Predicting Clearing Date of Account Receivables with Focus on Total Amount Paid
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F23%3A10254354" target="_blank" >RIV/61989100:27510/23:10254354 - isvavai.cz</a>
Result on the web
<a href="https://www.ekf.vsb.cz/smsis/en/" target="_blank" >https://www.ekf.vsb.cz/smsis/en/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Predicting Clearing Date of Account Receivables with Focus on Total Amount Paid
Original language description
The ability to predict the inflow and outflow of the money is crucial for many companies across various industries. Because each company has at least some control to choose when it will pay the debts (account payables), the tougher part is to predict the customer's behavior, so to predict when the company will receive the money due (account receivables). This study used 40,000 account receivables from year 2019 to train a Random Forest Regression model and 6,000 invoices from year 2020 to test the performance of the model. Sample weight parameter and also custom scoring function were tested to emphasize invoices based on the amount due. The best model with sample weight parameter was able to predict clearing date with mean absolute error of 3 days. The predictions were also aggregated to get weekly sums of amounts predicted to receive and amounts actually received. With the average percentage difference being 5.5 % it was proven that machine learning is able to accurately support financial experts managing cash flow.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
50200 - Economics and Business
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 15th International Conference on Strategic Management and its Support by Information Systems 2023: May 22-24, 2023, Ostrava, Czech Republic
ISBN
978-80-248-4687-3
ISSN
2570-5776
e-ISSN
2570-5776
Number of pages
10
Pages from-to
226-235
Publisher name
VŠB - Technical University of Ostrava
Place of publication
Ostrava
Event location
Ostrava
Event date
May 22, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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