P. Dency Mary

Work place: Department of Commerce, Nilgiri College of Arts and Science, The Nilgiris, Tamil Nadu- 643239, India

E-mail:

Website: https://orcid.org/0009-0008-8487-8115

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Biography

P. Dency Mary earned her M.Com degree from Bharathiar University in 2012 and her Doctorate in Commerce from the same University of Tamil Nadu in 2024. She has actively participated in numerous national and international conferences, where she has presented a substantial body of her scholarly work. She has contributed over eight research papers to prestigious journals indexed in Web of Science and Scopus. With more than a decade of pedagogical experience in various arts and science colleges in Nilgiris, she has distinguished herself as an educator. Additionally, she holds life membership in the Institute for Education Research and Publication (IFERP) as well in Global Professors Welfare Association Forum.

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

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

DOI: https://doi.org/10.5815/ijieeb.2025.03.02, Pub. Date: 8 Jun. 2025

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.

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