{"id":6076,"date":"2026-10-12T07:55:51","date_gmt":"2026-10-12T07:55:51","guid":{"rendered":"https:\/\/interfinance.tsue.uz\/?p=6076"},"modified":"2026-10-12T07:55:51","modified_gmt":"2026-10-12T07:55:51","slug":"predicting-employee-turnover-using-artificial-intelligence-and-hr-analytics-modern-approaches-and-opportunities","status":"publish","type":"post","link":"https:\/\/interfinance.tsue.uz\/?p=6076","title":{"rendered":"PREDICTING EMPLOYEE TURNOVER USING ARTIFICIAL INTELLIGENCE AND HR ANALYTICS: MODERN APPROACHES AND OPPORTUNITIES"},"content":{"rendered":"

Tohirova Mashhura Ulug\u2018bek qizi<\/em><\/strong><\/p>\n

Student, Tashkent State University of Economics<\/em><\/p>\n

Email:<\/em>tohirovamashhura74@gmail.com<\/em><\/a><\/p>\n

ORCID:0009-0002-1739-8281<\/em><\/p>\n

Abstract:<\/em><\/strong> This article explores the theoretical, methodological, and practical aspects of predicting employee turnover through Artificial Intelligence (AI) and HR Analytics. The study justifies the necessity of shifting from reactive workforce management to a proactive, data-driven framework in modern organizations. The effectiveness of integrating machine learning algorithms (such as regression models, random forests, gradient boosting, and neural networks) with behavioral indicators to detect turnover risks at an early stage is thoroughly analyzed. Furthermore, the practical potential of modeling HR metrics, the interpretability of algorithms using Explainable AI (XAI) principles, and the opportunities for implementing these digital tools within organizational frameworks are highlighted. The findings contribute to stabilizing workforce retention and optimizing strategic HR decision-making.<\/em><\/p>\n

Keywords:<\/em><\/strong> HR Analytics, Artificial Intelligence, employee turnover, machine learning, predictive models, staff rotation, Big Data, Explainable AI, proactive HR.<\/em><\/p>\n

<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Tohirova Mashhura Ulug\u2018bek qizi Student, Tashkent State University of Economics Email:tohirovamashhura74@gmail.com ORCID:0009-0002-1739-8281 Abstract: This article explores the theoretical, methodological, and practical aspects of predicting employee turnover through Artificial Intelligence (AI) and HR Analytics. The study justifies the necessity of shifting Read More …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[79],"tags":[],"class_list":["post-6076","post","type-post","status-publish","format-standard","hentry","category-2026-5-issue-1-6"],"yoast_head":"\nPREDICTING EMPLOYEE TURNOVER USING ARTIFICIAL INTELLIGENCE AND HR ANALYTICS: MODERN APPROACHES AND OPPORTUNITIES - Journal of International Finance and Accounting<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/interfinance.tsue.uz\/?p=6076\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"PREDICTING EMPLOYEE TURNOVER USING ARTIFICIAL INTELLIGENCE AND HR ANALYTICS: MODERN APPROACHES AND OPPORTUNITIES - Journal of International Finance and Accounting\" \/>\n<meta property=\"og:description\" content=\"Tohirova Mashhura Ulug\u2018bek qizi Student, Tashkent State University of Economics Email:tohirovamashhura74@gmail.com ORCID:0009-0002-1739-8281 Abstract: This article explores the theoretical, methodological, and practical aspects of predicting employee turnover through Artificial Intelligence (AI) and HR Analytics. 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