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A Decision Support Approach for Accounts Receivable Risk Management
Stockholm University, Faculty of Social Sciences, Stockholm Business School. University of Toronto, Canada.
2014 (English)In: IEEE Transactions on Systems, Man, and Cybernetics: Systems, ISSN 2168-2216, Vol. 44, no 12, 1624-1632 p.Article in journal (Refereed) Published
Abstract [en]

Financial disasters in private firms led to increased emphasis on various forms of risk management, to include market risk management, operational risk management, and credit risk management. Financial institutions are motivated by the need to meet increased regulatory requirements for risk measurement and capital reserves. This paper describes and demonstrates a model to support risk management of accounts receivable. We present a decision support model for a large bank enabling assessment of risk of default on the part of loan recipients. A credit scoring model is presented to assess account creditworthiness. Alternative methods of risk measurement for fault detection are compared, and a logistic regression model selected to analyze accounts receivable risk. Accuracy results of this model are presented, enabling accounts receivable managers to confidently apply statistical analysis through data mining to manage their risk.

Place, publisher, year, edition, pages
2014. Vol. 44, no 12, 1624-1632 p.
Keyword [en]
Accounts receivable, data mining, decision support system, enterprise risk management, risk measures
National Category
Business Administration Computer Systems
URN: urn:nbn:se:su:diva-111901DOI: 10.1109/TSMC.2014.2318020ISI: 000345627700006OAI: diva2:779754


Available from: 2015-01-13 Created: 2015-01-08 Last updated: 2015-01-13Bibliographically approved

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Wu, Desheng D.
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Stockholm Business School
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