Deep learning based loan eligibility prediction with Social Border Collie Optimization

PurposeThe bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to...

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Vydáno v:Kybernetes Ročník 52; číslo 8; s. 2847 - 2867
Hlavní autoři: Infant Cyril, G.L., Ananth, J.P.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Emerald Publishing Limited 03.08.2023
Emerald Group Publishing Limited
Témata:
Accuracy
Algorithms
Artificial neural networks
Banking industry
Banks
Classifiers
Conditional probability
Credit scoring
Data mining
Datasets
Decision making
Deep learning
Discriminant analysis
Eligibility
Feature selection
Fuzzy logic
Least squares method
Loans
Machine learning
Methods
Optimization
Parameter sensitivity
Prediction models
Recurrent neural networks
Regression models
Risk analysis
Statistical analysis
Support vector machines
Deep neuro fuzzy network
Naive Bayes
Wrapper method
Loan eligibility prediction
Box-cox transformation
ISSN:0368-492X, 1758-7883
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Shrnutí:PurposeThe bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction.Design/methodology/approachThis aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result.FindingsThe analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN).Originality/valueThis paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction.
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ISSN:0368-492X
1758-7883
DOI:10.1108/K-10-2021-1073