment Classification. IEEE Access 2020, 8, 191850–191861.
  19. Ileberi, E.; Sun, Y.; Wang, Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. J. Big Data 2022, 9, 24.
  20. Al-Ahmad, B.; Al-Zoubi, A.M.; Abu Khurma, R.; Aljarah, I. An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information. Symmetry 2021, 13, 1091.
  21. Soumaya, Z.; Drissi Taoufiq, B.; Benayad, N.; Yunus, K.; Abdelkrim, A. The detection of Parkinson disease using the genetic algorithm and SVM classifier. Appl. Acoust. 2021, 171, 107528.
  22. Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K. Extreme learning machine: A new learning scheme of feedforward neural networks. In Proceedings of the 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), Budapest, Hungary, 25–29 July 2004; Volume 2, pp. 985–990.
  23. Han, S.; Zhu, K.; Zhou, M.; Cai, X. Competition-Driven Multimodal Multiobjective Optimization and Its Application to Feature Selection for Credit Card Fraud Detection. IEEE Trans. Syst. Man Cybern. Syst. 2022, 52, 7845–7857.
  24. Malik, E.F.; Khaw, K.W.; Belaton, B.; Wong, W.P.; Chew, X. Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture. Mathematics 2022, 10, 1480.
  25. Zioviris, G.; Kolomvatsos, K.; Stamoulis, G. Credit card fraud detection using a deep learning multistage model. J. Supercomput. 2022, 78, 14571–14596.
  26. Alarfaj, F.K.; Malik, I.; Khan, H.U.; Almusallam, N.; Ramzan, M.; Ahmed, M. Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms. IEEE Access 2022, 10, 39700–39715.
  27. Van Belle, R.; Van Damme, C.; Tytgat, H.; De Weerdt, J. Inductive Graph Representation Learning for fraud detection. Expert Syst. Appl. 2022, 193, 116463.
  28. Esenogho, E.; Mienye, I.D.; Swart, T.G.; Aruleba, K.; Obaido, G. A Neural Network Ensemble with Feature Engineering for Improved Credit Card Fraud Detection. IEEE Access 2022, 10, 16400–16407.
  29. Zhang, Y.-F.; Lu, H.-L.; Lin, H.-F.; Qiao, X.-C.; Zheng, H. The Optimized Anomaly Detection Models Based on an Approach of Dealing with Imbalanced Dataset for Credit Card Fraud Detection. Mob. Inf. Syst. 2022, 2022, e8027903.
  30. Ala’raj, M.; Abbod, M.F.; Majdalawieh, M.; Jum’a, L. A deep learning model for behavioural credit scoring in banks. Neural Comput. Appl. 2022, 34, 5839–5866.
  31. Zhang, X.; Yu, L.; Yin, H.; Lai, K.K. Integrating data augmentation and hybrid feature selection for small sample credit risk assessment with high dimensionality. Comput. Oper. Res. 2022, 146, 105937.
  32. Yang, Y.; Fan, C.; Chen, L.; Xiong, H. IPMOD: An efficient outlier detection model for high-dimensional medical data streams. Expert Syst. Appl. 2022, 191, 116212.
  33. Chaquet-Ulldemolins, J.; Gimeno-Blanes, F.-J.; Moral-Rubio, S.; Muñoz-Romero, S.; Rojo Álvarez, J.-L. On the Black-Box Challenge for Fraud Detection Using Machine Learning (I): Linear Models and Informative Feature Selection. Appl. Sci. 2022, 12, 3328.
  34. Al-Yaseen, W.L.; Idrees, A.K.; Almasoudy, F.H. Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system. Pattern Recognit. 2022, 132, 108912.
  35. Beheshti, Z. BMPA-TVSinV: A Binary Marine Predators Algorithm using time-varying sine and V-shaped transfer functions for wrapper-based feature selection. Knowl.-Based Syst. 2022, 252, 109446.
  36. Prashanth, S.K.; Shitharth, S.; Praveen Kumar, B.; Subedha, V.; Sangeetha, K. Optimal Feature Selection Based on Evolutionary Algorithm for Intrusion Detection. SN Comput. Sci. 2022, 3, 439.
  37. Xue, X.; Yao, M.; Wu, Z. A novel ensemble-based wrapper method for feature selection using extreme learning machine and genetic algorithm. Knowl. Inf. Syst. 2018, 57, 389–412.
  38. Salazar, A.; Safont, G.; Rodriguez, A.; Vergara, L. Combination of multiple detectors for credit card fraud detection. In Proceedings of the 2016 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Limassol, Cyprus, 12–14 December 2016; pp. 138–143.
  39. Vergara, L.; Salazar, A.; Belda, J.; Safont, G.; Moral, S.; Iglesias, S. Signal processing on graphs for improving automatic credit card fraud detection. In Proceedings of the 2017 International Carnahan Conference on Security Technology (ICCST), Madrid, Spain, 23–26 October 2017; pp. 1–6.
  40. Mienye, I.D.; Sun, Y. A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection. IEEE Access 2023, 11, 30628–30638.
  41. Gkikas, D.C.; Theodoridis, P.K.; Beligiannis, G.N. Enhanced Marketing Decision Making for Consumer Behaviour Classification Using Binary Decision Trees and a Genetic Algorithm Wrapper. Informatics 2022, 9, 45.
  42. Mabdeh, A.N.; Al-Fugara, A.; Ahmadlou, M.; Al-Adamat, R.; Al Shabeeb, A.R. GIS-based landslide susceptibility assessment and mapping in Ajloun and Jerash governorates in Jordan using genetic algorithm-based ensemble models. Acta Geophys. 2022, 70, 1253–1267.
  43. Tao, P.; Sun, Z.; Sun, Z. An Improved Intrusion Detection Algorithm Based on GA and SVM. IEEE Access 2018, 6, 13624–13631.
  44. Kasongo, S.M. An Advanced Intrusion Detection System for IIoT Based on GA and Tree Based Algorithms. IEEE Access 2021, 9, 113199–113212.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback