Identifying Self-healing Contracts with Machine Learning: Analyzing the Accuracy and Disaggregation of 30 to 90 Day Debts

Subject Areas : Management

Mahboob Sadeghi ¹ , Ali Saeedi ² , Alireza Heidarzadeh Hanzaei ³

1 - Department of Management, NT.C., Islamic Azad University, Tehran, Iran
2 - Department of Management, NT.C., Islamic Azad University, Tehran, Iran
3 - Department of Management, North Tehran Branch, Islamic Azad University, Tehran, Iran

Received: 2025-03-25 Accepted : 2025-09-06 Published : 2025-09-12

Keywords: Non-performing Loan Collection Forecasting, Explainable Artificial Intelligence, Machine Learning, SHAP, Feature Analysis. ,

Abstract :

Forecasting the collection of non-current receivables is one of the key challenges in the financial management of financial and credit institutions. This issue not only affects the financial stability and soundness of banks, but also directly affects their ability to manage risk and determine effective credit strategies. The present study uses artificial intelligence-based methods to provide a forecasting model to determine the probability of collection of non-current receivables in contracts with debt due between 30 and 90 days. In this study, machine learning algorithms, including decision trees, random forests, and model clarification analyses, especially SHAP (SHapley Additive exPlanations), are used to analyze financial data and predict the status of receivables collection. The results of the analysis show that machine learning models are able to distinguish and isolate self-healing contracts from other contracts with considerable accuracy in the future. The findings show that machine learning models have a high power in distinguishing self-healing contracts from other cases. The SHAP tool has also played a key role in analyzing the features that affect the prediction. This approach can be effectively used in improving banks' credit risk management solutions.

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