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.

Summary People can use bank loans to make investment decisions thanks to technological advancements in the banking sector. The bank, however, only has a limited amount of resources, therefore it must grant to borrowers who can follow to the repayment schedule. Determining the candidate for a loan and finding a secure alternative for a bank are therefore crucial tasks. A deep recurrent neural network based on the whale social optimization algorithm (WSOA) is developed to predict loan eligibility. The WSOA is modified in this case to train Deep RNN for loan eligibility prediction in order to yield the most precise result. The proposed WSOA is developed by combining the social ski‐driver algorithm and the whale optimization algorithm. By selecting the trusted person to save bank assets, the risk factor is also decreased. The loan eligibility prediction is performed by processing massive data with Box‐Cox transformation. Moreover, wrapper feature selection is adapted for choosing imperative features. The proposed WSOA‐based Deep RNN provided increased efficiency with the highest accuracy of 94%, the highest sensitivity of 95.4%, and the highest specificity of 91.3%.