AI-Based Early Warning System for Financial Scams Targeting Consumers
Main Article Content
Abstract
Financial scams are aimed at consumers and are getting more advanced and use behavioral weaknesses, social engineering and rapid change frameworks. Innovative fraud-detection by rule based systems are likely to have problems in detecting the new scamp system, especially when genuine sounding transfers are being designed on an impersonation, investment as well as the elder fraud scam. This paper will present an Early Warning System (EWS) that will be essentially an AI-based system that aims to anticipate unusual financial conduct among the consumers with substantial losses. The built tool will be a hybrid of a transaction monitoring system, behavioral profiling, anomaly detection, machine learning, and generation of risk alerts to assess the abnormalities in the typical consumer transaction patterns. The system will detect the abnormal transfers associated with impersonation, investment and elder fraud by examining the frequency of the transactions, amount of transfer, characteristics of the recipient, time of transfers, geographical mismatches as well as the changes in behaviors. Explainable AI methods are also added to give transparent justifications of the generated alerts to justify an early corrective action by financial institutions and consumers. A dynamically-risk-scoring system and action-responsive are prioritized to potential fraud basing on the size of scam. The suggested approach will probably decrease the false-positives, yet enhance the level of early detection, response-time, and consumer protection. The framework provides a proactive System of smart financial fraud and can be integrated into the new banking and online payment systems.
Article Details
Section
How to Cite
References
[1] J. Jansen and R. Leukfeldt, “How people help fraudsters steal their money: An analysis of 600 online banking fraud cases,” in Proc. Workshop Socio-Technical Aspects Security and Trust (STAST), Verona, Italy, Jul. 2015, pp. 24–31, doi: 10.1109/STAST.2015.12.
[2] R. Barker, “The use of proactive communication through knowledge management to create awareness and educate clients on e-banking fraud prevention,” S. Afr. J. Bus. Manag., vol. 51, no. 1, Art. no. a1941, 2020, doi: 10.4102/sajbm.v51i1.1941.
[3] A. P. Abidoye and B. Kabaso, “Hybrid machine learning: A tool to detect phishing attacks in communication networks,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 6, pp. 559–569, 2020, doi: 10.14569/IJACSA.2020.0110668.
[4] L. R. Maulana, A. N. Fajar, and Meyliana, “Extending the design of smart mobile application to detect fraud theft of E-banking access using big data analytic and SOA,” in Proc. 2021 IEEE 5th Int. Conf. Inf. Technol., Inf. Syst. Electr. Eng. (ICITISEE), Purwokerto, Indonesia, Nov. 2021, pp. 360–364, doi: 10.1109/ICITISEE53823.2021.9655805.
[5] S. S. Khalaf Al Hattali, S. M. Hussain, and A. Frank, “Design and development for detection and prevention of ATM skimming frauds,” Indones. J. Electr. Eng. Comput. Sci., vol. 17, no. 3, pp. 1224–1231, 2019, doi: 10.11591/ijeecs.v17.i3.pp1224-1231.
[6] C. Tsai and P. Su, “The application of multi-server authentication scheme in internet banking transaction environments,” Inf. Syst. e-Bus. Manag., vol. 19, pp. 77–105, 2021, doi: 10.1007/s10257-020-00481-5.
[7] B. Hammi, S. Zeadally, Y. C. E. Adja, M. Di Giudice, and J. Nebhen, “Blockchain-based solution for detecting and preventing fake check scams,” IEEE Trans. Eng. Manag., vol. 69, pp. 3710–3725, 2022, doi: 10.1109/TEM.2021.3087112.
[8] M. I. Abdul Rani, S. N. F. Syed Mustapha Nazri, and S. Zolkaflil, “A systematic literature review of money mule: Its roles, recruitment and awareness,” J. Financ. Crime, 2023, doi: 10.1108/JFC-10-2022-0243.
[9] E. Ileberi, Y. Sun, and Z. Wang, “A machine learning based credit card fraud detection using the GA algorithm for feature selection,” J. Big Data, vol. 9, Art. no. 24, 2022, doi: 10.1186/s40537-022-00573-8.
[10] J. Chaquet-Ulldemolins, F.-J. Gimeno-Blanes, S. Moral-Rubio, S. Muñoz-Romero, and J.-L. Rojo-Álvarez, “On the black-box challenge for fraud detection using machine learning (I): Linear models and informative feature selection,” Appl. Sci., vol. 12, no. 7, Art. no. 3328, 2022, doi: 10.3390/app12073328.
[11] N. Nguyen, T. Duong, T. Chau, V.-H. Nguyen, T. Trinh, D. Tran, and T. Ho, “A proposed model for card fraud detection based on CatBoost and deep neural network,” IEEE Access, vol. 10, pp. 96852–96861, 2022, doi: 10.1109/ACCESS.2022.3205416.
[12] E. Esenogho, I. D. Mienye, T. G. Swart, K. Aruleba, and G. Obaido, “A neural network ensemble with feature engineering for improved credit card fraud detection,” IEEE Access, vol. 10, pp. 16400–16407, 2022, doi: 10.1109/ACCESS.2022.3148298.
[13] P. Gupta, A. Varshney, M. R. Khan, R. Ahmed, M. Shuaib, and S. Alam, “Unbalanced credit card fraud detection data: A machine learning-oriented comparative study of balancing techniques,” Procedia Comput. Sci., vol. 218, pp. 2575–2584, 2023, doi: 10.1016/j.procs.2023.01.231.
[14] P. Sharma, S. Banerjee, D. Tiwari, and J. C. Patni, “Machine learning model for credit card fraud detection—A comparative analysis,” Int. Arab J. Inf. Technol., vol. 18, no. 6, pp. 789–796, 2021, doi: 10.34028/iajit/18/6/6.