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 CollectEdge. Here, you can understand
– How CollectEdge’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.
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
According to recent reports from the Federal Reserve Bank of New York credit card, household debt and auto-loans delinquency rates have been slowly rising, hitting a 7 year high recently.
Artificial Intelligence has reached a seminal state and is being extensively used to underwrite the loans based on alternate data sources like telephone records and social footprint for the underbanke