A two-stage fuzzy neural approach for credit risk assessment in a Brazilian credit card company

Fonseca, Diego Paganoti; Corrêa, Henrique Luiz

doi:10.1016/j.asoc.2020.106329

Cited by 29 publications

(12 citation statements)

References 18 publications

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“…Simply put, in the light of FFISs, so many applications can be considered in which applying FFIS yields to more satisfactory results than ever before. The fields in which FFIS can prove applicable include fault detection [17], control systems [18], [19], handoff decision algorithms [20], internet of thing [21], risk assessment [22], [23], evaluating the quality of experience [24], decision support systems [25], fuzzy clustering and classification [26], [27], fuzzy image processing [28], fuel cell stack problem [29], educational systems [30], fuzzy modelling [31], psychology [32], emotion categories [33], packet scheduling algorithms [34], multiobjective optimization problem [35], decision-making [36], heuristic algorithms [37], etc.…”

Section: Discussionmentioning

confidence: 99%

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Mazandarani

2020

IEEE Access

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This paper presents a new machinery of compositional rule of inference called fractional fuzzy inference system (FFIS). An FFIS is a fuzzy inference system (FIS) in which consequent parts of a rule base consist of a new type of membership functions called fractional membership functions. Fractional membership functions are characterized using fractional indices. There are two types of fractional indices. Each type can be either constant or dynamic. An FFIS intelligently considers not only the truth degrees of information included in membership functions, but also the volume of the information in the process of making a conclusion. In other words, the volume of information extracted from a membership function depends on the truth degree of information. Concretely, the higher the truth degree, the larger the volume of information that is involved in the process of making a conclusion. It is shown that typical FISs, e.g. Mamdani's or Larsen's FISs, are special cases of FFISs. Specifically, as the fractional indices approach one, the FFIS approaches a typical FIS. In addition, using two theorems proved in this paper, it is demonstrated that, independent of the problem in question, a typical FIS never leads to results which are more satisfactory than those obtained by the FFIS corresponding to the typical FIS provided that a particular set of fractional indices is taken into account. Put another way, it seems sound to expect that applying FFIS always leads to more satisfactory results than applying its corresponding FIS. It is also shown that FFIS grants a special dynamic to FIS which can be also customized according to a new concept called reaction trajectories map (RTM). Particularly, the RTM enables decision makers to select an FFIS more suitable for their purpose. Some more concepts such as the left and right orders of an FFIS and the fracture index are also introduced in this paper.

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Section: Discussionmentioning

confidence: 99%

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Mazandarani

2020

IEEE Access

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“…Thus, the study of Fonseca et al (2020) was devoted to the development of scoring technologies as a tool for credit risk management of commercial banks. Hájek and Olej (2015) highlighted a number of practical studies aimed at assessing the financial condition and diagnosis of corporate bankruptcy with the use of tools from fuzzy logic theory, discriminant and regression analysis, and neural networks.…”

Section: Literature Surveymentioning

confidence: 99%

Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies

Kovalenko

Afanasieva

Zabuta

et al. 2021

JRFM

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This article considers the problems of overdue credit debt and the creation of effective methods to manage problem debts in banks. The purpose of this paper is to study the problem of overdue credit debt and create effective methods to manage problem debts in financial institutions. Based on a combination of tools of fuzzy logic theory and artificial neural networks, an economic-mathematical model of collection scoring was built. Kohonen self-organizing maps were used to set the parameters of membership functions in the process of fuzzification of quantitative variables of the built model. Data were taken from the official websites of four Bulgarian banks for 2015–2019. The volume of the prepared sample amounted to 1000 credit agreements with active overdue payments. The practical value of the built model of collection scoring for the recovery of overdue debt lies in the possibility to make recommendations for work with each segment of the portfolio of overdue loans in accordance with the calculated level of credit risk. The introduction of credit risk assessment models based on neuro-fuzzy technologies in the work of financial institutions will have a positive impact on the financial results of lending activities of banks.

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“…However, there are also problems such as low prediction accuracy and difficulty in collecting data. erefore, the combination of quantitative and qualitative methods has naturally become the research direction of enterprise human resource forecasting [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Human Resource Planning and Configuration Based on Machine Learning

Yuan

Dai

et al. 2022

Computational Intelligence and Neuroscience

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Human resources are the core resources of an enterprise, and the demand forecasting plays a vital role in the allocation and optimization of human resources. Starting from the basic concepts of human resource forecasting, this paper employs the backpropagation neural network (BPNN) and radial basis function neural network (RBFNN) to analyze human resource needs and determine the key elements of the company’s human resource allocation through predictive models. With historical data as reference, the forecast value of current human resource demand is obtained through the two types of neural networks. Based on the prediction results, the company managers can carry out targeted human resource planning and allocation to improve the efficiency of enterprise operations. In the experiment, the actual human resource data of a certain company are used as the experimental basic samples to train and test the two types of machine learning tools. The experimental results show that the method proposed in this paper can effectively predict the number of personnel required and can support the planning and allocation of human resources.

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A two-stage fuzzy neural approach for credit risk assessment in a Brazilian credit card company

Cited by 29 publications

References 18 publications

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies

Human Resource Planning and Configuration Based on Machine Learning

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