Fraud Detection in Digital and Netbanking Using Machine Learning

Financial fraud is a dishonest and illegal way to make money. Using dishonest means to gain financial benefit is called financial fraud and has become a threat to companies and organizations in recent years. Despite many measures taken to prevent financial fraud, it shows negative impact on people and businesses as losses from fraud are increasing. Various fraud detection methods have been used over the years. Most previous processes were still done manually; This was expensive, time-consuming, inaccurate and ineffective. More research is being done, but this will not help reduce losses from fraud. Traditional methods such as manual checks and inspections are effortless, inefficient, and costly in detecting these fraudulent activities. With the advances in artificial intelligence, machine learning-based technology can detect fraud by analyzing large amounts of financial data. Therefore, the aim of this study is to recommend payment methods in banking fraud models using the random forest classifier, a machine learning algorithm. Through training and validation, the proposed model is simulated using Banksim dataset.