Vulnerabilities in the banking system have made us open to fraudulent actions, which not only hurt customers but also the bank's reputation and bottom line. Financial fraud in banks is responsible for the loss of an estimated significant quantity of money annually. The fraud may be reduced with the help of early identification, which enables the creation of a countermeasure and the restoration of such losses. To help with fraud detection, this study suggests a machine learning-based approach. The technology, which is based on artificial intelligence (AI), will speed up the check verification process to fight counterfeits and reduce damage. In this post, we looked at a bunch of smart algorithms that were trained on a public dataset to find out which factors are associated with fraud. The dataset utilised for this investigation is resampled to lower the high class of imbalance. For more precise results, the data is analysed using the proposed method.

Financial Fraud, Banking System Vulnerabilities, Machine Learning, Artificial Intelligence, Fraud Detection, Class Imbalance, Dataset Resampling.