Journal article
Stacking-based modeling for improved over-indebtedness predictions

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Publication Details
Author list: Suleiman Ali Alsaif, Adel Hidri, and Minyar Sassi Hidri
Publisher: Inderscience
Publication year: 2022
Journal: International Journal of Computer Applications in Technology
Journal acronym: IJCAT
ISSN: 0952-8091
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eISSN: 1741-5047

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 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 the stacked generalization 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.

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Last updated on 2021-19-09 at 20:22