AI and Machine Learning Architectures for Autonomous Reliability in Financial Data Platforms

Abstract

The quantitative results of AI-native financial data pipeline reliability architecture have been presented in this paper. The findings yield the fact that transformer models, DeepLog-type log analysis and reinforcement learning are superior models as far as failure prediction, anomaly detection, and compliance stability are concerned. The system detects weak signals of drift and silent corruption at an earlier stage as compared to rule based monitoring. Reinforcement learning improves throughput, latency and recovery in the event of spikes or failure. The cases of compliance are minimized with the scores of reliability confidence and the risk is detected at an earlier stage. The architecture also improves fault containment, and prevents more large scale system issues. The results have shown that there are high levels of accuracy gains, stability gains, recovery time gains and operations resilience gains.