Self-Healing Architecture: Using ML to Enhance System Resilience in FinTech Platforms
Prashant Singh · Journal of Artificial Intelligence Machine Learning and Data Science · 2024
The digitization of financial services only increases the stakes for dependable, always-on FinTech systems that can recover from or weather system-level downtime.With the scale of these platforms growing thanks to our microservices adoption, container orchestration and real-time transaction pipelines, traditional reactive approaches to the failure of system components are no longer good enough.In this paper, a machine learning-driven self-healing framework is emerging for preemptive failure analysis, adaptive diagnostics and autonomous restoration in modern FinTech ecosystems.Leveraging the synergy of supervised learning for on-time anomaly detection, unsupervised learning for implicit pattern exploration and reinforcement learning for automatic policy creation, the resultant system continuously observes telemetry events, user transaction traces and resource usage signals at runtime to infer system fitness.When a potential fault or degradation is predicted, a fixed action, like restarting a service, re-routing traffic or initiating a scaling operation, is taken automatically without manual intervention.The system is developed using container-native tools in conjunction with model inference layers and can be deployed on off-the-shelf FinTech stacks.We evaluate the solution's effectiveness in several real-world failure scenarios, such as transaction latency spikes, payment API memory leaks and unexpected node crashes in distributed ledger networks.Results indicate a drastic reduction in downtime, mean time to resolution and operational overhead versus a baseline reactive approach.This work showcases the possibility of smart autonomy for FinTech platforms to uphold stringent uptime requirements, regulatory adherence and user confidence.The proposed solution raises fault tolerance and constructs the grounds for scalable, adaptive and innovative infrastructures in the financial computing environment.