Fraud detection has been a significant problem in the banking, insurance, and medical sectors. The total combined fraud losses climbed to $56 billion in 2020 (Business Wire). The large amount of confidential data stored online makes the financial and banking sector vulnerable and prone to security breaches. Identifying and preventing such threats is a challenging task. Earlier, fraud identification systems were created based on pre-defined rules that modern hackers can easily hack. The current industry trend has evolved to use data science and machine learning models to authenticate and prevent customers from fraudulent financial transactions.
You can build a completely automated financial system that can improve the efficiency of transaction fraud alerts for millions of people around the globe, which will help financial services firms reduce their losses and increase revenue. In the Machine learning world, fraud identification is classified as a classification problem. You will build a model using machine learning techniques that can predict 0 or 1 given various user transaction data. 0 generally suggests that a transaction is classified as non-fraudulent, and 1 suggests that the transaction is fraudulent. You can use this IEEE-CIS Fraud Detection Dataset for this finance machine learning project.
You can use the StratifiedKFold method to split data randomly, maintain the same class distribution, and overcome the imbalanced data problem, leading to a biased prediction model. You can use simple machine learning algorithms like logistic regression, and random forest can classify the training data and build the model. Remember to use Label encoding on the categorical data before using it to train the models.