Abstract: Since fraud is now much more common and sophisticated due to digital payments and online financial services, financial institutions need to be able to identify it promptly and accurately. In the context of real-world transaction streams, standard rule-based and classical machine learning techniques typically encounter difficulties with complicated temporal linkages, unbalanced transaction data, and fraud patterns that change over time. This paper provides a deep learning-based fraud detection system that models transactional activity and detects anomalous behaviors with high recall and precision in order to overcome these limitations.

The suggested method integrates representation learning and deep sequence learning to find both short-term and long-term patterns in transaction data. To comprehend how user activities correlate over time, recurrent and attention-based neural architectures are used to simulate transaction sequences. Compact representations of valid transactions are learned by an autoencoder-based anomaly detection module, which then identifies any deviations that point to fraud. Reconstruction mistakes, engineering behavioral data, and supervised classification scores are combined in a fusion layer to generate a high fraud risk score for every transaction. When there are numerous classes that differ significantly from one another, this hybrid design facilitates the detection of fraud as well as the discovery of novel fraud tactics.

Using benchmark transaction datasets that replicated real attacks, we tested the system in fake fraud situations. Based on the F1-score and the area under the precision-recall curve, experimental results demonstrate that the suggested model consistently performs better than both stand-alone deep learning models and conventional machine learning baselines. Additionally, it may be deployed immediately due to its short inference latency. By elucidating concepts and supporting decision-making, feature attribution methodologies can facilitate model comprehension. The findings demonstrate the efficacy of deep learning-based fraud detection systems and provide crucial criteria for developing scalable, accurate, and reliable AI solutions in financial contexts.

Keywords: Financial transaction analysis, deep learning, class imbalance, sequence modelling, autoencoders, explainable artificial intelligence (XAI), financial cybersecurity, anomaly detection, fraud detection, and real-time fraud analytics.

Downloads: | DOI: 10.17148/IJIREEICE.2025.131148