Importance of detecting anomalies in the environmental context of the banking sector by using machine learning models

Abstract

In the contemporary mobile banking sector, fraud transactions are becoming a huge concern day by day due to inadequate knowledge, susceptibility to phishing, and the propensity of individuals unfamiliar with banking practices, in such cases the victim disclosing the OTP(One Time Password) to the deceptive callers. We used machine learning (ML), precisely based on speech recognition, to cut down fraud activities. For the datasets both fraud calls and legitimate calls are employed in this study. Remarkable algorithms such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, Logistic Regression, and Decision Tree Algorithm are utilized for optimal speech recognition. The Random Forest Algorithm delivers 99% accuracy to contribute an efficacious speech recognition framework. This approach is proposed to detect fraud callers in the realm of online mobile banking for a robust solution during a routine transaction. Predicted speech recognition determines the nature of anomaly detection delivering a potential way to minimize fraud in the expanded mobile banking sector. Methodologies like fraud caller detection have shown promising results that can reduce this rising threat accurately. This is complete research on Reliability and usefulness of the project, by reducing the risk of fraud transaction and enhancing the capabilities of anomaly detection during transaction. Moreover, this approach will give an immediate solution to the ordinary people who are facing such deceptive activities in their financial transaction.