Real-Time Fraud Detection Using Time Series Anomaly Detection

As we move towards the digitized world, the electronic payment systems has increased the risks offraudulent activities which are a serious challenge to financial security. Fraud detection systems that arebuilt on rule-based logic and statistical methods usually fail to identify highly sophisticated fraud schemesthat continue to evolve. This is the gap that the study focuses on by proposing the use of realtime anomalydetection machine learning and deep learning techniques for fraud detection. The framework is builtaround multiple techniques such as Random Forest classification, ARIMA time-series forecasting, outlierdetection using Isolation Forest, Long Short-Term Memory (LSTM) networks, and deep anomalydetection through Autoencoders. The dataset is subjected to extensive preprocessing, which consists offeature encoding, scaling, and addressing class imbalance. The use of suprevised and unsupervisedlearning models within a single approach is useful in increasing the detection of fraudulent activities whileminimizing false positives. The experiments showed the effectiveness of the techniques in detectinganomalies in financial transactions. Identifying the fraud detection capabilities of different modelsprovided the comparative evaluation of their advantages and disadvantages.