The Front-End’s Lending Decision System for the Agricultural Bank in Thailand
Author
Abstract
Suggested Citation
DOI: 10.22004/ag.econ.334389
Download full text from publisher
References listed on IDEAS
- Wu, Chunchi & Wang, Xu-Ming, 2000. "A Neural Network Approach for Analyzing Small Business Lending Decisions," Review of Quantitative Finance and Accounting, Springer, vol. 15(3), pages 259-276, November.
- Songkran Somboon, 2015. "Credit Scoring System for Managing Risk in Agricultural Loan Portfolio of the Thai Rural Financial Market," Applied Economics Journal, Kasetsart University, Faculty of Economics, Center for Applied Economic Research, vol. 22(1), pages 27-50, June.
- Barney, Douglas K. & Finley Graves, O. & Johnson, John D., 1999. "The farmers home administration and farm debt failure prediction," Journal of Accounting and Public Policy, Elsevier, vol. 18(2), pages 99-139.
- Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
- Calum G. Turvey & Alfons Weersink, 1997. "Credit Risk and the Demand for Agricultural Loans," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 45(3), pages 201-217, November.
- James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Francesco Ciampi & Alessandro Giannozzi & Giacomo Marzi & Edward I. Altman, 2021. "Rethinking SME default prediction: a systematic literature review and future perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 2141-2188, March.
- Calabrese, G.G. & Falavigna, G. & Ippoliti, R., 2024. "Financial constraints prediction to lead socio-economic development: An application of neural networks to the Italian market," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
- Catherine Refait-Alexandre, 2004. "A Review of Business Failure Prediction Based on Financial Analysis of the Firm [La prévision de la faillite fondée sur l'analyse financière de l'entreprise : un état des lieux]," Post-Print hal-01391654, HAL.
- Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
- Catherine Refait-Alexandre, 2004.
"La prévision de la faillite fondée sur l'analyse financière de l'entreprise : un état des lieux,"
Economie & Prévision, La Documentation Française, vol. 162(1), pages 129-147.
- Catherine Refait, 2004. "La prévision de la faillite fondée sur l’analyse financière de l’entreprise : un état des lieux," Économie et Prévision, Programme National Persée, vol. 162(1), pages 129-147.
- BATRANCEA Ioan & BATRANCEA Larissa & STOIA Ioan, 2013. "Statistical Study On The Risk Of Bankruptcy In Bank," Revista Economica, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 65(5), pages 18-30.
- Matthew Smith & Francisco Alvarez, 2022. "Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 263-295, January.
- Bello Musa Zango & Sanni Mohammed Lekan & Mohammed Jibrin Katun, 2020. "Conventional Methods in Housing Market Analysis: A Review of Literature," Baltic Journal of Real Estate Economics and Construction Management, Paradigm, vol. 8(1), pages 227-241, January.
- Wenshuai Wu, 2022. "Credit Risk Measurement, Decision Analysis, Transformation and Upgrading for Financial Big Data," Complexity, John Wiley & Sons, vol. 2022(1).
- Ali Namaki & Reza Eyvazloo & Shahin Ramtinnia, 2023. "A systematic review of early warning systems in finance," Papers 2310.00490, arXiv.org.
- Harlan Platt & Marjorie Platt, 2002. "Predicting corporate financial distress: Reflections on choice-based sample bias," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 26(2), pages 184-199, June.
- Arundina, Tika & Azmi Omar, Mohd. & Kartiwi, Mira, 2015. "The predictive accuracy of Sukuk ratings; Multinomial Logistic and Neural Network inferences," Pacific-Basin Finance Journal, Elsevier, vol. 34(C), pages 273-292.
- Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
- Katsuyuki Tanaka & Takuo Higashide & Takuji Kinkyo & Shigeyuki Hamori, 2025. "A Multi-Stage Financial Distress Early Warning System: Analyzing Corporate Insolvency with Random Forest," JRFM, MDPI, vol. 18(4), pages 1-16, April.
- Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.
- Bárbara Díaz & Antonio Morillas, 2005. "Minería de datos y lógica difusa.Una aplicación al estudio de la rentabilidad económica de las empresas agroalimentarias en Andalucía," GE, Growth, Math methods 0512003, University Library of Munich, Germany.
- du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
- Haider A. Khan, 2002. "Can Banks Learn to Be Rational?," CIRJE F-Series CIRJE-F-151, CIRJE, Faculty of Economics, University of Tokyo.
- Bertrand Hassani & Xin Zhao, 2014. "Reconsidering Corporate Ratings," Post-Print hal-01117683, HAL.
- Sarbjit Singh Oberoi & Sayan Banerjee, 2023. "Bankruptcy Prediction of Indian Banks Using Advanced Analytics," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 22-41.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:thkase:334389. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/darkuth.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.
Printed from https://ideas.repec.org/a/ags/thkase/334389.html