With the wave of digitisation taking place in banks and specialist lenders across the African continent, so the requirement to automate decisions has become more important than ever. At the heart of automated decisions is the credit scorecard. Some lenders may have small portfolios and others have larger portfolios, but don’t have digitised data. Developing a customised data-driven scorecard is therefore not possible in these cases. Principa’s African Master Scorecard (PAMS) however, provides an excellent solution to all of this.
PAMS utilises Principa’s collective knowledge and expertise gained from building over 600 scorecards for clients representing more than 35 different countries across the African continent (and beyond), for 10 different financial products. We combine this knowledge and expertise to create a risk scorecard that is well suited at determining credit risk within your lending market.
Whilst PAMS is primarily applied in originations where it is used for credit vetting, PAMS can also be used for decision-making in the following credit lifecycle areas:
- Account management – this is a behavioural scorecard segment that calculates risk based on recent behaviour of your existing customers. These scorecards can be used for several actions including originating a repeat loan, cross-selling a new product, limit increases (for revolving products), top-ups, and IFRS9 provision PD segmentation.
- Retain – this scorecard segment can be used to predict the customers that are likely to leave you. Making proactive offers to these customers helps you retain the profitable accounts.
- Top-up – for term loans, here you can target good customers who are about to finish repaying their loan with a loan top-up (before your competitors get to them!)
- Collections – this scorecard segment indicates the likelihood of a customer missing his/her next payment which will assist you in determining who to prioritise first in collections.
The Principa African Master Scorecard is a tool suited for lenders across the African continent – assisting with the key process of automated risk assessment. For more information on how to deploy these models, please visit our DecisionSmart product page here, or read our FAQ here. To learn more about our African Master Scorecard please be in touch – email@example.com
Thomas has 18 years of experience in data science focusing on the EMEA region. His experience traverses multiple industries and disciplines covering analytics, consulting and software solutions for companies ranging from large banks and retailers to telcos and manufacturing operations. A large part of his experience comes from working with credit providers helping them harness the predictive power of data through the use of machine learning and decision tech. Thomas holds an MA in Mathematics from Edinburgh University.