Predictive Reliability Modeling for Regulatory Systems in Modern Financial Institutions

In this paper, we propose a new predictive reliability model for the regulatory systems in a distributed environment. While existing approaches to observability in these systems are based on threshold violations and alerts, our approach is based on temporal pattern recognition and multi-dimensional signal correlation to predict reliability degradation before regulatory violations happen. The model ingests telemetry data related to service latencies, error rates, resource consumption, and transaction flows, and uses machine learning to predict phases of stress on the system over configurable time horizons. When triggered by reliability scores, remediation is performed automatically by orchestration platforms whenever the confidence is more than a user-settable risk threshold. The decision to rebalance workloads, scale resources, and trigger circuit breakers is done automatically; however, service isolation and failover are handled by human operators when the model's prediction is below a user-settable confidence threshold. Smart caching and streaming analytics allow it to operate continuously without sacrificing transaction throughput. Running on production data validates the model's output in terms of its ability to flag reliability issues in time for further intervention, as opposed to only being used for diagnostics like other monitoring models. It is especially useful in regulatory system reliability applications, where standard monitoring systems do not provide sufficient warning of possible compliance violations.