Enterprise Data Lakes for Credit Risk Analytics: An Intelligent Framework for Financial Institutions

Abstract

Financial institutions face unprecedented challenges in managing massive, heterogeneous datasets for
credit risk analytics while ensuring regulatory compliance and real-time decision-making capabilities.
This paper introduces an intelligent enterprise data lake framework (IEDLF) designed to address these
challenges through a unified, scalable architecture that integrates data engineering, machine learning,
and metadata-driven governance. By applying the schema-on-read principles, the framework integrates
structured, semi-structured, and unstructured data from various sources, including credit bureaus,
transactional systems, and alternative data streams. The IEDLF transforms conventional static reporting
systems into dynamic intelligence centers by integrating AI-driven credit scoring models, real-time
processing capabilities utilizing Apache Spark, and automated ingestion pipelines leveraging Apache
Kafka and NiFi. The architecture encompasses multiple layers: source, ingestion, validation, storage, and
consumer – each optimized for specific functions within the credit risk analytics workflow. Implementation
strategies incorporate comprehensive data quality frameworks using Great Expectations and Deequ to
ensure reliability and transparency. The framework demonstrates how financial institutions can achieve
scalable, compliant, and insight-driven credit risk management while overcoming limitations of legacy
systems and siloed infrastructures, ultimately enabling enhanced predictive modeling, portfolio stress
testing, and automated decision-making aligned with Basel III and International Financial Reporting
Standard 9 regulatory requirements credit risk management while overcoming limitations of legacy
systems and siloed infrastructures.