Abstract: This research explores the application of artificial intelligence in detecting bank fraud, a problem exacerbated by the COVID-19 pandemic's shift to online operations and the proliferation of charitable funds used by criminals to deceive users. The study focuses on leveraging machine learning algorithms to analyze and identify fraudulent transactions in online banking. Its key contribution lies in developing machine learning models tailored for detecting fraudulent banking activities, along with preprocessing techniques to enhance data comparison and result selection. Additionally, the paper elaborates on methods to enhance detection accuracy, including managing highly imbalanced datasets, transforming features, and engineering new features. However, this paper proposes the use of Convolutional Neural Network (CNN) for UPI fraud detection. The CNN model is designed to analyze the spending profile of cardholders, thereby enhancing the accuracy of fraud detection. The Fraud Detection System (FDS) implemented in the bank monitors the spending patterns of cardholders, automatically blocking transactions deemed unusual and alerting the bank for further investigation. This approach minimizes the need for manual intervention and ensures swift action against fraudulent activities, thereby safeguarding users' financial assets.
Keywords: Transaction, Payment, UPI, Attackers, Fraudulent, Money, Datasets, Machine learning, recognition of fraudulent operations, Convolutional Neural Networks,
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DOI:
10.17148/IJARCCE.2024.134143
