Journal of Science Engineering Technology and Management Science

Artificial Intelligence (AI) has emerged as a transformative force in contemporary recruitment and selection, reshaping how organizations identify, assess, and hire talent. The growing adoption of AI-enabled tools including automated resume screening, recruitment chatbots, predictive analytics, algorithmic matching systems, and video-based assessments has generated both opportunities and challenges for human resource management. This narrative review synthesizes scholarly literature published between 2015 and 2025 to examine the evolving role of AI in recruitment and selection and its implications for organizational decision-making. The review identifies four dominant themes: the transformation of recruitment processes through automation and data-driven decision support; the changing roles of recruiters and applicants; ethical concerns relating to algorithmic bias, transparency, and privacy; and the growing importance of human–AI collaboration in hiring decisions. The analysis indicates that AI can enhance recruitment efficiency, scalability, consistency, and candidate matching while reducing administrative workload. However, these benefits are contingent upon the quality of underlying data, the transparency of algorithmic systems, effective governance mechanisms, and continued human oversight. The review argues that AI should be viewed as a decision-support technology rather than a substitute for professional judgment. By integrating technological, organizational, ethical, and human-centered perspectives, the study advances understanding of responsible AI adoption in recruitment and selection. It further proposes that sustainable hiring outcomes depend on balancing technological capabilities with fairness, accountability, transparency, and human involvement in decision-making processes.

Keywords : Artificial Intelligence; Recruitment and Selection; Human Resource Management; Algorithmic Hiring; Human–AI Collaboration; Responsible AI.

Author : Jallipalli A. N. K. Jayanth¹, Sanka Lithish Gupta², Kella Yasaswini³

Title : Impact of Artificial Intelligence on Recruitment and Selection

Volume/Issue : 2026;03(06)

Page No : 1-23

—In today's digital financial landscape, the rise of internet banking, electronic payment systems, blockchain, and cryptocurrencies has created a significant challenge for financial fraud. Due to their inability to identify complicated relations between connected entities, conventional fraud detection techniques, such rule-based and statistical models, are inadequate for detecting more sophisticated fraud schemes. Thanks to AI's use of cutting-edge machine learning (ML) and deep learning (DL) technologies, fraud detection has never been more effective. Graph Neural Networks (GNNs) are one approach that has attracted a lot of attention; they employ GNNs to examine the relationships between commodities, customers, merchants, and devices, as well as between accounts and devices. GNNs can identify hidden fraud trends, coordinated fraud rings, money laundering, identity theft and cryptocurrency crimes among other things, using relational and structural information. This article covers all aspects related to AI fraud detection solutions, ranging from simple statistical techniques to more advanced methods like ML, DL, and graph-based solutions. Furthermore, the paper explores the primary GNN architectures, including Graph Attention Networks, Graph Convolutional Networks, and Temporal Graph Neural Networks, delving into their applications, advantages, and disadvantages in financial fraud detection. The review finds that the GNN-based methods demonstrated high detection accuracy, good real-time surveillance ability, and could be applied to develop scalable, interpretable and privacy-preserving financial security frameworks.

Keywords : Financial Fraud Detection, Artificial Intelligence (AI), Graph Neural Networks (GNNs), Machine Learning, Deep Learning, Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs).

Author : Dr. Prashant Kumar Srivastava

Title : AI-Driven Financial Fraud Detection: A Review of Graph Neural Network Approaches

Volume/Issue : 2026;03(06)

Page No : 24-34

The growing integration of artificial intelligence and algorithmic systems into Human Resource Management (HRM) has transformed organisational decision-making processes while simultaneously intensifying concerns surrounding fairness, transparency, and ethical accountability. Although existing scholarship has extensively examined the operational benefits of algorithmic HRM, comparatively limited attention has been devoted to understanding how organisations can balance algorithmic efficiency with human-centred equity in consequential employment decisions. Addressing this gap, the present study develops a conceptual characterisation of Algorithmic Human Resource Management through the integration of socio technical systems theory, augmented decision-making theory, and the algorithmic fairness literature. Methodologically, the study adopts an integrative literature review approach supplemented by comparative case analysis of three widely discussed organisational cases: Amazon’s abandoned AI recruitment system, Unilever’s hybrid AI-assisted hiring model, and HireVue’s algorithmic video interview platform. These cases are further contextualised through a theoretically constructed vignette designed to examine the limits of narrow algorithmic filtering and the continuing relevance of human evaluative judgment. Through this multi-layered analysis, the paper investigates the governance structures required for effective human–AI integration in HRM and explores the broader tension between predictive efficiency and procedural equity.

Keywords : Algorithmic Human Resource Management; Artificial Intelligence in HRM; Algorithmic Governance; Human-in-the-Loop Systems; Algorithmic Fairness; Ethical AI; Socio Technical Systems Theory; HR Analytics.

Author : Ruwayda Hussain¹*, Dalli Vineetha Reddy²

Title : Towards a Characterisation of Algorithmic Human Resource Management: Exploring the Duality of Efficiency and Equity

Volume/Issue : 2026;03(06)

Page No : 35-58