The COVID pandemic constitutes a Black Swan event, of which the economic consequences have only started to unfold. According to a McKinsey report, the sudden and simultaneous depression in supply and demand resulted in typical credit-risk data becoming “obsolete overnight”. Traditional 6 and 12 month old data were no longer suitable for identifying the resilience of individual borrowers. During the latter half of 2020, Matogen Applied Insights (MAI) joined forces with a commercial lending company to conduct internal risk modelling for approving loans for new and existing clients.
Due to a shortage of behavioural data, demographic metrics were a significant input into filling information gaps. In collaboration with the client, the MAI data scientist created variables which proved to be innovative business metrics in that they were unrelated to other variables and provided significant lift. Logistic regression was applied to the augmented dataset and a model was constructed using credit bureau modelling techniques, facilitating the credit approval process.
Commercial lending risk modelling
During the second phase of the project, the MAI data scientist added macro-economic data using the one-factor Merton model to adapt the credit scorecard for the pandemic-and-beyond trading environment. Linear regression was performed with macroeconomic indicators, including interest rates, unemployment figures, inflation indices, GDP and exchange rates. Model performance was checked with cross-validation and used to establish the solvency of clients. The portfolio’s time variation or systematic risk could then be explained and forecasted, allowing better business decisions.
Monitoring and machine learning segmentation
MAI delivered a model that predicts the likelihood of default, given current macro economic conditions. Model performance continues to be monitored and has been found to be stable. Significantly, the model has resulted in an increase in loan approvals, despite exceedingly challenging market circumstances.
Currently, segmentation of the client base is being explored using a series of decision trees, seeking an improvement of the credit approval process.