Behaviour-based short-term invoice probability of default evaluation.
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| Title: | Behaviour-based short-term invoice probability of default evaluation. |
|---|---|
| Authors: | Perko, Igor1 Igor.Perko@uni-mb.si |
| Source: | European Journal of Operational Research. Mar2017, Vol. 257 Issue 3, p1045-1054. 10p. |
| Subjects: | Financial management, Liquidity (Economics), Invoices, Behavioral assessment, Credit risk, Probability theory |
| Abstract: | In this paper, the effect of behavioural analytics on short-term default predictions at the invoice level is addressed by answering a question that slightly diverges from the traditional probability of default definition: ‘What is the probability that this invoice will be paid within the next 30 days?’ Resultantly improving short-term liquidity planning accuracy and supporting financial management in companies. To provide a valid answer to the research question, a set of issues needs to be resolved, including identifying an appropriate data set, increasing the data predictive power, and creating and testing predictive models. Since the appropriate data set is not yet presented, we primarily focus on the first two issues: identifying appropriate data and raising its predictive power. In this paper, we propose to build predictive models upon a new data source from multiple companies, acquired by business partners' data sharing concept. Furthermore, we upgrade these data with behavioural analysis to test the assumption that the probability of default depends not only on payment capability but also on payment preparedness. The predictive power of shared invoice data and the effects of behavioural analysis are tested in a two-phase experiment: first, basic shared data are used to predict short-term invoice defaults, and in the second phase, the behavioural analysis results are included in the dataset. Lastly, the predictive models’ test results are compared. Both results are positive: the already high accuracy of models, build upon basic data is significantly upgraded in models, using the behaviour analysis extended data set. [ABSTRACT FROM AUTHOR] |
| Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 119441131 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Behaviour-based short-term invoice probability of default evaluation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Perko%2C+Igor%22">Perko, Igor</searchLink><relatesTo>1</relatesTo><i> Igor.Perko@uni-mb.si</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Operational+Research%22">European Journal of Operational Research</searchLink>. Mar2017, Vol. 257 Issue 3, p1045-1054. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Financial+management%22">Financial management</searchLink><br /><searchLink fieldCode="DE" term="%22Liquidity+%28Economics%29%22">Liquidity (Economics)</searchLink><br /><searchLink fieldCode="DE" term="%22Invoices%22">Invoices</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Credit+risk%22">Credit risk</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this paper, the effect of behavioural analytics on short-term default predictions at the invoice level is addressed by answering a question that slightly diverges from the traditional probability of default definition: ‘What is the probability that this invoice will be paid within the next 30 days?’ Resultantly improving short-term liquidity planning accuracy and supporting financial management in companies. To provide a valid answer to the research question, a set of issues needs to be resolved, including identifying an appropriate data set, increasing the data predictive power, and creating and testing predictive models. Since the appropriate data set is not yet presented, we primarily focus on the first two issues: identifying appropriate data and raising its predictive power. In this paper, we propose to build predictive models upon a new data source from multiple companies, acquired by business partners' data sharing concept. Furthermore, we upgrade these data with behavioural analysis to test the assumption that the probability of default depends not only on payment capability but also on payment preparedness. The predictive power of shared invoice data and the effects of behavioural analysis are tested in a two-phase experiment: first, basic shared data are used to predict short-term invoice defaults, and in the second phase, the behavioural analysis results are included in the dataset. Lastly, the predictive models’ test results are compared. Both results are positive: the already high accuracy of models, build upon basic data is significantly upgraded in models, using the behaviour analysis extended data set. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Journal of Operational Research is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.ejor.2016.08.039 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1045 Subjects: – SubjectFull: Financial management Type: general – SubjectFull: Liquidity (Economics) Type: general – SubjectFull: Invoices Type: general – SubjectFull: Behavioral assessment Type: general – SubjectFull: Credit risk Type: general – SubjectFull: Probability theory Type: general Titles: – TitleFull: Behaviour-based short-term invoice probability of default evaluation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Perko, Igor IsPartOfRelationships: – BibEntity: Dates: – D: 16 M: 03 Text: Mar2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 03772217 Numbering: – Type: volume Value: 257 – Type: issue Value: 3 Titles: – TitleFull: European Journal of Operational Research Type: main |
| ResultId | 1 |
