Efficiency is key to the success of any debt collection operation. Banks and financial institutions cannot continue relying on conventional methods to stay competitive and keep up with changing market trends. With increasing proliferation of AI, banks now have the opportunity to stay relevant and keep up with dynamic business requirements. In fact, 75% of banks are in various stages of either evaluating or implementing AI strategies to optimize their business processes.
In our earlier thought papers – we had shared our position on how banks can improve collections without affecting customer experience, and how this can be achieved by making existing debt collection processes more intelligent with AI. Collections teams are now empowered with AI enabled risk segmentation, early prediction of delinquent accounts, suggested treatment plans based on risk segment which helps them improve efficiency and customer experience.
In this guide, we provide you a walk-through on how you can create and fulfill a powerful debt collection strategy with FinXEdge Collect (formerly CollectEdge). Here, you can understand
– How FinXEdge Collect’s flexible data model ingests data from multiple touchpoints across the application landscape
– Feature creation that encompasses credit score changes, macro-economic data and other trends for depth and context to assessment, behavioural features
– Selecting an AI model, and validating it after training and testing it
– And setting up automated product pipelines
Get started now, download this guide to begin your AI enabled debt collection journey.
Lenders are pursuing machine learning (ML) to grow recovery and reduce collections costs. Learn from experts the solution approaches and implementation best practices to maximize ROI
FinXEdge Collect (formerly CollectEdge) is a data-driven intelligence application powered by advanced Machine Learning that helps lenders and debt collectors reduce delinquency rates and charge-offs,
Collections teams are now empowered with AI enabled risk segmentation, early prediction of delinquent accounts, suggested treatment plans based on risk segment which helps them improve efficiency