The practice of obtaining financial gains by dishonest and unlawful means is known as financial fraud. Financial fraud, which is defined as the use of dishonest methods to obtain financial gains, has recently grown to be a serious threat to businesses and organizations. Despite several initiatives to curtail financial fraud, it continues to negatively impact society and the economy since daily losses from fraud amount to significant sums of money.

Several methods for detecting fraud were first introduced many years ago. The majority of old procedures are manual, which is not only time-consuming, expensive, and inaccurate, but also unworkable. There are more studies being done, however they are ineffective at reducing losses brought on by fraud. Conventional methods for detecting these fraudulent activities, like human verifications and inspections, are inaccurate, expensive, and time-consuming.

Machine-learning-based technologies can now be used intelligently to identify fraudulent transactions by examining a significant amount of financial data, thanks to the development of artificial intelligence. As a result, this study seeks to offer a novel model of fraud detection on bank payments utilizing the Random Forest Classifier Machine Learning Algorithm.

Our suggested system makes use of the Banksim dataset, and we have demonstrated that it is more effective than the current system by achieving train and test accuracy of 99%.

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