Whale social optimization driven deep recurrent neural network for loan eligibility prediction. (9th December 2022)

• Record Type: Journal Article
• Authors: Infant Cyril, Gnanasamy Lazar Sindhuraj; Ananth, John Patrick
• Is Part Of: Concurrency and computation. Volume 35:Number 3(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-09
• Subjects: Box‐Cox transformation -- Deep RNN -- loan eligibility -- prediction -- wrapper feature; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7510
• Languages: English
• ISSNs: 1532-0626

Abstract

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%.