Case Study

A Robust Data Management Platform with AI-powered Reporting

A Loan and Credit Cards Company Improves Data Governance and Automates its Reporting Process

An esteemed loan and credit cards company implements a robust data ingestion framework to improve data governance and automates its reporting process to deliver AI-generated financial reports to issuer banks.

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Industry & Region: Banking and Financial Services, USA
Technology Stack: AWS MSK, AWS Glue, AWS RDS, Confluent, Databricks, Apache Spark, Databricks Delta Live Tables, Debezium, Amazon Redshift, Python, Shell Script, MariaDB, MySQL
Client Overview

Our client is a reputed loan and credit card company partnering with industrial issuer banks across the US to offer affordable financing to customers.

Business Challenge
Our client’s new corporate ventures had resulted in a rapid increase in the inflow of data and delays in processing this large volume of data were impacting customer experience and bottom line.
As there was no automated process of identifying and tracking changes made to data sources over time, manual data change management mechanisms were time-intensive and left room for errors. These errors or discrepancies could result in financial and legal consequences.
Furthermore, they were facing challenges in delivering the complex financial reports that their industrial bank partners needed for lending decisions. Their current manual reporting processes caused delays in meeting the specific requirements of each bank partner and submitting these reports to them in a timely and accurate manner. The company wanted to overcome these redundancies and speed up its reporting processes to ensure these do not get in the way of its business relations with the lending banks.
Solution Offered
After a thorough analysis of these challenges faced by the client, our team proposed a robust and automated data platform that could efficiently ingest large volumes of data from their diverse systems of records, as well as third-party product vendors. The modern data platform would also manage change data capture far more efficiently than the manual system for enhanced data accuracy and consistency.

Based on the proof of concept (POC), a data ingestion framework was built on Databricks and AWS, using the medallion data architecture for credit cards and loans. We used data pipelines to connect to source databases to ingest data from multiple sources. This allowed for scalability and flexibility in ingesting data and streamlined the data ingestion process for data consistency across sources.

We enabled change data capture with the help of Debezium to capture and propagate data changes in real-time. The Confluent data ingestion platform was used for ingesting data and saving it in S3 buckets. Data extraction from the S3 location was made possible using Databricks and the delivery of the data to Enterprise Data Warehouse was enabled using Data Live Tables. The solution could also generate reports based on various business conditions from data sources like MariaDB and other partner data from S3 far more seamlessly now the automated reports generated across multiple business areas such as credit cards and embedded finance could be securely transferred using SFTP to the bank’s S3 location.

Value Delivered
  • Provided a unified view of data and centralized access to the data team for holistic decision-making.
  • Enabled storage and processing of large volumes of data in real-time in a cost-effective and scalable manner.
  • Improved data governance and reduced the risk of errors and inconsistencies.
  • Streamlined change management with change data capture.
  • Enhanced decision-making through real-time insights from the data.
  • Strengthened business relations with issuer banks through fast and accurate report generation.
  • Transformed the overall customer experience through faster disbursal of loans and credit cards to the right customers.
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