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Using AI & ML to Personalize Payment Plans and Improve Collections Success
Learn how collections teams are using machine learning models and artificial intelligence to personalize collections and meet the demands of an evolving lending landscape.
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About the whitepaper
This whitepaper explores how AI and machine learning can help lenders prevent delinquencies and improve collections recovery rates through personalized payment plans. It covers industry challenges and explains how personalization benefits borrowers and lenders alike.
Below are some of the topics we will cover:
- Rising costs and delinquencies are forcing lenders to optimize collections processes
- Current collections strategies lead to NSF and overdraft fees
- Our data shows that the current state of collections doesn’t consider the borrower’s affordability
- Collections teams have seen success with using ML to improve collections
In this paper, we'll review an analysis of our sample dataset demonstrating that aligning a borrower’s loan payment schedule around their payroll has a huge impact on collections success.
Download this whitepaper to learn:
How collections teams are using cashflow analytics to personalize collections to:
- Prevent late or returned payments
- Identify signals of affordability
- Reduce NSFs and overdraft fees
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Success Story
A large consumer lender was looking to reduce defaults for new and existing users. Using Pave's Income Detection and Paycheck Prediction Model, they improved collections success by accurately detecting and predicting users' paydays to align payments accordingly.
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