A Hybrid Transformer-Based Human Resource Recruitment System for Efficient Business Process Management

Business process management (BPM) heavily relies on recruitment processes which determine organizational performance and the quality of hired employees. Traditional hiring procedures create selection flaws because they waste time on questionable decision-making processes with inefficient steps that produce insufficient candidate evaluation. A hybrid recruitment system using Transformer technology with NLP and MCDM helps boost HR recruitment efficiency by processing text data and making multi-criteria decision support possible. The new recruitment system unites Transformer models including BERT, GPT, T5 to analyze resumes alongside Analytic Hierarchy Process (AHP) and TOPSIS decision structure to perform Artificial Intelligence-powered candidate selection and ranking. The test results using data from more than 100,000 resumes and job descriptions show that this proposed model produces better results than conventional hiring approaches. By utilizing the proposed AI system, the screening accuracy for candidate-job matches increased from 78.4 % manual screening to 92.4 %. This improvement led to more accurate talent acquisition. The recruitment process duration decreased by 47% due to improved hiring operations. This framework delivered improved hiring equity because it successfully decreased discrimination by 36% during the selection process. The system demonstrated operational supremacy while reducing hiring expenses by 32% together with a 28% improvement in overall hiring efficiency. The research shows that AI-powered recruiting software can boost selection quality while decreasing expense and prejudice during employee search and acquisition processes. New research will study Explainable AI (XAI) methods for developing decision clarity and multimodal assessment methods based on voice and video analytics to enhance automatic recruitment systems. These findings demonstrate that AI stands crucial for modernizing Human Resource systems while enhancing business staff management methods.