Deep Learning vs. Financial Fraud Real-Time Detection in High-Frequency Trading

HFT systems are sensitive to microseconds, and generate order-book streams that present novel challenges to the detection of fraud-related behaviors like spoofing and layering. Current algebraic-based surveillance strategies are ineffective in describing the nonlinear temporal patterns and subtle manipulation schemes that exist in contemporary financial markets since these strategies are typically solely rule-based. This paper presents an exploratory investigation of DL architectures to support real-time fraud detection in HFT, albeit with very low costs in terms of latency and in spite of predictions with a high level of accuracy. We test temporal convolutional networks (TCNs) and lightweight Transformers as well as machine learning- and rule-based baselines under a mix of historical data collected on a limit-order-book (LOB) and simulator-generated manipulation scenarios. We combine latency-aware acceleration techniques, including quantization, pruning, and micro-batching within a streaming design that can complete inference in sub-5 Ms. Experimental outcomes support the claim that DL models can perform detection better at extremely low false-positive rates even as operational service-level objectives are met. In addition to benchmarking, we cite difficulties of distributional robustness, deployment tradeoffs and explain ability, providing a reproducible framework and methodological improvements to applying deep learning to real time fraud detection in high frequency financial settings.