Bankruptcy Prediction and Credit Scoring: A Review of Recent Developments Based on Hybrid Systems and Some Related Patents
Javier de Andres, Pedro Lorca, Fernando Sanchez-Lasheras and Francisco Javier De Cos-Juez
Affiliation: University of Oviedo (Spain), Department of Accounting, Avda. del Cristo s/n, Oviedo 33006, Spain.
This paper reviews some recent developments from the field of Artificial Intelligence aimed at the determination of credit scores of firms and individuals. Specifically, the academic research efforts which are reviewed are those that consist of hybrid systems. These systems combine two or more intelligent techniques in several forms to derive the advantages of all of them. There are four ways to elaborate a hybrid system: first, by constructing a hybrid algorithm which tightly combines two or more single algorithms. Second, by using an ensemble, which is the combination of the results of a pool of models. Third, by using an algorithm to select the relevant variables and another one to perform the scoring. And finally, by clustering individuals and using this information for the scoring stage. Some patents have used these developments, but in our opinion only a small part of the research has been translated into patents and practical applications.
Keywords: Artificial intelligence, bankruptcy, classification, clustering, credit applications, credit risk, credit scoring, ensemble classifiers, financial distress, financial ratios, financial statements, hybrid systems, feature selection, hybrid models, solvency, statistical learning
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