Prediction and Analysis of Fraud Detection in Finance: A Deep Learning and Machine Learning based approach

Online financial transactions have become an essential part of our everyday lives in this era of digital commerce. But in addition to its convenience, there’s a real risk of fraudulent activity, which could lead to serious financial losses and erode confidence in digital financial institutions. This research project focuses on the creation and evaluation of sophisticated fraud detection algorithms in an effort to address this urgent problem. In addition to traditional machine learning methods, the research makes use of state-of-the-art deep learning models including Long Short-Term Memory (LSTM), Feedforward Neural Network (FNN), and Recurrent Neural Network (RNN) to accomplish this. Large-scale datasets are used to train these models in order to find patterns suggestive of fraudulent transactions. The intricate interplay between transactional data, consumer behavior, and temporal relationships within these deep learning architectures significantly enhances the accuracy and efficiency of fraud detection processes.