The client is an American multinational investment bank and financial services company. In order to align with changing customer requirements, it had to enhance capabilities to ingest external data quickly for analytics, create reports on-the-fly, and improve data governance.
The client had issues with identifying new market opportunities and understanding risks as it did not have a comprehensive, 360 degree view of the customer. It also needed greater transparency in the quality of the consumed data to determine ‘fit for use’.
We, at Mphasis, addressed these concerns by developing a full-fledged solution that captured the 360-degree profile of the customer. This development not only enabled proficient management of risks, it also helped in identifying right market opportunities. Our team worked on proof of concepts (POCs) to rebuild the attribute generation engine to be more resilient and fault tolerant, while catering to big data analytics. We then built a data ingestion framework to transfer petabytes of data across Hadoop clusters securely. An automated data distribution platform from data lake into downstream systems was developed and secure data access and governance was provided. Our solution was created through the following steps:
• Designing architecture and engineering attribute generation for offer engines
• Developing data ingestion, processing, and consumption framework to consolidate data from multiple siloed system to enhance analytical capabilities
• Creating features to enable self-serve analytics from authoritative data sources
• Implementing data-specific validation checks to enable quality data for business
• Improving data governance to understand the lineage and meaning, thereby ensuring data is credible and reliable
Through our solutions, the client could clearly understand the risk profiles of its customers. It also gained benefits such as:
• Higher fees and interest from revenues for credit products based on the risk profile of a customer
• Improved time to market for new features developed for existing products
• Automated models that reduced manual assessments
• Shifting work to fully digital and workflow-based experiences, eliminating manual processes