• MAHALINGAM R
• JAYANTHI K

Small and Medium Enterprises (SMEs) are essential contributors to economic growth, yet they face significant financial vulnerabilities that can lead to crises or insolvency. Accurate prediction and mitigation of financial risks are crucial for ensuring their sustainability. This study presents a comparative analysis between the World Bank’s Women, Business, and the Law (WBL) RAW DATA (2010–2018) dataset and a newly proposed dataset designed for SMEs in Tamil Nadu, India. The proposed dataset comprises 180 features, surpassing the WBL dataset’s 177 features by including SME-specific and regionally relevant variables such as family dependents, financial assistance frequency, and vendor support metrics. Machine learning models, including Random Forest, Support Vector Machine, and Logistic Regression, were applied to both datasets to predict financial crises in SMEs. The SME dataset demonstrated superior predictive performance, with Random Forest achieving an accuracy of 92.5% compared to 85.6% with the WBL dataset. The enhanced feature set and regional focus of the SME dataset significantly improved the identification of risk factors and actionable insights for SMEs. The findings highlight the importance of customized datasets tailored to the unique needs of SMEs and specific regions. This study provides a foundation for policymakers and financial stakeholders to develop targeted strategies for strengthening SME financial resilience and ensuring their long-term success.

SMEs, financial crisis prediction, WBL dataset, SME dataset, machine learning, regional financial analysis, predictive analytics

"Comparative Analytics of Complex Financial Dataset and Proposed SME Dataset Enhancements in Financial Crisis Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.e727-e731, November-2024, Available :http://www.jetir.org/papers/JETIR2411482.pdf

"Comparative Analytics of Complex Financial Dataset and Proposed SME Dataset Enhancements in Financial Crisis Prediction", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. ppe727-e731, November-2024, Available at : http://www.jetir.org/papers/JETIR2411482.pdf