AI-Powered Fraud Detection in Banking Transactions Using Deep Learning

A very significant risk and complexity which has emerged in fraudulent activities in banking
transactions is the accelerated rate at which the financial services have become digitized. This
study brings forth a state-of-the-art AI enabled fraud detection framework utilizing deep learning
technology to increase the accuracy and real-time response of detecting anomalous patterns. More
specifically, we deploy a Long Short-Term Memory (LSTM) neural network architecture because
of its enhanced ability to learn the temporal dependencies in sequential transaction data. A real
world financial dataset is used to train and test the model using Google Cloud AI Platform that
guarantees scalable processing and integrated model deployment. Through extensive experiments
it is shown that the proposed system is capable of delivering high precision and recall when it
comes to detecting fraudulent behavior and outperforms traditional ML baselines. Such research
emphasizes the effectiveness of the deep learning technology in automating fraud detection and
demonstrates the breakthrough novelty of cloud-based AI tools to the financial industry.