Stacking-based modelling for improved over-indebtedness predictions

In a world now starkly divided into pre- and post-COVID times, it's imperative to examine the impact of this public health crisis on the banking functions - particularly over-indebtedness risks. In this work, a flexible analytics-based model is proposed to improve the banking process of detecting customers who are likely to have difficulty in managing their debt. The proposed model assists the banks in improving their predictions. The proposed meta-model extracts information from existing data to determine patterns and to predict future outcomes and trends. We test and evaluate a large variety of Machine Learning Algorithms (MLAs) by using new techniques like feature selection. Moreover, models of previous months are combined in order to build a meta-model representing several months using stacked generalisation technique. The new model will identify 91% of the customers potentially unable to repay their debt six months ahead and enable the bank to implement targeted collections strategies.