DESIGNING OBSERVABLE MACHINE LEARNING PIPELINES FOR REAL-TIME CREDIT RISK DETECTION: A SCALABLE APPROACH

Abstract

This article introduces a comprehensive framework for implementing observable machine learning pipelines in real-time credit risk detection systems. The article presents a novel approach that integrates advanced observability components, including automated drift detection, feedback loops, and A/B testing infrastructure, to enhance the accuracy and reliability of credit risk assessment. The implemented system demonstrated substantial improvements in early risk detection rates and a reduction in false positives compared to traditional methods. Through a microservices-based architecture, the system maintained exceptional latency performance while processing high volumes of requests, successfully handling significant surge loads. The article research also addresses critical challenges in scalability, data privacy, and model stability, proposing innovative solutions for maintaining system reliability in high-stakes financial decisions. The article demonstrates significant improvements in feature drift detection, overall prediction accuracy, and notably reduced model adaptation time. The framework's success in balancing real-time performance requirements with model complexity while ensuring privacy compliance and distributed deployment coordination represents a significant advancement in automated credit risk assessment technology. This work offers valuable practical implications for financial institutions seeking to enhance their risk management capabilities through advanced machine learning systems.