CASE STUDY

Data Platform Modernization Case Study: Real-Time Credit Decisioning in BFSI

Introduction

This case study underscores the vital role of modern data platforms in driving social impact through financial inclusion. By leveraging cloud technologies, advanced processing tools such as Databricks, Snowflake, and robust governance practices, Hoonartek helped the client transform a fragmented data landscape into a unified, strategic asset. This transformation is not just beneficial for the client, but also crucial for gaining a competitive edge by unlocking the value of customer acquisition, pre-approved loans proposal data and driving sustainable growth in the rapidly evolving microfinance sector.

Faced with the challenges of a rapidly evolving business landscape and the need for data-driven decision-making, the Client recognized the critical need to modernize their existing data infrastructure. Their legacy data warehouse, constrained by limited scalability, slow query performance, and data quality issues, was no longer adequate to support their growing business needs.

The Business Challenge

The client’s rapidly expanding operational footprint and a legacy data environment created significant organizational hurdles that threatened timely credit decisioning and growth:

Data Silos and Fragmentation: Data was scattered across numerous disparate sources, including multiple core banking systems, servers, and business units, preventing the establishment of a single, unified data repository for enterprise analytics.
Inability to Leverage Unstructured Data: Critical data for credit assessment, such as Credit Bureau reports, was stored in complex, semi-structured formats (XML and zip files), rendering it inaccessible for automated data analytics and real-time risk modelling.
Delayed Decisioning due to Batch Processing: Data was not available in near real-time, impeding the speed required for critical functions like credit decisioning and dynamic risk analytics.
Operational Impact from Slow Data Access: Direct queries against operational source systems led to long query runtimes, impacting system performance and slowing down internal decision-making processes.
Compliance and Governance Deficiencies: The manual preparation of regulatory reports and the complete lack of automated data lineage created significant auditability challenges and increased compliance risk.

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Joint Solution Discovery & Implementation Plan

Recognizing the need for deep domain expertise, the client chose Hoonartek based on our proven track record of successful data modernization projects and specialized understanding of the financial inclusion sector.

The engagement began with a comprehensive assessment of the client’s data landscape—including sources, volumes, quality, and specific business requirements. Through collaborative workshops, a roadmap for phased data platform implementation was developed. This roadmap focused on creating a modern data ecosystem using cloud-native tools:

Cloud Migration and Architecture

Cloud Platform Adoption: Migrated all existing data sources—both structured and semi-structured—to the Microsoft Azure cloud, leveraging its high scalability, flexible performance, and cost-effectiveness.
Medallion Architecture: Established a Data Lake architecture using the Medallion framework to systematically store, process, and govern data quality across all layers.

Data Ingestion and Transformation

Robust Pipeline Implementation: Deployed a data pipeline using Azure Data Factory (ADF) to efficiently ingest data from over 20 source systems and 550+ data feeds.
Real-Time Capabilities: Integrated Oracle GoldenGate (OGG) and Azure Event Hubs to establish real-time data streaming, enabling near real-time analytics for faster risk management.
Advanced Transformation: Developed a custom ETL framework and data transformation logic using Databricks to manage Data Quality, implement Slowly Changing Dimensions (SCD), and maintain historical data for trend analysis.
Semi-structured Data Engine: Built a dedicated engine using Databricks to flatten and process complex external data formats, specifically Credit Bureau XML and zip files containing bulk XMLs, making this critical information available for the Data Science teams.
Business Consumption Layer: Implemented aggregated business data and high-performance Data Marts on Snowflake to ensure rapid and efficient consumption by business users.

Data Quality and Governance

Data Quality Framework: Implemented a comprehensive data quality framework, including profiling and validation rules, defining and enforcing over 1000+ DQ Rules to ensure organizational data consistency.
Data Lineage and Catalog: Integrated Azure Purview for enterprise-wide data catalog, metadata management, and business glossary, empowering data consumers to easily discover, understand, and trust the data assets.

Industry Perspective

This project highlights the vital role of modern data platforms in driving social impact through financial inclusion. By leveraging cloud technologies, advanced processing tools such as Databricks, and robust Digital Engineering practices, Hoonartek helped the client transform a fragmented data landscape into a unified, strategic asset. This transformation is crucial for gaining a competitive edge and driving sustainable growth in the rapidly evolving microfinance sector.

Outcomes

The successful implementation of this new data platform, powered by Databricks and Snowflake, delivered immediate and significant benefits for the client, accelerating their mission of financial inclusion:

Accelerated Decision-Making

Significantly reduced query response times, enabling faster and more informed credit decisioning across 10+ business departments, directly improving loan processing efficiency.

Enhanced Data Quality and Reliability

The flattening engine successfully converted complex, semi-structured Credit Bureau data into a usable format, significantly improving data accuracy and consistency critical for risk modelling.

Increased Scalability and Futureproofing

The cloud-based platform provides the necessary scalability to manage future data growth and evolving business needs, now supporting an enterprise data model with over 25 key entities.

Robust Compliance and Auditability

The implementation of data lineage tracking and security measures ensures compliance with relevant regulations and minimizes operational and security risks.

Reduced Operational Overhead

Optimized data storage costs through cloud-native solutions and streamlined data management processes through the automation of various regulatory reporting and manual data processes.

Conclusion:

The successful deployment of this enterprise data platform demonstrates Hoonartek’s ability to navigate complex data challenges within high-growth, mission-driven institutions. By combining deep industry expertise with cutting-edge technologies, we empowered the client to operationalize their data assets, drive significant business growth, and maintain market leadership.

To learn how Hoonartek can help your organization overcome similar data challenges, unlock the true value of your data, and drive significant business outcomes, schedule a free consultation with our data experts today. We’ll discuss your specific needs and explore how our data-driven solutions can help you achieve your business goals.