Risk assessment when granting credit to non-financial legal entities

Autores

DOI:

https://doi.org/10.21680/2176-9036.2024v16n2ID36718

Palavras-chave:

Credit assessment for legal entities, default, credit score, rating score.

Resumo

Purpose: This study aimed to verify risk assessment procedures when granting credit by a legal entity via financing in negotiations with its customers.

Methodology: A documentary approach was used, emphasizing qualitative analysis, during the experimental study conducted at an XYZ organization linked to information technology.

Results: They point out that the credit score and rating score, established through the evaluation stages, taking into account the company's history, documentation, guarantees, financial health, cash flow, indebtedness, corporate governance, and market prospects, enable the risk classification of their analyzed clients, ranging from AAA for the "alpha" client to D for the "beta" client. This result made it possible to deduce that credit assessment is appropriate automation in financing decisions for commercial activities, capable of promoting financial flexibility and commercial agility. Customer risk classification is therefore seen as a fundamental tool for improving XYZ's financial decisions, minimizing default risks, and guaranteeing the financial security of its commercial operations, resulting from establishing risk scores for both defaulting and non-defaulting companies.

Contributions of the Study: This study makes a relevant theoretical contribution to the field of research by highlighting the importance of validating the criteria used in granting financing, as well as the contextual factors that can affect decisions. The practical contribution of analyzing a non-financial institution under the pandemic context and how they minimize their default by establishing credit analysis mechanisms provides improvements and consequently greater security as lenders.

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Biografia do Autor

Daniele Dias Cardoso, Federal University of Uberlândia (UFU).

Bachelor's Degree in Accounting. Federal University of Uberlândia (UFU).

Nilton Cesar Lima, Federal University of Uberlândia (UFU).

PhD in Business Administration. University of São Paulo (USP).

Referências

Acharya, V. V., & Steffen, S. (2020). The Risk of Being a Fallen Angel and the Corporate Dash for Cash in the Midst of COVID. The Review of Corporate Finance Studies, 9(3), 430-471. doi: 10.1093/rcfs/cfaa013

Agência Brasil. (2022). Brazil registers 1,023 new cases and 66 deaths from covid-19 in 24 hours. Retrieved May 14, 2023, from https://agenciabrasil.ebc.com.br/saude/noticia/2022-10/brasil-registra-5986-novos-casos-de-covid-19-em-24-horas

Akkoç, S. (2012). An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish credit card data. European Journal of Operational Research, 222(1), 168-178. doi: 10.1016/j.ejor.2012.04.009

Altman, E. I. (1968). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. doi: 10.1111/j.1540-6261.1968.tb00843.x

Annibal, C. A. Default in the Brazilian banking sector: an evaluation of its measures. Working Papers Series, 192. Brasília, DF: Central Bank of Brazil, 2009. Retrieved May 10, 2023, from https://ideas.repec.org/p/bcb/wpaper/192.html

BACEN - Central Bank of Brazil (2021). Monetary and Credit Statistics. Press release on 01/28/2021. Retrieved June 11, 2023, from https://www.bcb.gov.br/content/estatisticas/hist_estatisticasmonetariascredito/202101_Texto_de_estatisticas_monetarias_e_de_credito.pdf

BACEN - Central Bank of Brazil (2022). Monetary and Credit Statistics. Press release on 01/28/2022. Retrieved May 2, 2023, from https://www.bcb.gov.br/content/estatisticas/hist_estatisticasmonetariascredito/202201_Texto_de_estatisticas_monetarias_e_de_credito.pdf

Beck, T., & Keil, J. (2022). Have banks caught Corona? Effects of COVID on lending in the U.S. Journal of Corporate Finance, 72, 102160. doi: 10.1016/j.jcorpfin.2022.102160

Berger, A. N. et al. (2021). Is a friend in need a friend indeed? How relationship borrowers fare during the COVID-19 crisis, Working Papers 21-13, Federal Reserve Bank of Philadelphia.

Bianconi, M., Yoshino, J. A., & Machado de Sousa, M. O. (2013). BRIC and the U.S. financial crisis: An empirical investigation of stock and bond markets. Emerging Markets Review, 14, 76-109. doi: 10.1016/j.ememar.2012.11.002

Carvalho, K. W. et al. (2014). The importance of analyzing financial statements in granting credit. In: SEGET - XI Symposium on Excellence in Management and Technology, 2014, Resende/RJ. Proceedings of SEGET - XI Symposium on Excellence in Management and Technology.

CNC - National Confederation of Commerce (2022). Consumer debt and default survey. Brasília, DF. Retrieved June 11, 2023, from https://www.fecomercio.com.br/pesquisas/indice/peic

Dahooie, J. H., Hajiagha, S. H. R., Farazmehr, S., Zavadskas, E. K., & Antucheviciene, J. (2021). A novel dynamic credit risk evaluation method using data envelopment analysis with common weights and combination of multi-attribute decision-making methods. Computers & Operations Research, 129, 105223. doi: 10.1016/j.cor.2021.105223

Dekkers, R., de Boer, R., Gelsomino, L. M., de Goeij, C., Steeman, M., Zhou, Q., Sinclair, S., & Souter, V. (2020). Evaluating theoretical conceptualizations for supply chain and finance integration: A Scottish focus group. International Journal of Production Economics, 220, 107451. doi: 10.1016/j.ijpe.2019.07.024

Dinh, T. H. T., & Kleimeier, S. (2007). A credit scoring model for Vietnam's retail banking market. International Review of Financial Analysis, 16(5), 471-495. doi: 10.1016/j.irfa.2007.06.001

Dursun-de Neef, H. Ö., & Schandlbauer, A. (2022). COVID-19, bank deposits, and lending. Journal of Empirical Finance, 68, 20-33. doi: 10.1016/j.jempfin.2022.05.003

Gong, D., Jiang, T., & Lu, L. (2020). Pandemic and bank lending: Evidence from the 2009 H1N1 pandemic. Finance Research Letters, 39, 101627. doi: 10.1016/j.frl.2020.101627

Goodell, J. W. (2020). COVID-19 and finance: Agendas for future research. Finance Research Letters, 35, 101512. doi: 10.1016/j.frl.2020.101512

Gouvêa, M. A., Gonçalves, E. B., & Mantovani, D. M. N. (2015). Credit risk analysis with the application of logistic regression and neural networks. Contabilidade Vista & Revista, 24(4), 96-123.

Han, I., Chandler, J. S., & Liang, T.-P. (1996). The impact of measurement scale and correlation structure on classification performance of inductive learning and statistical methods. Expert Systems with Applications, 10(2), 209-221. doi: 10.1016/0957-4174(95)00047-x

Houaiss, A., & Villar, M. de S. (2001). Houaiss Dictionary of the Portuguese Language. Rio de Janeiro: Objetiva.

Kavussanos, M. G., & Tsouknidis, D. A. (2016). Default risk drivers in shipping bank loans. Transportation Research Part E: Logistics and Transportation Review, 94, 71-94. doi: 10.1016/j.tre.2016.07.008

Kozeny, V. (2015). Genetic algorithms for credit scoring: Alternative fitness function performance comparison. Expert Systems with Applications, 42(6), 2998-3004. doi: 10.1016/j.eswa.2014.11.028

Lee, T.-S., Chiu, C.-C., Chou, Y.-C., & Lu, C.-J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50(4), 1113-1130. doi: 10.1016/j.csda.2004.11.006

Li, L., Strahan, P. E., & Zhang, S. (2020). Banks as Lenders of First Resort: Evidence from the COVID-19 Crisis. The Review of Corporate Finance Studies, 9(3), 472-500. doi: 10.1093/rcfs/cfaa009

Neoway (2021). 5 Cs of credit: learn how this credit analysis is applied. Neoway Blog, 2021. Retrieved June 11, 2023, from https://blog.neoway.com.br/5-cs-do-credito-2

Norden, L., Mesquita, D., & Wang, W. (2021). COVID-19, policy interventions and credit: The Brazilian experience. Journal of Financial Intermediation, 48, 100933. doi: 10.1016/j.jfi.2021.100933

Park, C.-Y., & Shin, K. (2021). COVID-19, nonperforming loans, and cross-border bank lending. Journal of Banking & Finance, 106233. doi: 10.1016/j.jbankfin.2021.106233

Ponce, D. (2020). The impact of coronavirus in Brazil: politics and the pandemic. Nature Reviews Nephrology, 16(9), 483. doi: 10.1038/s41581-020-0327-0

Portal do Empreendedor (2022). I want to be an MEI. Retrieved June 19, 2023, from https://www.gov.br/empresas-e-negocios/pt-br/empreendedor

Ramos-Francia, M., & García-Verdú, S. (2022). Central Bank Response to COVID-19. Latin American Journal of Central Banking, 100065. doi: 10.1016/j.latcb.2022.100065

Receita Federal do Brasil (2023). Questions and Answers MEI and Simei [S. L.], 2023. 24 p. Retrieved June 19, 2023, from https://www8.receita.fazenda.gov.br/simplesnacional/arquivos/manual/perguntaomei.pdf

Santos, J. O. (2003). Análise de crédito - empresas e pessoas físicas. 2. ed. São Paulo: Atlas.

Santos, J. O. (2008). Comparative analysis of methods for predicting insolvency in a bank credit portfolio of medium-sized companies. REGE - Revista de Gestão USP, 15, 11-24.

Santos, J. O. d., & Famá, R. (2007). Evaluation of the applicability of a credit scoring model with systemic and non-systemic variables in revolving bank credit portfolios of individuals. Revista Contabilidade & Finanças, 18(44), 105-117. doi: 10.1590/s1519-70772007000200009

Schiozer, R., & Yoshida Jr., V. (2020). Flattening the default curve. GV Executivo, 19(3), 20. doi: 10.12660/gvexec.v19n3.2020.81727

Securato, J. R., & Famá, R. (1997). A procedure for credit decision by banks. Revista de Administração Contemporânea, 1(1), 101-119. doi: 10.1590/s1415-65551997000100006

Sehn, C.F., & Carlini Júnior, R. J. (2007). Default in the housing finance system: a study of the federal savings bank. Revista de Administração Mackenzie, 8(2), 2007.

SEBRAE - Brazilian Micro and Small Business Support Service (2022). Micro and Small Business Statistical Bulletin. São Paulo, 2022. Retrieved June 19, 2023, from www.sebrae.com.br

Serasa Experian. (2022a). Bad debt in Brazil falls for the first time in four years and ends 2020 with 61.4 million people, reveals Serasa Experian. 2022a. Retrieved June 19, 2023, from https://www.serasaexperian.com.br/sala-de-imprensa/noticias/inadimplência-no-brasil-caipela-primeira-vez-em-quatro-anos-e-encerra2020-com-614-milhões-de-pessoas-revelaserasa-experian

Serasa Experian. (2022b). Product manual: Credit Rating Serasa Experian. Retrieved June 19, 2023, from https://www.serasaexperian.com.br/solucoes/credit-rating

Silva, J. J. M., Carvalho Neto, W. J., & Souza, D. S. (2021). The analysis of financial statements as a parameter for granting credit. Caderno de Graduação-Ciências Humanas e Sociais-UNIT-Sergipe, 6(3), 85-98.

WHO - World Health Organization (2022). World Health Statistics: World Health Organization. Retrieved June 19, 2023, from https://www.who.int/data/gho/publications/world-healthstatistics

Zhang, Z., Gao, G., & Shi, Y. (2014). Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors. European Journal of Operational Research, 237(1), 335-348. doi: 10.1016/j.ejor.2014.01.044

Ҫolak, G., & Öztekin, Ö. (2021). The impact of COVID-19 pandemic on bank lending around the world. Journal of Banking & Finance, 106207. doi: 10.1016/j.jbankfin.2021.106207

Publicado

01-07-2024

Como Citar

CARDOSO, D. D.; LIMA, N. C. . Risk assessment when granting credit to non-financial legal entities . REVISTA AMBIENTE CONTÁBIL - Universidade Federal do Rio Grande do Norte - ISSN 2176-9036, [S. l.], v. 16, n. 2, 2024. DOI: 10.21680/2176-9036.2024v16n2ID36718. Disponível em: https://periodicos.ufrn.br/ambiente/article/view/36718. Acesso em: 16 jul. 2024.

Edição

Seção

Seção 7: Internacional (S7)