Our suite guides banks, funds and asset managers in the transformation of the Non Performing Asset into cash via different strategies
• Most NPL workout strategies are focused on manpower rather than effectiveness and efficiency
• This generates highly inefficient workout units that need optimisation in economic downturns
• Banks and Distressed Funds outsource the workout of default loans to Servicers, who outsource it too to Debt Collection Agencies and Asset Managers
• This creates an inefficient value chain where responsibility is diluted in too many outsourcings, where middlemen add little value while charging fees
• Menhir’s task was to identify in advance which loans would generate payments in the short term, and design an effective and efficient asset management strategy,based on the output of the algorithm
• Loan data quality was very low, Asset Managers lacked the methodology to implement well designed processes and stick to them
• The customer held a +4bn€ NPA portfolio, with both NPL and REO. A small mistake could generate huge impact
• Payment anticipation model: Menhir created a Machine Learning model capable of identifying paying customers before any management was performed
• Allocation algorithm: Menhir designed a loan allocation algorithm to sort the assets between the available resources, to increase management intensity (with the same FTE), while reducing servicing costs
• As not all assets could undergo automatic allocation, Menhir segmented the AUM in two trenches:
• Assets with Payment Anticipation = 3,441.7 M€
• Assets with Payment Anticipation & Allocation Algorithm = 721.2 M€
• Monthly collection rate increased by 81%
• Collection cost reduced 41%