scholarly journals Application of Fuzzy Logic to Assess Banks' Credit Risk

2021 ◽  
Vol 21 (2) ◽  
pp. 70-78
Author(s):  
M.I. Ozerova ◽  
◽  
I.E. Zhigalov
Keyword(s):  
Fuzzy Logic ◽  
Credit Risk ◽  
Fuzzy Inference ◽  
Credit Scoring ◽  
Banking System ◽  
Fuzzy Model ◽  
Computing Methods ◽  
Financial Assessment ◽  
Evolving System ◽  
To Receive

The banking system is a constantly evolving system. The information environment of the bank is growing, the volumes of processed information are increasing due to the growth of users and banking products. To reduce risks, banks make a financial assessment of the situation of individuals and legal entities. The aim of the work is to develop fuzzy multi-connected models designed to predict the receipt of a positive or negative decision to receive a banking product. The decision is made based on scoring. Scoring consists in assigning points for completing a certain questionnaire developed by underwriters of credit risk assessors. Based on the results of the points gained, the system automatically makes a decision on approving or refusing to issue a loan. Different banks have diffe¬rent scoring models. Purpose of the study. The paper considers the use of fuzzy models for making a decision by a bank to issue a banking product that implements the concept of “soft computing”. Methods. The use of fuzzy logic methods in credit scoring is not new, but it is not widely used in practice because it is expensive to integrate into existing systems. Each bank uses its own indicators of the client's financial reliability in scoring. Most of the indicators in banks are the same, but when deciding to issue different banking products, they have different numerical values. The data of the standard scoring methodology of a real bank were taken as the initial data. To predict a bank's decision to issue a banking product to a client, a fuzzy model was applied, production rules were proposed, and membership functions were determined. The model focused on the simultaneous processing of incoming data from multiple clients and for different banks and different scoring models. Results. The developed mathematical model for assessing the client's rating and predicting the decision to receive a banking product based on the fuzzy inference rule. The obtained results are proposed to be used in a multi-banking web-oriented system of providing banking products to corporate clients.

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation

2016 ◽  
Vol 12 (1) ◽  
pp. 47-62 ◽  
Author(s):  
Asogbon Mojisola Grace ◽  
Samuel Oluwarotimi Williams
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Risk Level ◽  
Inference System ◽  
Wrong Decision

Credit risk evaluation techniques that aid effective decisions in credit lending are of great importance to the financial and banking industries. Such techniques assist credit managers to minimize the risks often associated with wrong decision making. Several techniques have been developed in the time past for credit risk evaluation and these techniques suffer from one form of limitation or the other. Recently, powerful soft computing tools have been proposed for problem solving among which are the neural networks and fuzzy logic. In this study, a neural network based on backpropagation learning algorithm and a fuzzy inference system based on Mamdani model were developed to evaluate the risk level of credit applicants. A comparative analysis of the performances of both systems was carried out and experimental results show that neural network with an overall prediction accuracy of 96.89% performed better than the fuzzy logic method with 94.44%. Finding from this study could provide useful information on how to improve the performance of existing credit risk evaluation systems.

scholarly journals A Fuzzy Inference System for Credit Scoring using Boolean Consistent Fuzzy Logic

2018 ◽  
Vol 11 (1) ◽  
pp. 414 ◽  
Author(s):  
Milica Latinovic ◽  
Ivana Dragovic ◽  
Vesna Bogojevic Arsic ◽  
Bratislav Petrovic
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Credit Scoring ◽  
Inference System

Comparative Analysis of Neural Network and Fuzzy Logic Techniques in Credit Risk Evaluation

2016 ◽  
pp. 1289-1305
Author(s):  
Asogbon Mojisola Grace ◽  
Samuel Oluwarotimi Williams
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Risk Level ◽  
Fuzzy Logic Method ◽  
The Neural Networks

Credit risk evaluation techniques that aid effective decisions in credit lending are of great importance to the financial and banking industries. Such techniques assist credit managers to minimize the risks often associated with wrong decision making. Several techniques have been developed in the time past for credit risk evaluation and these techniques suffer from one form of limitation or the other. Recently, powerful soft computing tools have been proposed for problem solving among which are the neural networks and fuzzy logic. In this study, a neural network based on backpropagation learning algorithm and a fuzzy inference system based on Mamdani model were developed to evaluate the risk level of credit applicants. A comparative analysis of the performances of both systems was carried out and experimental results show that neural network with an overall prediction accuracy of 96.89% performed better than the fuzzy logic method with 94.44%. Finding from this study could provide useful information on how to improve the performance of existing credit risk evaluation systems.

scholarly journals Fuzzy Model for Risk Assessment of Machinery Failures

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 525 ◽  
Author(s):  
Dejan V. Petrović ◽  
Miloš Tanasijević ◽  
Saša Stojadinović ◽  
Jelena Ivaz ◽  
Pavle Stojković
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Fuzzy Sets ◽  
Fuzzy Inference ◽  
Mining Industry ◽  
Fuzzy Model ◽  
Assessment Model ◽  
Risk Level ◽  
Engineering System ◽  
Risk Assessment Model

The main goal of this research was the development of an algorithm for the implementation of negative risk parameters in a synthesis model for a risk level assessment for a specific machine used in the mining industry. Fuzzy sets and fuzzy logic theory, in combination with statistical methods, were applied to analyze the time picture state of the observed machine. Fuzzy logic is presented through fuzzy proposition and a fuzzy composition module. Using these tools, the symmetric position of the fuzzy sets with regard to class was used, and the symmetric fuzzy inference approach was used in an outcome calculation. The main benefit of the proposed model is being able to use numerical and linguistic data in a risk assessment model. The proposed risk assessment model, using fuzzy logic conclusions and min–max composition, was used on a mobile crushing machine. The results indicated that the risk level of the mobile crushing machine was in the “high” category, which means that it is necessary to introduce maintenance policies based on this high risk. The proposed risk assessment model is useful for any engineering system.

scholarly journals Driver's Behavior Modeling Using Fuzzy Logic

2010 ◽  
Vol 2010 ◽  
pp. 1-29 ◽  
Author(s):  
Sehraneh Ghaemi ◽  
Sohrab Khanmohammadi ◽  
Mohammadali Tinati
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Fuzzy Rules ◽  
Behavior Modeling ◽  
Linguistic Terms ◽  
Steering Angle ◽  
Hierarchical Fuzzy System

In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model calledModel Iis presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules calledModel IIandModel IIIby using Sugeno fuzzy inference.Model IIandModel IIIhave less linguistic terms thanModel Ifor the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.

scholarly journals Development of a fuzzy logic based software for automation of a single pool irrigation canal

2013 ◽  
pp. 226-232
Author(s):  
Dr. R. Gopakumar ◽  
Reena Nair ◽  
Vinuraj R. ◽  
Sony Davis ◽  
Bijeesh V. ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Water Level ◽  
Fuzzy Inference ◽  
Optimization Technique ◽  
Research Work ◽  
Fuzzy Model ◽  
Irrigation Canal ◽  
Level Control ◽  
Doctoral Research ◽  
Fuzzy Control Algorithm

A fuzzy logic based software for automation of a single pool irrigation canal is presented. Purpose of the software is to control downstream discharge and water level of the canal, by adjusting discharge release from the upstream end and upstream gate settings. The software is developed on a fuzzy control algorithm proposed by the first author during his doctoral research work and published in literature. Details of the algorithm are given. The algorithm was originally developed using fuzzy logic tool box of MATLAB, which is proprietary software not available freely and hence cannot be adopted for general use. Present study describes development of a canal automation software based on this algorithm using open source tools, which are freely available. The software is transparent and intuitive, which can be easily applied by field engineers. The effort required in tuning the fuzzy model has been reduced by including an optimization technique. Also, a new procedure has been introduced for fuzzy inference based on the Mamdani Implication method. The software is tested by applying it to water level control problem in a canal with a single pool, as reported in literature, and satisfactory results are obtained.

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies were 95.56% for J48-CFS, 92.78% for REP-CFS, and 93.33% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

Plant Methods ◽  
2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

AbstractA combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals Determining traffic congestion utilizing a fuzzy logic model and Floating Car Data (FCD)

2021 ◽  
Vol 4 ◽  
pp. 1-8
Author(s):  
Maja Kalinic ◽  
Jukka M. Krisp
Keyword(s):  
Fuzzy Logic ◽  
Traffic Congestion ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Linear Functions ◽  
Inference System ◽  
Floating Car Data ◽  
Federal Highway ◽  
Almost All

Abstract. Traffic congestion is a dynamic spatial and temporal process and as such might not be possible to model with linear functions of various dependent variables. That leaves a lot of space for non-linear approximates, such as neutral networks and fuzzy logic. In this paper, the focus is on the fuzzy logic as a possible approach for dealing with the problems of measuring traffic congestion. We investigate the application of this framework on a selected case study, and use floating car data (FCD) collected in Augsburg, Germany. A fuzzy inference system is built to detect degrees of congestion on a federal highway B17. With FCD, it is possible to obtain local speed information on almost all parts of the network. This information, together with collected vehicle location, time and heading, can be further processed and transformed into valuable information in the form of trip routes, travel times, etc. Initial results are compared with traditional method of expressing levels of congestion on a road network e.g. Level of Service – LOS. The fuzzy model, with segmented mean speed and travel time parameters, performed well and showed to be promising approach to detect traffic congestions. This approach can be further improved by involving more input parameters, such as density or vehicle flow, which might reflect traffic congestion event even more realistically.

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