Predictive Failure Modeling for Microservices-Based Financial Platforms

Microservices architectures have become central to modern financial platforms, offering scalability and faster delivery, although they also introduce distributed dependencies, partial failures, and cascading failure dynamics that threshold-based and reactive monitoring cannot adequately address. This review examines how predictive failure modelling may strengthen the reliability of microservices-based financial platforms, and is guided by three questions: how predictive models are built to recognise failures in microservices environments, what challenges arise in deploying them in finance, and which trade-offs organisations face when selecting among them. Drawing on literature from distributed systems engineering, machine learning, and financial technology, the review traces how failure dynamics have changed with the shift from monolithic to microservices architectures, defines predictive failure modelling in relation to anomaly detection and root cause analysis, and then compares four families of techniques (anomaly detection, time-series modelling, classification, and reinforcement learning) against criteria relevant to financial platforms. It also considers the architectural and data engineering foundations these methods depend on, including telemetry pipelines, feature engineering, deployment, and model maintenance. The review identifies recurring challenges, including data heterogeneity, scarcity of labelled failure events, concept drift, the balance between sensitivity and alert fatigue, and the limited interpretability of complex models in regulated settings. The reviewed literature provides limited evaluation of production financial platforms. The review concludes that predictive modelling is most effective as part of a broader system, supported by mature observability, MLOps practices, and human oversight, and highlights explainable AI, reinforcement learning for adaptive control, digital twins, and the integration of business and regulatory context into prediction as priorities for future work.