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Supercharging Your Collections With DecisionSmart

September 9, 2019 at 8:18 AM

Collections Cascade_Principa

Collection departments utilise diallers and collections management systems to improve their collections by segmenting the delinquent customers, prioritising them and applying a host of treatments.  Whilst segmentation takes you so far, there are a host of other mathematical models that can be explored to improve what we call the “Collections Cascade”.  Improvement in any step of the cascade can help improve the collections yield and there are a number of models that can be used.  A few of them are listed below:

Icons list

Where to start

If your collections department doesn’t use scores, a reasonable question might be, “where do I start?”  Typically, this would be with pure collections scores such as “Probability of missing next payment” (also known as roll-cards) and “payment projection scorecard” for late stage collections (cycle delinquent ≥ 3months).  Pre-delinquency is also a hot topic with some collection departments opting for a straight forward SMS while others offering a monthly lucky draw for a car if you make your monthly payment!  Most recently in South Africa, connectivity has been a regular problem that collection managers have raised.  That is where the probability of contact and right-time-to-call models are useful. 

Deployment

At Principa, when we look at any analytical solution we have adopted a “3-D approach– determine, develop, deploy. Determine the business problem and data; develop the model; then deploy it.  We consider all three when we start a project.  A model is useless unless it can be deployed. For collection scoring Principa utilise DecisionSmart to deploy our collections models. 

DecisionSmart is a business rules management system that allows the user to deploy complex scorecards and collections strategies.

DecisionSmart Dashboard

These can be fed directly into a collections management system or dialler.  Principa have also integrated DecisionSmart with CollectSmart so that scores are calculated directly from the loans processing system and sent directly into CollectSmart for smart segmentation. If you have adopted machine learning, we can even use DecisionSmart to call a Python or R machine learning model through a SQL stored procedure.  You can find out more about DecisionSmart on these useful pages:

  1. DecisionSmart FAQ
  2. Our DecisionSmart video
  3. Mastering a Business Rules Management System

To find out more or to organise a demo of DecisionSmart, please contact us here.

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Thomas Maydon
Thomas Maydon
Thomas Maydon is the Head of Credit Solutions at Principa. With over 13 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.

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Supercharging Your Collections With DecisionSmart

Collection departments utilise diallers and collections management systems to improve their collections by segmenting the delinquent customers, prioritising them and applying a host of treatments.  Whilst segmentation takes you so far, there are a host of other mathematical models that can be explored to improve what we call the “Collections Cascade”.  Improvement in any step of the cascade can help improve the collections yield and there are a number of models that can be used.  A few of them are listed below:

Frequently Asked Questions On CollectSmart

What is CollectSmart? CollectSmart is a powerful, modular, enterprise-wide debt collection management system that combines business controlled segmentation with the allocation of appropriate and differentiated actions to individual customer profiles - all accessed via an easy-to-use web interface.