Credit Risk Prediction and Classifier Comparison Using Enhanced Fuzzy Based Genetic Approach

Authors: Dr.G.Arutjothi, Mrs.D.Dhanalakshmi, Dr.K.Geetha, Mrs.M.Ushanandhini

DOI: https://doi.org/10.37082/IJIRMPS.v13.i2.232250

Short DOI: https://doi.org/g9fgff

Country: India

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Abstract: Credit risk assessment is important for financial institutions, which helps them to decide whether or not to accept loan applications from customers. DNN’s are used to establish multiple layer network structure evaluation model in credit risk assessment field, in which can directly obtain feature information to improve accuracy of classification from a large number of customer credit data. In this paper proposed Enhanced Fuzzy Based Genetic Approach used genetic approach reminiscence to do not forget suitable B cells in the course of the cloning system and designed two types of reminiscence: easy reminiscence and layer memory. Accomplish such combination; two noteworthy GA-Fuzzy half breed approaches have been investigated: Fuzzy Logic helped evolutionary algorithm and Genetic-Fuzzy Systems. The performance of the proposed scheme is evaluated using various metrics such as: Acquisition Cost, Cost Per Promotion, Life Time, Time to Process, Accuracy, Precision and Recall.

Keywords: Credit Prediction, Genetic Approach, Precision, Recall, Fuzzy Logic.

Paper Id: 232250

Published On: 2025-03-18

Published In: Volume 13, Issue 2, March-April 2025

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