Measuring the creditworthiness of customers and debtors’ portfolios, as well as the expected value of the doubtful debts is of outmost importance. Current credit limit assignments, collection processes and marketing actions do not consider the actual customer credit risk.
Development of a collections analytics and reporting solution that utilizes ML in combination with standard econometric models for proactively assessing the credit risk of its existing and prospect customers and the expected losses. Design strategies for the end-to-end management of collections, debt recoveries and its timings and product recommendations based on expected credit risk and losses.
An analytics solution that utilizes ML algorithms for credit risk assessment, customer segmentation and product recommendations based on customer credit risk and payment/usage behavior.
Deficient Design Images Detection Model
DESCRIPTION WHAT WE HAVE DONE/ACHIEVED
Optimization of collections (debt/loss minimization) and of marketing opportunities (increased revenues)