Superior customer service with robust risk management through reengineered data flows and processes for a global payments major
Efficient and effective customer proﬁling for risk management is a determinant of business success in the global payments industry. Accurate proﬁling ensures that enterprises can align exposure to individual customers based on expected revenue and risks.
Here is how Mindtree helped a leader in global payments leverage customer data spread across disparate systems to improve the service extended to high- spending customers while enhancing risk management.
Information on the customer's high-spending global customer base was fed into a highly sophisticated proﬁling framework for optimized service and risk management. However, the framework was unable to effectively guide decision making as it lacked adequate customer data, which resided in multiple systems. In addition, inefficient data-related processes further complicated their operations. The payments major approached Mindtree to help with:
- Utilizing information from multiple systems for better decision making on high spend customers
- Streamlining risk and under-writing processes
Mindtree collaborated with the customer to redesign its interfaces and overhaul data ﬂows so that the customer proﬁle was of a consistently high quality. We also worked together to make the process of risk proﬁling more efficient. Solution highlights included:
- Establishing a link between key interface systems to leverage customer risk-related data
- Streamlining the way data was stored in the system and centralizing the database for a consistent customer proﬁle
- Enhancing services by streamlining the conversion process
- Reengineering processes to eliminate redundancies
- Developing an automated tool to generate an implementation plan with minimal effort and reduced chance of human error
- Projected savings of USD 950,000 from more effective operations
- Improved customer proﬁling capability for better service and risk management
- Annual savings of USD 300,000 through reduced computing costs