ENHANCING DATA QUALITY IN CREDIT PROCESSING PIPELINES: A TECHNICAL DEEP DIVE - Mithun Kumar Pusukuri (INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 2024)
Credit processing pipelines represent a critical infrastructure in modern financial institutions, handling increasingly complex data streams and decision-making processes. This technical article examines the challenges and solutions in maintaining data quality across credit processing operations, focusing on implementing AI observability and advanced monitoring systems. Let’s explore how financial institutions leverage pattern recognition algorithms, automated validation frameworks, and machine learning models to enhance data quality management. The article presents a comprehensive analysis of real-time error detection mechanisms, advanced data profiling techniques, and the integration of AI observability solutions. Through a detailed case study of a major credit institution, we demonstrate these technologies' practical implementation and measurable impacts. The article also evaluates various open-source tools and emerging best practices while providing insights into future credit pipeline data quality management trends.