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http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3683Повний запис метаданих
| Поле DC | Значення | Мова |
|---|---|---|
| dc.contributor.author | Yaroshchuk, Svitlana O. | - |
| dc.contributor.author | Shapovalova, Nonna N. | - |
| dc.contributor.author | Striuk, Andrii M. | - |
| dc.contributor.author | Rybalchenko, Olena H. | - |
| dc.contributor.author | Dotsenko, Iryna O. | - |
| dc.contributor.author | Bilashenko, Svitlana V. | - |
| dc.date.accessioned | 2020-02-18T12:22:46Z | - |
| dc.date.available | 2020-02-18T12:22:46Z | - |
| dc.date.issued | 2020-02-09 | - |
| dc.identifier.citation | Yaroshchuk S. O. Credit scoring model for microfinance organizations [Electronic resource] / Svitlana O. Yaroshchuk, Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, Svitlana V. Bilashenko // Computer Science & Software Engineering : Proceedings of the 2nd Student Workshop (CS&SE@SW 2019), Kryvyi Rih, Ukraine, November 29, 2019 / Edited by : Arnold E. Kiv, Serhiy O. Semerikov, Vladimir N. Soloviev, Andrii M. Striuk. – P. 115-127. – (CEUR Workshop Proceedings (CEUR-WS.org), Vol. 2546). – Access mode : http://ceur-ws.org/Vol-2546/paper07.pdf | uk_UA |
| dc.identifier.issn | 1613-0073 | - |
| dc.identifier.uri | http://ceur-ws.org/Vol-2546/paper07.pdf | - |
| dc.identifier.uri | http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3683 | - |
| dc.identifier.uri | https://doi.org/10.31812/123456789/3683 | - |
| dc.description | 1. Aleshin, V.A, Rudayeva, O.O.: Kreditnyj skoring kak instrument povysheniya kachestva bankovskogo risk-menedzhmenta v sovremennyh usloviyah (Credit scoring as an instrument for improving the quality of banking risk management in current conditions). Terra economicus. 10(2), 27–30 (2012) 2. Allison, P.D. (ed.): Logistic regression using the SAS system: theory and application. SAS Institute, Stanford (2012) 3. Anderson, R.: The credit scoring toolkit: theory and practice for retail credit risk management and decision automation. Oxford University Press, New York (2007) 4. Coelho, L.P., Richert, W.: Building Machine Learning Systems with Python. Packt Publishing, Birmingham (2013) 5. Flach, P.: Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, Cambridge (2012) 6. Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Pearson, New Jersey (2008) 7. Kiv, A., Semerikov, S., Soloviev, V., Kibalnyk, L., Danylchuk, H., Matviychuk, A.: Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics. In: Kiv, A., Semerikov, S., Soloviev, V., Kibalnyk, L., Danylchuk, H., Matviychuk, A. (eds.) Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics, Proceedings of the Selected Papers of the 8th International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2 2019), Odessa, Ukraine, May 22-24, 2019. CEUR Workshop Proceedings 2422, 1–4. http://ceurws.org/Vol-2422/paper00.pdf (2019). Accessed 17 Aug 2019 8. Lewis, E.M. An introduction to credit scoring. Athena Press, London (1992) 9. Luo, F.L., Unbehauen, R.: Applied Neural Networks for Signal Processing. Cambridge University Press, Cambridge (1997) 10. Mays, E. (ed.): Handbook of credit scoring. Global Professional Publishing, Chicago (2001) 11. Rojas, R.: Neural Networks: A Systematic Introduction. Spring-Verlag, Berlin (1996) 12. Saternos, C.: Client-Server Web Apps with JavaScript and Java. O’Reilly Media, Sebastopol (2014) 13. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Markova, O.M., Soloviev, V.N., Kiv, A.E.: Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. In: Ermolayev, V., Mallet, F., Yakovyna, V., Kharchenko, V., Kobets, V., Korniłowicz, A., Kravtsov, H., Nikitchenko, M., Semerikov, S., Spivakovsky, A. (eds.) Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer (ICTERI, 2019), Kherson, Ukraine, June 12-15 2019, vol. II: Workshops. CEUR Workshop Proceedings 2393, 833–848. http://ceur-ws.org/Vol-2393/paper_348.pdf (2019). Accessed 30 Jun 2019 14. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Kiv, A.E.: Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. In: Kiv, A.E., Soloviev, V.N. (eds.) Proceedings of the 1st International Workshop on Augmented Reality in Education (AREdu 2018), Kryvyi Rih, Ukraine, October 2, 2018. CEUR Workshop Proceedings 2257, 122–147. http://ceur-ws.org/Vol-2257/paper14.pdf (2018). Accessed 30 Nov 2018 15. Siddiqi, N.: Credit risk scorecard: developing and implementing credit scoring. John Wiley and Sons, New Jersey (2006) 16. Sorokin, A.S.: K voprosu validacii modeli logisticheskoj regressii v kreditnom skoringe (On the validation of the logistic regression model in credit scoring). Naukovedenie 2. http://naukovedenie.ru/PDF/173EVN214.pdf (2014). Accessed 10 Nov 2019 17. Sorokin, A.S.: Postroenie skoringovyh kart s ispolzovaniem modeli logisticheskoj regressii (Construction of scoring maps using a logistic regression model). Naukovedenie 2. http://naukovedenie.ru/PDF/180EVN214.pdf (2014). Accessed 10 Nov 2019 18. Sorokin, S.V., Sorokin, A.S.: Ispolzovanie nejrosetevyh modelej v povedencheskom skoringe (Use of neural network models in behavioral scoring). Prikladnaja informatika 10(2(56)), 92–109 (2015) | - |
| dc.description.abstract | The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used research methods such as analysis of the literature on banking scoring; artificial intelligence methods for scoring; modeling of scoring estimation algorithm using neural networks, empirical method for determining the optimal parameters of the training model; method of object-oriented design and programming. The result of the work is a neural network scoring model with high accuracy of calculations, an implemented system of automatic customer lending. | uk_UA |
| dc.language.iso | en | uk_UA |
| dc.publisher | Arnold E. Kiv, Serhiy O. Semerikov, Vladimir N. Soloviev, Andrii M. Striuk | uk_UA |
| dc.subject | neural network | uk_UA |
| dc.subject | machine learning | uk_UA |
| dc.subject | lending | uk_UA |
| dc.subject | scoring | uk_UA |
| dc.title | Credit scoring model for microfinance organizations | uk_UA |
| dc.type | Article | uk_UA |
| Розташовується у зібраннях: | Кафедра інформатики та прикладної математики | |
Файли цього матеріалу:
| Файл | Опис | Розмір | Формат | |
|---|---|---|---|---|
| paper07.pdf | article | 1.04 MB | Adobe PDF | Переглянути/Відкрити |
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