While growing into one of Europe’s largest banks, a leading multinational financial-services firm built a network of systems spanning disparate geographies and technologies. Gaining actionable intelligence from the system’s vast amount of disconnected data was critical to the bank’s ongoing efficient growth. With a presence in more than 65 countries, the firm planned to leverage the cloud and create strong data analytics, ETL, data quality, and data-visualization layer to better handle its prospects and end-to-end acquisition tracking while improving its enterprise intelligence. Adding a data science workbench to handle data discovery, preparation, and generation of predictive data-analysis models could also improve its sales-process efficiency.
Wipro collaborated with the bank to identify the various teams and data sources that would benefit from centralization. This analysis led Wipro to develop and deploy a Google Cloud-based solution using Google-native and other open-source AI/ML tools across teams throughout the bank. Using a centralized data reference hub, the bank could incorporate data from multiple legacy systems and multiple geographies while maintaining strict data-privacy and regulatory protocols.
These tools provided holistic insights into bank’s prospective customers thereby opening significant growth opportunities with minimum to near zero prospective customer drop offs due to lack of data.
The solution created a substantial improvement in the bank’s data quality foundation framework, improving the speed and agility with which it could handle large amounts of data by more than 50%. In addition, the bank’s data quality and security models received a boost through visualization and synthesis layers created using Google Cloud native tools. Data depicted through various dashboards resulted in valuable insights, improving efficiency when following up with leads and prospects. Enterprise intelligence and data utilization improved as well, along with a 20% improvement in cost savings per acquisition.