Extração de regras de classificação a partir de redes neurais para auxílio à tomada de decisão na concessão de crédito bancário

Steiner, María Teresinha Arns; Nievola, Júlio César; Soma, Nei Yoshihiro; Shimizu, Tamio; Neto, Pedro José Steiner

doi:10.1590/s0101-74382007000300002

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…Credit risk analysis is an active research area in financial risk management, and credit scoring is one of the key analytical techniques in credit risk evaluation (Yu, Wang, and Lai, 2009;Steiner, Nievola, Soma, Shimizu, and Steiner Neto, 2007). With the fast development of financial products and services, bank credit departments have collected large amounts of data, which risk analysts use to build appropriate credit risk models to accurately evaluate an applicant's credit risk (Zhang, Gao, and Shi, 2014).…”

Section: Introductionmentioning

confidence: 99%

Ten-year evolution on credit risk research: a systematic literature review approach and discussion

Assef

Steiner

2020

Ing. Inv.

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Given its importance in financial risk management, credit risk analysis, since its introduction in 1950, has been a major influence both in academic research and in practical situations. In this work, a systematic literature review is proposed which considers both “Credit Risk” and “Credit risk” as search parameters to answer two main research questions: are machine learning techniques being effectively applied in research about credit risk evaluation? Furthermore, which of these quantitative techniques have been mostly applied over the last ten years of research? Different steps were followed to select the papers for the analysis, as well as the exclusion criteria, in order to verify only papers with Machine Learning approaches. Among the results, it was found that machine learning is being extensively applied in Credit Risk Assessment, where applications of Artificial Intelligence (AI) were mostly found, more specifically Artificial Neural Networks (ANN). After the explanation of each answer, a discussion of the results is presented.

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Section: Introductionmentioning

confidence: 99%

Ten-year evolution on credit risk research: a systematic literature review approach and discussion

Assef

Steiner

2020

Ing. Inv.

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“…As RNAs possuem uma ampla área de aplicação como previsão de risco de crédito (Selau & Ribeiro, 2009;Steiner et al, 2007), medicina (Blazadonakis & Michalis, 2008) e em polímeros (Contant et al, 2004), entre outras.…”

Section: Introductionunclassified

Aplicação de redes Neuro Fuzzy ao processamento de peças automotivas por meio de injeção de polímeros

Affonso

Sassi

2014

Prod.

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O processamento de peças automotivas por meio de injeção de polímeros envolve vários fenômenos físicos que ocorrem simultaneamente e que possuem caráter não linear e multivariável. Softwares comerciais podem ser utilizados na previsão dos parâmetros do processo, o que pode ser caro e inviável. Pode-se determinar os parâmetros de forma analítica, mas o tratamento desse problema requer a aplicação de teorias clássicas dos fenômenos de transporte, de difícil equacionamento. As redes NeuroFuzzy são aplicáveis a esse problema porque reúnem a capacidade de aprender das redes neurais artificiais com a capacidade da lógica Fuzzy de transformar variáveis linguísticas em regras. Neste trabalho combinou-se uma rede neural artificial Multilayer Perceptron e uma rede neural artificial Radial Basis Function à lógica Fuzzy para construir-se um modelo de inferência que previu o tempo de ciclo de processos de injeção de polímeros. Os resultados obtidos confirmam as redes NeuroFuzzy como opção para esse tipo de problema.

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“…According to Steiner et al (2007), making the correct decision as to granting credit is essential for the survival of financial institutions. Any error in the decision to grant credit may mean that a single operation promotes a loss equivalent of the gain obtained in dozens of other successful transactions, since non-receipt represents the total loss of the amount lent.…”

Section: Introductionmentioning

confidence: 99%

A systematic approach to construct credit risk forecast models

Selau

Ribeiro

2011

Pesqui. Oper.

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ABSTRACT. Due to the recent growth in the consumer credit market and the consequent increase in default indices, companies are seeking to improve their credit analysis by incorporating objective procedures. Multivariate techniques have been used as an alternative to construct quantitative models for credit forecast. These techniques are based on consumer profile data and allow the identification of standards concerning default behavior. This paper presents a methodology for forecasting credit risk by using three multivariate techniques: discriminant analysis, logistic regression and neural networks. The proposed method (deemed the CRF Model) consists of six steps and is illustrated by means of a real application. An important contribution of this paper is the organization of the methodological procedures and the discussion of the decisions that should be made during the application of the model. The feasibility of the approach proposed was tested in a program for granting credit offered by a network of pharmacies. The use of the models for forecasting credit risk greatly reduces the subjectivity of the analysis, by establishing a standardized procedure that speeds up and qualifies credit analysis.

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Extração de regras de classificação a partir de redes neurais para auxílio à tomada de decisão na concessão de crédito bancário

Cited by 11 publications

References 6 publications

Ten-year evolution on credit risk research: a systematic literature review approach and discussion

Ten-year evolution on credit risk research: a systematic literature review approach and discussion

Aplicação de redes Neuro Fuzzy ao processamento de peças automotivas por meio de injeção de polímeros

A systematic approach to construct credit risk forecast models

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