The client is an Indian financial services company. Out of different kind of loans, the volume of their SME loans is very low such that developing machine learning models in a traditional  way is not possible.

• Client wanted to develop a quantitative scorecard to underwrite their SME portfolios.
• Leverage different techniques that increases the stability and generalizability of the models.

• Detailed analysis of target definitions along with reject-inferencing to mimic the Eventual-NPA.
• Use of cross-validation with k=3 technique for robust model assessment.
• Use of data augmentation technique to synthetically create both majority and minority classes of cross-validated training datasets to increase the stability of models.
• Leverage random-forest’s variable importance to get best variables for the scorecard.

Given the data with poor quality & quantity, the performance on the client’s out-of-time validation datasets are much closer to the performance during the model development process.