Detect Suspicious Transactions and Identify Fraud Transactions in Banking Data Using Machine Learning - Sharma, Geetanjali; Bhushan, Shashi; Joshi, Ram; Manna, Asmita; Suryavanshi, Madhuri Amol (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The identification of potentially malicious transactions is a crucial and difficult activity for the purpose of preventing cyberattacks, which may have devastating effects on a company's finances, reputation, and legal standing. This is an extremely important problem for assuring the safety of investors in investment applications. When it comes to our scenario, the goal of identifying potentially suspicious transactions is to determine the typical patterns of behavior shown by application users and to pinpoint any circumstances that deviate significantly from these norms at the appropriate moment. Changing your password, logging in from several locations, and engaging in high-volume cash flow or trading activities are all examples of events that have a high level of significance for investing applications. It is very essential to promptly identify suspicious transactions and activity and to take the appropriate steps in order to protect the users as well as the assets and reputation of the firm. The proposed method for predicting fraudulent transactions begins with the dataset, which is pre-processed in the initial phase and then passed into further steps where, test/train split was performed, dataset spliced model parameters were defined, then genetic algorithm for feature selection was initiated, which returns weights for particular features after that model is loaded and those weights are passed into it, and model training was then initiated, and once training was completed, model M was returned.