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.

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.

Applying Fuzzy Logic and Neural Network to Rheumatism Treatment in Oriental Medicine

2007 ◽  
Vol 11 (1) ◽  
pp. 4-10 ◽  
Author(s):  
Cao Thang ◽  
◽  
Eric W. Cooper ◽  
Yukinobu Hoshino ◽  
Katsuari Kamei ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference ◽  
Training Data ◽  
Oriental Medicine ◽  
The Neural Networks

In this paper, we present an application of soft computing into a decision support system RETS: Rheumatic Evaluation and Treatment System in Oriental Medicine (OM). Inputs of the system are severities of observed symptoms on patients and outputs are a diagnosis of rheumatic states, its explanations and herbal prescriptions. First, an outline of the proposed decision support system is described after considering rheumatic diagnoses and prescriptions by OM doctors. Next, diagnosis by fuzzy inference and prescription by neural networks are described. By fuzzy inference, RETS diagnoses the most appropriate rheumatic state in which the patient appears to be infected, then it gives a prescription written in suitable herbs with reasonable amounts based on neural networks. Training data for the neural networks is collected from experienced OM physicians and OM text books. Finally, we describe evaluations and restrictions of RETS.

A NEURAL NETWORK MODEL FOR CREDIT RISK EVALUATION

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Schemes

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

Neural Network and Fuzzy Logic Diagnostics of 1X Faults in Rotating Machinery

2005 ◽  
Author(s):  
A. El-Shafei ◽  
T. A. F. Hassan ◽  
A. K. Soliman ◽  
Y. Zeyada ◽  
N. Rieger
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Vector Quantization ◽  
Fuzzy Inference ◽  
Rotating Machinery ◽  
Test Rig ◽  
Fuzzy Inference Engine ◽  
In Series ◽  
Fuzzy Logic Method

In this paper, the application of Neural Networks and Fuzzy Logic to the diagnosis of Faults in Rotating Machinery is investigated. The Learning-Vector-Quantization (LVQ) Neural Network is applied in series and in parallel to a Fuzzy inference engine, to diagnose 1x faults. The faults investigated are unbalance, misalignment, and structural looseness. The method is applied to a test rig [1], and the effectiveness of the integrated Neural Network and Fuzzy Logic method is illustrated.

scholarly journals MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

2018 ◽  
Vol 2 (1) ◽  
pp. 76
Author(s):  
Novan Wijaya
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Financial Risk ◽  
Banking Sector ◽  
Major Focus ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Risk Evaluation System

Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

Fuzzy Comprehensive Evaluation of Trading Regulatory Risk for a Unified and Interconnected Electricity Market in China

2014 ◽  
Vol 1070-1072 ◽  
pp. 1486-1490
Author(s):  
Yin Fang ◽  
Zhi Qiang Zhao ◽  
Chun Cheng Gao ◽  
Yong Dai ◽  
Shu Hong Shi
Keyword(s):  
Electricity Market ◽  
Risk Evaluation ◽  
Evaluation Method ◽  
Fuzzy Inference ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Risk Level ◽  
Risk Levels ◽  
Risk Indices ◽  
Evaluation Models

Trading regulatory risk arises prominently in the preliminary formulation of a unified and interconnected electricity market in China. Risk indices, evaluation models and methods as well as index weights are three important aspects of a comprehensive trading regulatory risk evaluation. In this paper, firstly, on the basis of the current electricity market environment in China, systematic trading regulatory risk indices used to quantify the risk level of the unified and interconnected electricity market are established. Secondly, evaluation models and a evaluation method of the trading regulatory risk are developed on the basis of synthesis of fuzzy inference and analytic hierarchy process (AHP). The fuzzy set approach is employed to identify the membership degree of each index to various risk levels, while the AHP is used to acquire the weights of the proposed trading regulatory risk indices. Finally, a simulation based case study on the trading regulatory risk evaluation is presented to illustrate the effectiveness of the proposed indices and the evaluation method.

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