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@article{174971,
        author = {Vuppala Saranya and T Dilip Sai Nitish and V Mercy},
        title = {Smart HR Solutions--using AI to predict and reduce employee attrition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1257-1262},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174971},
        abstract = {Organizations have been concerned about high voluntary turnover, or attrition, as a significant area of focus, resulting in elevated recruitment costs, productivity losses, and team-disruptive behaviour. Historically, HR systems have primarily focused upon the analysis of past data and behaviours of dependent variables - known as lag effect. This article presents a machine learning solution to predict, on the basis of the performance, engagement, demographics, and job satisfaction of employees, the risk of employees ‘attrition. The system provides real time predictions of employees’ likelihood of exit from the firm, enabling Human Resource (HR) managers to premptively intervene as required. The model facilitates individualized customized retention strategies dependent on the individual risk profile, including assessments of work-life balance and conducting stay one on one sessions if necessary. The combination of predictive analytics with useable, actionable insights provides HR managers enable to make informed decisions improving workforce stability and employee retention.},
        keywords = {HR Management, Retention Strategies, Workforce Stability, Employee Risk Prediction, Human Resource Analytics, Predictive Model, Data-Driven Decision Making},
        month = {April},
        }