Case Study | Banking & Financial Services
Catalyzing an analytics-led transformation for one of Europe’s largest credit management companies.
The client is one of Europe’s largest credit management enterprises, formed through the merger of UK’s and Germany’s former market leaders. The client leverages a combination of data analytics insights and robust risk management to provide expert solutions in debt purchasing, third-party collections, and business process outsourcing.
With 8 lines of business across 9 different countries in the EU, the client inherited 500+ source systems that were regularly accessed by 3000+ users. The company wanted to accelerate bringing highly accurate and reliable predictive AI/ML models to market. In a growing industry with increasing credit risk exposures and frauds, it was critical for the client to foresee and minimize potential risks through effective data models at scale. The client faced various challenges, ranging from the lack of tools for self-service BI to high inefficiencies and costs with data management. To name a few:
Isolated and disparate systems/data sources, and an extremely high time (8+ months) required to build analytical models
Lacklustre cross department alignment on data processes, lack of roles and accountability for data management, and difficulties maintaining regulatory compliance
Brillio provided a multi-year roadmap for data transformation on cloud which addressed the current requirements, as well the client’s aspirations to become a self-serve analytics-driven organization, capable of leveraging enterprise-grade analytics models for business decision making. The solution was rolled out in a phase approach as follows:
By implementing Brillio’s solution, the comprehensive data and analytics modernization endeavor resulted in a return on their investment of ~3.7x with a profit boost of $17 million. By leveraging Brillio’s implementation, the company managed to reduce the model development time from 7-8 months to 2 weeks, as well as lower the query execution time by 90%.