Fraud detection using big data tools and machine learning in banking

The use of big data analytics applications is prevalent in the banking industry, owing to the abundance and quality of customer information and transaction records available through online and offline channels. Processing such data through machine learning algorithms can greatly benefit decision-making processes. Big data applications can be employed by banks to identify fraudulent money transfer transactions, which pose a substantial risk to their financials and reputation. This study presents information on rule-based systems and big data applications for fraud detection in banks. Digital money transfer data obtained from a private bank was subjected to different supervised and unsupervised classification models, including extreme gradient boosting and isolation forests, and their results were compared. The extreme gradient boosting model displayed superior performance, while the unsupervised isolation forest algorithm provided notable outcomes. It was also concluded that the application of big data analytics and machine learning significantly contributes to fraud detection.