The banking industry's metamorphosis through digitalization has unquestionably revolutionized accessibility and convenience for customers worldwide. However, this paradigm shift has ushered in a new era of challenges, most notably in the realm of cybersecurity. Conventional rule-based fraud detection strategies have struggled to keep pace with the rapid evolution of cyber threats, prompting a surge of interest in more adaptive approaches like unsupervised learning. Furthermore, the COVID-19 pandemic has exacerbated the issue of bank fraud due to the widespread transition to online platforms and the proliferation of charitable funds, which present ripe opportunities for exploitation by cybercriminals. In response to these pressing concerns, this study delves into the realm of machine learning algorithms for the analysis and identification of fraudulent banking transactions. Notably, it contributes scientific novelty by developing models specifically tailored to this purpose and implementing innovative preprocessing techniques to enhance detection accuracy. Utilizing a diverse array of algorithms, including Random Forest, K-Nearest Neighbor (KNN), Naïve Bayes, Decision Trees, and Logistic Regression, the study showcases promising results. In particular, logistic regression and decision tree models exhibit impressive accuracy and Area Under the Curve (AUC) values of approximately 0.98, 0.97 and 0.95, 0.94, respectively. Given the pervasive nature of banking fraud in our digital society, the utilization of artificial intelligence algorithms for fraud detection stands as a critical and timely endeavor, promising enhanced security and trust in the financial ecosystem.

Banking, Cybersecurity, Fraud detection, Machine learning, Digital transaction

REFERENCES

Ameme, B. and J. Wireko, Impact of technological innovations on customers in the banking industry in developing countries. The Business & Management Review, 2016. 7(3): p. 388.

Arslanian, H. and F. Fischer, The future of finance: The impact of FinTech, AI, and crypto on financial services. 2019: Springer.

Abdelnabi, S., R. Hasan, and M. Fritz. Open-domain, content-based, multi-modal fact-checking of out-of-context images via online resources. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

Ahammad, R., et al., Social-ecological and institutional factors affecting forest and landscape restoration in the Chittagong Hill Tracts of Bangladesh. Land Use Policy, 2023. 125: p. 106478.

Amon, M.J., et al. Influencing photo sharing decisions on social media: A case of paradoxical findings. in 2020 IEEE Symposium on Security and Privacy (SP). 2020. IEEE.

Patel, D., C.K. Sahu, and R. Rai, Security in modern manufacturing systems: integrating blockchain in artificial intelligence-assisted manufacturing. International Journal of Production Research, 2024. 62(3): p. 1041-1071.

Himeur, Y., et al., Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives. Applied Energy, 2021. 287: p. 116601.

Jabin, J.A., et al., High-temperature effect on the mechanical behavior of recycled fiber-reinforced concrete containing volcanic pumice powder: An experimental assessment combined with machine learning (ML)-based prediction. Construction and Building Materials, 2024. 418: p. 135362.

Md Abdullah Al Mahmud, et al., INFORMATION TECHNOLOGY FOR THE NEXT FUTURE WORLD: ADOPTION OF IT FOR SOCIAL AND ECONOMIC GROWTH: PART II. International Journal of Innovative Research in Technology, 2024. 10(12): p. 742-747.

Rakibul Hasan, et al., Information Technologies For The Next Future World: Implications, Impacts And Barriers: Part - I. International Journal of Creative Research Thoughts (IJCRT),, 2024. 12(5): p. a323-a330.

Shahana, A., et al., AI-Driven Cybersecurity: Balancing Advancements and Safeguards. Journal of Computer Science and Technology Studies, 2024. 6(2): p. 76-85.

Habibur Rahman Sobuz, M., et al., Assessment of mechanical properties with machine learning modeling and durability, and microstructural characteristics of a biochar-cement mortar composite. Construction and Building Materials, 2024. 411: p. 134281.

Akid, A.S.M., et al., Flexural behavior of corroded reinforced concrete beam strengthened with jute fiber reinforced polymer. Advances in Structural Engineering, 2021. 24(7): p. 1269-1282.

Jakhrani, A., et al., Assessment of dissolved salts concentration of seawater in the vicinity of Karachi. International Journal of Structural and Civil Engineering, 2012. 1(2): p. 61-69.

Rahman, M. and H.R. Sobuz. Comparative study of IPS & PPVC precast system—A case study of public housing buildings project in Singapore. in Proceedings of the 4th International Conference on Civil Engineering for Sustainable Development (ICCESD 2018), KUET, Khulna, Bangladesh. 2018.

Rahman Sobuz, M.H., et al., Performance evaluation of high-performance self-compacting concrete with waste glass aggregate and metakaolin. Journal of Building Engineering, 2023. 67: p. 105976.

Rana, M.J., M.R. Hasan, and M.H.R. Sobuz, An investigation on the impact of shading devices on energy consumption of commercial buildings in the contexts of subtropical climate. Smart and Sustainable Built Environment, 2022. 11(3): p. 661-691.

Sobuz, M.H.R., et al., Assessing the influence of sugarcane bagasse ash for the production of eco-friendly concrete: Experimental and machine learning approaches. Case Studies in Construction Materials, 2024. 20: p. e02839.

Sobuz, M.H.R., S.D. Datta, and A.S.M. Akid, Investigating the combined effect of aggregate size and sulphate attack on producing sustainable recycled aggregate concrete. Australian Journal of Civil Engineering, 2023. 21(2): p. 224-239.

Uddin, M.A., et al., The Effect of Curing Time on Compressive Strength of Composite Cement Concrete. Applied Mechanics and Materials, 2012. 204-208: p. 4105-4109.

Uddin, M.A., et al., Experimental study on strength gaining characteristics of concrete using Portland Composite Cement. KSCE Journal of Civil Engineering, 2013. 17(4): p. 789-796.

Zaman, A.A.U., A. Abdelaty, and M.H.R. Sobuz, Integration of BIM data and real-time game engine applications: Case studies in construction safety management. Journal of Information Technology in Construction (ITcon), 2024. 29(7): p. 117-140.

Rana, J., et al., Impact assessment of window to wall ratio on energy consumption of an office building of subtropical monsoon climatic country Bangladesh. International Journal of Construction Management, 2022. 22(13): p. 2528-2553.

Smith, K.J., G. Dhillon, and L. Carter, User values and the development of a cybersecurity public policy for the IoT. International Journal of Information Management, 2021. 56: p. 102123.

Sobuz, M.H.R., et al., Optimization of recycled rubber self-compacting concrete: Experimental findings and machine learning-based evaluation. Heliyon, 2024. 10(6).

Chen, Y. and X. Han. CatBoost for fraud detection in financial transactions. in 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). 2021. IEEE.

Aditto, F.S., et al., Fresh, mechanical and microstructural behaviour of high-strength self-compacting concrete using supplementary cementitious materials. Case Studies in Construction Materials, 2023. 19: p. e02395.

Lebichot, B., et al., Incremental learning strategies for credit cards fraud detection. International Journal of Data Science and Analytics, 2021. 12(2): p. 165-174.

Khatri, S., A. Arora, and A.P. Agrawal. Supervised machine learning algorithms for credit card fraud detection: a comparison. in 2020 10th international conference on cloud computing, data science & engineering (confluence). 2020. IEEE.

Mytnyk, B., et al., Application of artificial intelligence for fraudulent banking operations recognition. Big Data and Cognitive Computing, 2023. 7(2): p. 93.

Barker, R., The use of proactive communication through knowledge management to create awareness and educate clients on e-banking fraud prevention. South African Journal of Business Management, 2020. 51(1): p. 1-10.

Abidoye, A.P. and B. Kabaso, Hybrid machine learning: A tool to detect phishing attacks in communication networks. ECTI Transactions on Computer and Information Technology (ECTI-CIT), 2021. 15(3): p. 374-389.

Shah, S.S.H., et al., Memory forensics-based malware detection using computer vision and machine learning. Electronics, 2022. 11(16): p. 2579.

Maulana, L.R. and A.N. Fajar. Extending the Design of Smart Mobile Application to Detect Fraud Theft of E-Banking Access Using Big Data Analytic and SOA. in 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). 2021. IEEE.

Al Hattali, S.S.K., S.M. Hussain, and A. Frank, Design and development for detection and prevention of ATM skimming frauds. Indonesian Journal of Electrical Engineering and Computer Science, 2020. 17(3): p. 1224.

Tsai, C.-H. and P.-C. Su, The application of multi-server authentication scheme in internet banking transaction environments. Information systems and e-business management, 2021. 19(1): p. 77-105.

Hammi, B., et al., Blockchain-based solution for detecting and preventing fake check scams. IEEE Transactions on Engineering Management, 2021. 69(6): p. 3710-3725.

Abdul Rani, M.I., S.N.F. Syed Mustapha Nazri, and S. Zolkaflil, A systematic literature review of money mule: Its roles, recruitment and awareness. Journal of Financial Crime, 2024. 31(2): p. 347-361.

Ileberi, E., Y. Sun, and Z. Wang, A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data, 2022. 9(1): p. 24.

Esenogho, E., et al., A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE Access, 2022. 10: p. 16400-16407.

Arora, M. and I. Bhardwaj, Artificial intelligence in collaborative information system. Int. J. Mod. Educ. Comput. Sci.(IJMECS), 2022. 14(1): p. 44-55.

Navaneethakrishnan, P. and R. Viswanath, Fraud Detection On Credit Cards Using Artificial Intelligence Methods. Elementary Education Online, 2022. 19(2): p. 2086-2086.

Polimis, K., A. Rokem, and B. Hazelton, Confidence intervals for random forests in python. Journal of Open Source Software, 2017. 2(19): p. 124.

Isnain, A.R., J. Supriyanto, and M.P. Kharisma, Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning. IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2021. 15(2): p. 121-130.

Zhang, Y.-C. and L. Sakhanenko, The naive Bayes classifier for functional data. Statistics & Probability Letters, 2019. 152: p. 137-146.

Charbuty, B. and A. Abdulazeez, Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2021. 2(01): p. 20-28.

Mood, C., Logistic regression: Why we cannot do what we think we can do, and what we can do about it. European sociological review, 2010. 26(1): p. 67-82.

Liu, F.T., K.M. Ting, and Z.-H. Zhou. Isolation forest. in 2008 eighth ieee international conference on data mining. 2008. IEEE.

Wold, S., K. Esbensen, and P. Geladi, Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 1987. 2(1): p. 37-52.

Linderman, G.C., et al., Efficient algorithms for t-distributed stochastic neighborhood embedding. arXiv preprint arXiv:1712.09005, 2017.

Gonaygunta, H., Machine learning algorithms for detection of cyber threats using logistic regression. Department of Information Technology, University of the Cumberlands, 2023.