R. Sangeetha; S. Sathish Kumar; B. Sharmila; P. Dency Mary

Optimizing Credit Risk Assessment in Banking Human Resource Management: A Enhanced Humboldt Squid based Probabilistic Spiking Neural Networks with Shunted Self-Attention

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Author(s)

R. Sangeetha ^1,* S. Sathish Kumar ² B. Sharmila ³ P. Dency Mary ⁴

1. Department of Commerce School of Social Sciences and Languages, Vellore Institute of Technology, Vellore, Tamil Nadu- 632014, India

2. Department of Electronics and Communication Engineering, PGP College of Engineering and Technology, Namakkal, Tamil Nadu - 637207, India

3. PG and Research Department of Commerce, Arulmigu Palaniandavar Arts College for Women, Palani 624615, India

4. Department of Commerce, Nilgiri College of Arts and Science, The Nilgiris, Tamil Nadu- 643239, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2025.03.02

Received: 23 Aug. 2024 / Revised: 8 Jan. 2025 / Accepted: 13 Feb. 2025 / Published: 8 Jun. 2025

Index Terms

Credit Risk Assessment, Commercial Banks, General Data Protection Regulation, Grid-Constrained Data Cleansing, Human Resource Management (HRM)

Abstract

The movement of capital, integration, distribution, and social supply and demand adjustment are all greatly aided by commercial banks; yet, integrating credit risk assessment is a difficult task for banking Human Resource Management (HRM). To overcome these issues, a novel credit risk assessment in HRM frameworks is done using the Enhanced Humboldt Squid based Probabilistic Spiking Neural Networks with Shunted self-attention (EHSPNN-SSA) method is proposed. Initially, the input commercial bank datasets are taken from General Data Protection Regulation (GDPR) and Advanced Analytics of Credit Registry (AACR) Datasets. Then these data are pre-processes using Grid-Restricted Data Filtering Approach (GRDFA). After that, the data is extracted using Hybrid Absolute deviation factors (ADFs) class document frequency (CDF) (hyb ADF-CDF) based feature extraction method. Then these data are classified using Enhanced Probabilistic Spiking Neural Networks with Shunted self-attention (EPSNN-SSA) and optimized using the Humboldt Squid Optimization Algorithm (HSOA). The framework is validated using real-world banking data and compared to existing methods to demonstrate its efficacy in assessing credit risk and optimizing human resource management processes. The results show that the introduced approach performs better than previous approaches in a number of performance measures, including risky data accuracy (99.6%), non-risky data accuracy (99.7%), and risky data accuracy (99.4%) for dataset 1 and dataset 2, respectively. This indicates the method's exceptional effectiveness and room for advancement in the field.

Cite This Paper

R. Sangeetha, S. Sathish Kumar, B. Sharmila, P. Dency Mary, "Optimizing Credit Risk Assessment in Banking Human Resource Management: A Enhanced Humboldt Squid based Probabilistic Spiking Neural Networks with Shunted Self-Attention", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.3, pp. 14-38, 2025. DOI:10.5815/ijieeb.2025.03.02

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