Expanding Banking Loan Base
Banks desire to expand their customer loan base to drive revenue, manage risk, expand services per customer, and maintain competitiveness. As a result, banks are turning to AI to better segment their customers for new and cross-selling opportunities and to improve their overall customer acquisition process.
Challenge
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.
Solution
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.
Outcomes
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.
View Additional Use Cases
Reducing Customer Churn
Build a high-performing customer churn model to improve customer loyalty, realize sustainable revenue growth, and optimize business processes.
