Financial__Fraud__Prediction

This project aims to address the problem of fraud in mobile payment systems, which has become increasingly prevalent with the rise of smartphones. Researchers have developed various fraud detection methods using supervised machine learning, but one major challenge in this area is the lack of enough labeled data, which can negatively impact the performance of these detection methods. Additionally, financial fraud data often suffer from extreme class imbalance, where the number of non-fraud instances far outnumber the fraud instances, further complicating the problem. The main challenges in detecting fraud in mobile payment transactions include changing patterns of fraud over time and inadequate selection of performance metrics. To address these challenges, the project proposes a novel approach for real-time fraud detection in financial payment services. The approach utilizes machine learning techniques to build a predictive model that can detect fraud in online transactions as they occur. This approach can help service providers to effectively identify and prevent fraudulent activities.