Post 3

Overview

• At banks we encounter the same problem as we found at Loan Servicers: Loan workout strategies are based on huge amounts of intuition work that generate a certain return
• Consumer loans have an increase complexity, as low tickets lead to high volumes
•  This high volume – low ticket combination makes granular action impossible for manual work based structures

The Challenge

• The bank approached Menhir to explore the potential application of the model deployed at the Real Estate Loan Servicer in their portfolio
• The portfolio was composed by personal loans, credit cards and overdrafts

The Approach

• Payment anticipation model: Menhir adapted it’s existing NPL platform capable of identifying paying customers before any management was performed
• Allocation algorithm: Menhir adapted it’s loan allocation algorithm to sort the assets between the available resources, to increase management intensity (with the same FTE), while reducing collection costs

The results

• For the whole portfolio: High score loans held a collection rate of 6.12%, while low score held a collection rate of 1.64%
• On new entries (+90DPD): High score loans held a 24.16% collection rate, while low score held a 10.95% collection rate