Anomaly Prediction Based On LSTM And Autoencoders Using Federated Learning In Financial Transactions- Survey

Abstract

As more and more financial activities transition to digital platform, recognizing unusual transaction patterns has become an important and challenging task. Conventional centralized machine learning models for fraud detection bring with them significant challenges like data privacy and scaling. This survey investigates the promising combination of Long Short-Term Memory (LSTM) networks, Autoencoders and Federated Learning (FL) act as a powerful privacy-preserving solution for anomaly detection on financial transactions. Long Short-Term Memory (LSTM) models are a state-of-the-art choice for learning long range relations on sequential data whereas autoencoders are very efficient models which learn lower-dimensional state representations and pinpoint anomalous behavior. Federated Learning, in contrast, presents a decentralized model training mode in which collaborative learning can be conducted among banks and financial institutions without sharing confidential transaction information. This review discusses the state of the art, the key advances and the potential synergies of these methodologies. This inspires practical implementations of scalable and trustworthy AI-driven financial anomaly systems, as the movement towards an increasingly federated data ecosystem mandates secure and scalable solutions.