{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T18:38:01Z","timestamp":1781721481301,"version":"3.54.5"},"reference-count":53,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T00:00:00Z","timestamp":1716422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Recognizing fraudulent activity in the banking system is essential due to the significant risks involved. When fraudulent transactions are vastly outnumbered by non-fraudulent ones, dealing with imbalanced datasets can be difficult. This study aims to determine the best model for detecting fraud by comparing four commonly used machine learning algorithms: Support Vector Machine (SVM), XGBoost, Decision Tree, and Logistic Regression. Additionally, we utilized the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of class imbalance. The XGBoost Classifier proved to be the most successful model for fraud detection, with an accuracy of 99.88%. We utilized SHAP and LIME analyses to provide greater clarity into the decision-making process of the XGBoost model and improve overall comprehension. This research shows that the XGBoost Classifier is highly effective in detecting banking fraud on imbalanced datasets, with an impressive accuracy score. The interpretability of the XGBoost Classifier model was further enhanced by applying SHAP and LIME analysis, which shed light on the significant features that contribute to fraud detection. The insights and findings presented here are valuable contributions to the ongoing efforts aimed at developing effective fraud detection systems for the banking industry.<\/jats:p>","DOI":"10.3390\/info15060298","type":"journal-article","created":{"date-parts":[[2024,5,23]],"date-time":"2024-05-23T03:49:03Z","timestamp":1716436143000},"page":"298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0858-0232","authenticated-orcid":false,"given":"S. M. Nuruzzaman","family":"Nobel","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shirin","family":"Sultana","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8966-4578","authenticated-orcid":false,"given":"Sondip Poul","family":"Singha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7286-6722","authenticated-orcid":false,"given":"Sudipto","family":"Chaki","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1763-2544","authenticated-orcid":false,"given":"Md. Julkar Nayeen","family":"Mahi","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Daffodil International University, Dhaka 1207, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3114-8978","authenticated-orcid":false,"given":"Tony","family":"Jan","sequence":"additional","affiliation":[{"name":"Design and Creative Technologies, Torrens University, Brisbane, QLD 4006, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alistair","family":"Barros","sequence":"additional","affiliation":[{"name":"School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2822-0657","authenticated-orcid":false,"given":"Md","family":"Whaiduzzaman","sequence":"additional","affiliation":[{"name":"Design and Creative Technologies, Torrens University, Brisbane, QLD 4006, Australia"},{"name":"School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Awoyemi, J.O., Adetunmbi, A.O., and Oluwadare, S.A. (2017, January 29\u201331). Credit card fraud detection using machine learning techniques: A comparative analysis. Proceedings of the 2017 International Conference on Computing Networking and Informatics (ICCNI), Lagos, Nigeria.","DOI":"10.1109\/ICCNI.2017.8123782"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mytnyk, B., Tkachyk, O., Shakhovska, N., Fedushko, S., and Syerov, Y. (2023). Application of Artificial Intelligence for Fraudulent Banking Operations Recognition. Big Data Cogn. Comput., 7.","DOI":"10.3390\/bdcc7020093"},{"key":"ref_3","first-page":"23","article-title":"Credit card fraud detection using machine learning as data mining technique","volume":"10","author":"Yee","year":"2018","journal-title":"J. Telecommun. Electron. Comput. Eng. (JTEC)"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Raval, J., Bhattacharya, P., Jadav, N.K., Tanwar, S., Sharma, G., Bokoro, P.N., Elmorsy, M., Tolba, A., and Raboaca, M.S. (2023). RaKShA: A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions. Mathematics, 11.","DOI":"10.3390\/math11081901"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ir\u00e9n\u00e9e, M., Wang, Y., Hei, X., Song, X., Turiho, J.C., and Nyesheja, E.M. (2023). XTS: A Hybrid Framework to Detect DNS-Over-HTTPS Tunnels Based on XGBoost and Cooperative Game Theory. Mathematics, 11.","DOI":"10.3390\/math11102372"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108545","DOI":"10.1109\/ACCESS.2022.3213818","article-title":"BMNet-5: A novel approach of neural network to classify the genre of Bengali music based on audio features","volume":"10","author":"Hasib","year":"2022","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hasib, K.M., Iqbal, M., Shah, F.M., Mahmud, J.A., Popel, M.H., Showrov, M., Hossain, I., Ahmed, S., and Rahman, O. (2020). A survey of methods for managing the classification and solution of data imbalance problem. arXiv.","DOI":"10.3844\/jcssp.2020.1546.1557"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Maitra, S., Hossain, T., Hasib, K.M., and Shishir, F.S. (2020, January 4\u20137). Graph theory for dimensionality reduction: A case study to prognosticate parkinson\u2019s. Proceedings of the 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON51383.2020.9284926"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jahan, S., Islam, M.R., Hasib, K.M., Naseem, U., and Islam, M.S. (2021, January 18\u201322). Active Learning with an Adaptive Classifier for Inaccessible Big Data Analysis. Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China.","DOI":"10.1109\/IJCNN52387.2021.9534046"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Varmedja, D., Karanovic, M., Sladojevic, S., Arsenovic, M., and Anderla, A. (2019, January 20\u201322). Credit card fraud detection-machine learning methods. Proceedings of the 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH), Novi Sad, Serbia.","DOI":"10.1109\/INFOTEH.2019.8717766"},{"key":"ref_11","unstructured":"Pech, R. (2019). Fraud Detection in Mobile Money Transfer as Binary Classification Problem, Eagle Technilogies Inc Publ."},{"key":"ref_12","first-page":"532909","article-title":"Fraud detection using machine learning","volume":"528812","author":"Oza","year":"2018","journal-title":"Transfer"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kurshan, E., Shen, H., and Yu, H. (2020, January 21\u201323). Financial crime & fraud detection using graph computing: Application considerations & outlook. Proceedings of the 2020 Second International Conference on Transdisciplinary AI (TransAI), Irvine, CA, USA.","DOI":"10.1109\/TransAI49837.2020.00029"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pambudi, B.N., Hidayah, I., and Fauziati, S. (2019, January 5\u20136). Improving money laundering detection using optimized support vector machine. Proceedings of the 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia.","DOI":"10.1109\/ISRITI48646.2019.9034655"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s10614-018-9864-z","article-title":"Machine learning and sampling scheme: An empirical study of money laundering detection","volume":"54","author":"Zhang","year":"2019","journal-title":"Comput. Econ."},{"key":"ref_16","first-page":"14","article-title":"Applying supervised machine learning algorithms for fraud detection in anti-money laundering","volume":"1","author":"Raiter","year":"2021","journal-title":"J. Mod. Issues Bus. Res."},{"key":"ref_17","unstructured":"Lopez-Rojas, E.A., and Barneaud, C. (2019). Intelligent Computing: Proceedings of the 2019 Computing Conference, Volume 2, Springer."},{"key":"ref_18","unstructured":"Besenbruch, J. (2024, April 30). Fraud Detection Using Machine Learning Techniques. Research Paper Business Analytics. Available online: https:\/\/vu-business-analytics.github.io\/internship-office\/papers\/paper-besenbruch.pdf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4924","DOI":"10.1109\/TIFS.2021.3117075","article-title":"Adversarial XAI methods in cybersecurity","volume":"16","author":"Kuppa","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1016\/j.dss.2010.08.006","article-title":"The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature","volume":"50","author":"Ngai","year":"2011","journal-title":"Decis. Support Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Saia, R., and Carta, S. (2017). Evaluating Credit Card Transactions in the Frequency Domain for a Proactive Fraud Detection Approach, SECRYPT.","DOI":"10.5220\/0006425803350342"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.ins.2019.05.042","article-title":"Combining unsupervised and supervised learning in credit card fraud detection","volume":"557","author":"Carcillo","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, Z., and Bai, T. (2022). Financial Fraud Detection and Prediction in Listed Companies Using SMOTE and Machine Learning Algorithms. Entropy, 24.","DOI":"10.3390\/e24081157"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/TNSM.2023.3246794","article-title":"Improving performance, reliability, and feasibility in multimodal multitask traffic classification with XAI","volume":"20","author":"Nascita","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Khatri, S., Arora, A., and Agrawal, A.P. (2020, January 29\u201331). Supervised machine learning algorithms for credit card fraud detection: A comparison. Proceedings of the 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.","DOI":"10.1109\/Confluence47617.2020.9057851"},{"key":"ref_26","first-page":"1","article-title":"Machine Learning methods for Discovering Credit Card Fraud","volume":"III","author":"Hema","year":"2020","journal-title":"IRJCS Int. Res. J. Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kumar, M.S., Soundarya, V., Kavitha, S., Keerthika, E., and Aswini, E. (2019, January 21\u201322). Credit card fraud detection using random forest algorithm. Proceedings of the 2019 3rd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India.","DOI":"10.1109\/ICCCT2.2019.8824930"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/s10479-021-04149-2","article-title":"An intelligent payment card fraud detection system","volume":"334","author":"Seera","year":"2021","journal-title":"Ann. Oper. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Puh, M., and Brki\u0107, L. (2019, January 20\u201324). Detecting credit card fraud using selected machine learning algorithms. Proceedings of the 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2019.8757212"},{"key":"ref_30","unstructured":"Lopez-Rojas, E., Elmir, A., and Axelsson, S. (2016, January 26\u201328). PaySim: A financial mobile money simulator for fraud detection. Proceedings of the 28th European Modeling and Simulation Symposium, EMSS, Larnaca, Cyprus."},{"key":"ref_31","unstructured":"(2023, July 01). Sklearn.Preprocessing.LabelEncoder\u2014scikit-learn.org. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.LabelEncoder.html."},{"key":"ref_32","unstructured":"(2023, July 01). Sklearn.Preprocessing.MinMaxScaler\u2014scikit-learn.org. Available online: https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.MinMaxScaler.html."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s44230-022-00013-z","article-title":"Convolutional Auto-Encoder and Independent Component Analysis Based Automatic Place Recognition for Moving Robot in Invariant Season Condition","volume":"3","author":"Islam","year":"2022","journal-title":"Hum. Centric Intell. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hasnat, F., Hasan, M.M., Nasib, A.U., Adnan, A., Khanom, N., Islam, S.M., Mehedi, M.H.K., Iqbal, S., and Rasel, A.A. (2022, January 6\u201318). Understanding Sarcasm from Reddit texts using Supervised Algorithms. Proceedings of the 2022 IEEE 10th Region 10 Humanitarian Technology Conference (R10-HTC), Hyderabad, India.","DOI":"10.1109\/R10-HTC54060.2022.9929882"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"312","DOI":"10.17706\/jsw.14.7.312-339","article-title":"Development of an Intelligent Job Recommender System for Freelancers using Client\u2019s Feedback Classification and Association Rule Mining Techniques","volume":"14","author":"Hossain","year":"2019","journal-title":"J. Softw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1108\/JMLC-07-2019-0055","article-title":"Detecting money laundering transactions with machine learning","volume":"23","author":"Jullum","year":"2020","journal-title":"J. Money Laund. Control"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). Xgboost: A scalable tree boosting system. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s10509-019-3602-4","article-title":"A hybrid ensemble method for pulsar candidate classification","volume":"364","author":"Wang","year":"2019","journal-title":"Astrophys. Space Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cody, C., Ford, V., and Siraj, A. (2015, January 9\u201311). Decision tree learning for fraud detection in consumer energy consumption. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.80"},{"key":"ref_40","unstructured":"Javed Mehedi Shamrat, F., Ranjan, R., Hasib, K.M., Yadav, A., and Siddique, A.H. (2022). Pervasive Computing and Social Networking: Proceedings of ICPCSN 2021, Springer."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nobel, S.N., Sultana, S., Tasir, M.A.M., and Rahman, M.S. (2023, January 13\u201315). Next Word Prediction in Bangla Using Hybrid Approach. Proceedings of the 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ICCIT60459.2023.10441580"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_43","unstructured":"Quinlan, J.R. (1993). C4. 5: Programs for Machine Learning, Morgan Kaufmann Publishers, Inc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_45","first-page":"10084","article-title":"Data mining techniques for anti money laundering","volume":"12","author":"Salehi","year":"2017","journal-title":"Int. J. Appl. Eng. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"20713","DOI":"10.3390\/s141120713","article-title":"An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"110487","DOI":"10.1016\/j.asoc.2023.110487","article-title":"A floating offshore platform motion forecasting approach based on EEMD hybrid ConvLSTM and chaotic quantum ALO","volume":"144","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hossen, R., Whaiduzzaman, M., Uddin, M.N., Islam, M.J., Faruqui, N., Barros, A., Sookhak, M., and Mahi, M.J.N. (2021). Bdps: An efficient spark-based big data processing scheme for cloud fog-iot orchestration. Information, 12.","DOI":"10.3390\/info12120517"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Whaiduzzaman, M., Sakib, A., Khan, N.J., Chaki, S., Shahrier, L., Ghosh, S., Rahman, M.S., Mahi, M.J.N., Barros, A., and Fidge, C. (2023). Concept to Reality: An Integrated Approach to Testing Software User Interfaces. Appl. Sci., 13.","DOI":"10.3390\/app132111997"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Achar, S., Faruqui, N., Whaiduzzaman, M., Awajan, A., and Alazab, M. (2023). Cyber-physical system security based on human activity recognition through IoT cloud computing. Electronics, 12.","DOI":"10.3390\/electronics12081892"},{"key":"ref_51","first-page":"e01059","article-title":"A novel approach to explain the black-box nature of machine learning in compressive strength predictions of concrete using Shapley additive explanations (SHAP)","volume":"16","author":"Ekanayake","year":"2022","journal-title":"Case Stud. Constr. Mater."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ahmed, S., Nobel, S.N., and Ullah, O. (2023, January 23\u201325). An effective deep CNN model for multiclass brain tumor detection using MRI images and SHAP explainability. Proceedings of the 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), Chittagong, Bangladesh.","DOI":"10.1109\/ECCE57851.2023.10101503"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Khedkar, S., Subramanian, V., Shinde, G., and Gandhi, P. (2019, January 8\u20139). Explainable AI in healthcare. Proceedings of the 2nd International Conference on Advances in Science & Technology (ICAST), Mumbai, India.","DOI":"10.2139\/ssrn.3367686"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/6\/298\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:46:56Z","timestamp":1760107616000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/6\/298"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,23]]},"references-count":53,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["info15060298"],"URL":"https:\/\/doi.org\/10.3390\/info15060298","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,23]]}}}