Risk process management: a case study in a financial institution

Autores

DOI:

https://doi.org/10.15675/gepros.3009

Palavras-chave:

Financial Institution, Financial Risk, Process Management

Resumo

Purpose: The purpose of this study is to propose a new method for controlling the product "Financial Risk Management" in a multiple bank. The focus is on addressing Model Risk, particularly in the context of financial institutions, where reliance on incorrect statistical models can pose serious problems.

Theoretical framework: Model Risk is the possibility of losses due to models with flaws, deficiencies or inadequacies in the development, implementation or use process. Model Risk is considered one of the main problems for financial institutions. With the advancement of technology and the use of more robust models, dependence on incorrect statistical models can cause serious problems for institutions, increasing the need for studies in the area.

Methodology/Approach: To conduct the research, the institution's own survey was used, being applied to the analysis of the main financial models built in the last six months in the organization. As a result, the structure of this process was evaluated, action plans and control indicators were proposed, dividing them into the areas of: Data Governance, Systems, Data Quality and Reporting. With this categorization, it was also possible to create a Control Panel for each Area or Department, improving the management necessary to control Risk. 

Findings: The findings of the study involve the evaluation of the structure of the risk management process, with a particular focus on addressing Model Risk. The research proposes a new method for risk assessment within the context of "Financial Risk Management" in a multiple bank setting.

Research, practical & social implications: In terms of research implications, the study contributes by proposing a novel method for controlling financial risk in a banking environment. The practical implication is the potential improvement in risk management processes within financial institutions. Socially, this research may contribute to the overall stability and reliability of financial systems.

Originality/ Value: The originality of this study lies in its proposition of a new method for risk assessment within the domain of "Financial Risk Management" in a multiple bank setup. The value of the research is evident in addressing the critical issue of Model Risk, which can have substantial consequences for financial institutions.

Keywords: Financial Institution, Financial Risk, Process Management.

Referências

Andrade, R. P. (2011). A construção do conceito de incerteza: uma comparação das contribuições de Knight, Keynes, Shackle e Davidson. Nova Economia, 21(2), 171-195.

Arrieta, D. (2022). Model risk quantification based on relative entropy. Journal of Risk Model Validation, 16(3).

Berkowitz, J., & O’Brien, J. (2002). How accurate are value-at-risk models at commercial banks? Journal of Finance, 57(3), 977–987.

Brigs, P. (1998). Principles of international trade and payments. Blackwell Publishers.

Brinckmann, S., Liberali, G., & De Souza, R. (2005). Estudo de caso: Braskem: Implantação de uma gestão integrada de riscos. Revista Melhores Práticas.

Carvalho, A. S. (1994). Administrando risco de taxas de juros em instituições financeiras. Caderno de Estudos, 10.

Crawford, K., & Calo, R. (2022). There is a blind spot in AI research. Nature. https://www.nature.com/articles/d41586-022-00683-0

Derman, E. (1996). Model risk.

Doshi-Velez, F., & Kim, B. (2023). Towards a rigorous science of interpretable machine learning. arXiv Preprint. https://arxiv.org/abs/2304.04368

Garcia, M., & Didier, T. (2003). Taxa de juros, risco cambial e risco Brasil. Revista Pesquisa e Planejamento Econômico.

Gitman, L. J. (2004). Princípios de administração financeira. Pearson.

Gonçalves, J. E. (2000). As empresas são grandes coleções de processo. Revista de Administração de Empresas, 40(1), 6-19.

Greene, W. H. (2008). Econometric analysis (6th ed.).

Guasti Lima, F. (2016). Análise de riscos (2nd ed.). Atlas.

Hendricks, D. (1996). Evaluation of value-at-risk models using historical data. Federal Reserve Bank of New York Economic Policy Review, 2(1).

Jia, J., & Bradbury, M. E. (2020). Complying with best practice risk management committee guidance and performance. Journal of Contemporary Accounting & Economics, 16(3).

Jorion, P. (2003). Value at risk: A nova fonte de referência para a gestão do risco financeiro. Bolsa de Mercadorias & Futuros.

Junior, P. J., & Scucuglia, R. (2011). Mapeamento e gestão por processos – BPM (Business Process Management). M. Books.

Kato, T., & Yoshiba, T. (2000). Model risk and its control. Monetary and Economic Studies.

Klein Jr., V. H., & Reilley, J. T. (2021). The temporal dynamics of enterprise risk management. Critical Perspectives on Accounting.

Knight, F. H. (1921). Risk, uncertainty and profit. Houghton Mifflin Company.

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2023). Model cards for model reporting: Standardizing model reporting in NLP. In Proceedings of the 2023 Conference on Fairness, Accountability, and Transparency (FAccT '23), 311–324.

Mittelstadt, B. D., & Floridi, L. (2024). Why AI needs interpretability. AI & Society, 39(1), 17-24.

Montavon, G., Samek, W., & Müller, K. R. (2022). Explainable AI: Interpreting, explaining and visualizing deep learning. Springer.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2023). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.

OCDE. (2011). Avaliações da OCDE sobre governança pública: Avaliação da OCDE sobre o sistema de integridade da administração pública federal brasileira - Gerenciando riscos por uma administração pública mais íntegra.

Office of the Comptroller of the Currency, Federal Reserve. (2011). Supervisory guidance on model risk.

PMI. (2008). Um guia do conhecimento em gerenciamento de projetos (Guia PMBOK, 4th ed.). Project Management Institute.

Pradella, S., Furtado, J. C., & Kipper, L. M. (2012). Gestão de processos da teoria à prática – Aplicando a metodologia de simulação para a otimização do redesenho de processos. Atlas.

Rudin, C. (2022). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 4(1), 3-5.

Saunders, A. (2000). Administração de instituições financeiras. Atlas.

Selbst, A. D., & Barocas, S. (2023). The intuitive appeal of explainable machines. Fordham Law Review, 91(1), 101-148.

Shenhar, A. J., & Dvir, D. (1996). Toward a typological theory of project management. Research Policy, 25, 607-632.

Sibbertsen, P., Stahl, G., & Luedtke, C. (2008). Measuring model risk.

Smith, P. G., & Merritt, G. M. (2002). Proactive risk management: Controlling uncertainty in product development. Productivity Press.

Solomon, J. F., Solomon, A., & Norton, D. S. (2000). A conceptual framework for corporate risk disclosure emerging from the agenda for corporate governance reform.

Veale, M., & Binns, R. (2024). Fairness and accountability design needs better incentives. In Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society (AIES '24), 129-138.

Downloads

Publicado

2024-09-22

Como Citar

Martins Mallet, F., Christine Sotsek, N., & Frazon, A. (2024). Risk process management: a case study in a financial institution. Revista Gestão Da Produção Operações E Sistemas, 1. https://doi.org/10.15675/gepros.3009

Edição

Seção

Artigos