A Mathematical Model for Predicting Debt Repayment: A Technical Note

Abstract

Debt collection is a massive industry, within the USA alone more than $50 billion recovered each year. However the information available is often limited and incomplete, and predicting whether a given debtor would repay is inherently a challenging task. This has amplified research on debt recovery classification and prediction models of late. This report considers three main mathematical, data mining and statistical models in debt recovery classification, in logistic regression, artificial neural networks and affinity analysis. It also compares the effectiveness of the above-mentioned tools in evaluating whether a debt is likely to be repaid. The construction and analysis of the models were based on a fairly large unbalanced data sample provided by a debt collection agency. We have shown that all three models could classify the debt repayments with a considerable accuracy, if the assumptions of the models are satisfied.

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“…Le's findings should be important lessons for other developing countries where governments often face a dilemma in increasing charter capital requirements due to various political and social pressures. Wijewardhana (2018) experimented with the effectiveness of mathematical, data mining and statistical models in logistic regression artificial neural networks and market-based analysis to predict debt recovery based on large unbalanced data sample collected from debt collection agency. Wijewardhana has provided evidence that all three models could predict the repayments with considerable accuracy.…”