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Behaviour-based short-term invoice probability of default evaluation. (English) Zbl 1395.91480
Summary: 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, built upon basic data is significantly upgraded in models using the behaviour analysis extended data set.
MSC:
91G40 Credit risk
91G70 Statistical methods; risk measures
91B38 Production theory, theory of the firm
Software:
Orange
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