Developing a Framework to Identify Professional Skills Required for Banking Sector Employee in UK using Natural Language Processing (NLP) Techniques

Anthony, Gayanika

Abstract

The banking sector is changing dramatically, and new studies reveal that many financial institutions are having challenges keeping up with technology advancements and an acute shortage of skilled workers. The banking industry is changing into a dynamic field where success requires a wide range of talents. For the industry to properly analyses, match, and develop personnel, a strong skill identification process is needed. The objective of this research is to establish a framework for determining the competencies needed by banking industry experts through data extraction from job postings on UK websites.
Data is extracted from job vacancy websites leveraging web-based annotation tools and Natural Language Processing (NLP) techniques. This study starts by conducting a thorough examination of the literature to investigate the theoretical underpinnings of NLP techniques, its applications in talent management and human resources within the banking industry, and its potential for skill identification. Next, textual data from job ads is processed using NLP techniques to extract and categorize talents unique to these categories. Advanced algorithms and approaches are used in the NLP-based development process to automatically extract skills from unstructured textual material, guaranteeing that the skills gathered are accurate and most relevant to the needs of the banking industry. To make sure the NLP techniques-driven skill identification is accurate and up to date, the extracted skills are verified by expert feedback.
In the final phase, machine learning models are employed to predict the skills required for banking sector employees. This study delves into various machine learning techniques, which are implemented within the framework. By preprocessing and training on skills extracted from job advertisements, these models undergo evaluation to assess their effectiveness in skill prediction. The results offer a detailed analysis of each model's performance, with metrics such as recall, precision, and F1-score being used for assessment. This comprehensive examination underscores the potential of machine learning in skill identification and highlights its relevance in the banking sector.
Key Words: Machine Learning, Banking Sector, Employability, Data Mining, NLP, Semantic analysis, Skill assessment, Skill Recognition, Talent management

Citation

Anthony, G. (2024). Developing a Framework to Identify Professional Skills Required for Banking Sector Employee in UK using Natural Language Processing (NLP) Techniques. (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Mar 18, 2024
• Publicly Available Date: Apr 27, 2024
• Award Date: Mar 26, 2024