To effectively segment customers and target prospective customers with effective offers, banks must gather data from multiple systems, including online banking, core banking, loan origination and processing, marketing, CRM, etc.
However, moving and combining these datasets to build a high-performing AI model is impossible as they contain sensitive PII and are subject to strict regulatory and compliance standards.
Devron’s federated data science platform overcomes these challenges by allowing teams to get tarted quickly with the data they have and build and train models using these distributed and private datasets where they reside. It can be easily deployed across divisions, departments, systems, cloud providers, and jurisdictions without the cost and risk of data movement.
By harnessing multiple previously inaccessible datasets, Devron can deliver a model to help banks better segment their customers and target the most suitable loan product and offer for each customer.
This predictive AI model enables banks to optimize their marketing campaigns to increase wallet share, improve personalization, boost customer satisfaction, and reduce acquisition costs.
Similar federated models we've built have shown double-digit percentage improvements in precision compared to models built on only one of the bank’s datasets.