A major financial institution sought to increase the efficiency of retail banking promotions for customers across the world.
Challenge
The bank lacked adequate user behavioral data to predict credit card promotions to users geographically using their service around the world.
Privacy concerns and sovereignty regulations inhibited the centralization of PII.
Decreased accuracy in financial projections due to a lack of purchase history and user activity data.
Solution
Devron deployed algorithms at the user level to preserve the privacy of underlying user data.
Devron trained highly efficient promotional recommendation algorithm to recommend restaurants, events, and credit cards to optimize user purchase behavior.
Decentralized model training resulted in a 30% increase in customer onboarding and retention from increased service adoption and better product recommendations.
