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@article{191027,
        author = {Shrivastav Ratnesh Kumar and Dr. M.P Singh},
        title = {Effectiveness Of AI-Driven Models for Employee Attrition Prediction in The Indian Financial Sector},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {53-65},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191027},
        abstract = {Employee attrition poses a critical challenge to the Indian financial sector, where high turnover rates in banks and NBFCs lead to substantial costs in recruitment, training, productivity loss, and client relationships. Traditional HR analytics methods provide limited predictive accuracy due to the complex and nonlinear nature of workforce data. Artificial Intelligence (AI) and Machine Learning (ML) offer promising alternatives by analysing large datasets and uncovering hidden patterns that drive attrition.
This study examines the effectiveness of AI-driven models for predicting employee attrition in the Indian financial sector. Multiple algorithms—Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machines, and Neural Networks—were applied to employee datasets covering demographic, job-related, and organizational factors. Models were evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, while feature importance and explainable AI techniques (e.g., SHAP values) were used to enhance interpretability.
Findings indicate that ensemble models like Random Forest and Gradient Boosting outperform traditional regression methods, offering higher predictive accuracy and robustness. Key predictors of attrition include tenure, compensation progression, performance ratings, and role transitions. The study highlights the dual value of AI models: predictive power and actionable insights for HR strategy.
The research contributes to workforce analytics by demonstrating the practical applicability of AI in reducing attrition-related risks, thereby enabling financial institutions to strengthen retention strategies and ensure organizational stability.},
        keywords = {.},
        month = {},
        }