• Dr. S Shiva Prasad
• Dr. B.Harikrishna
• A S Arockia Mancy
• CH Gnaneshwarakushlav
• Bala Sridhar Reddy ,M Sai Kiran Chary

Accurate cost estimation and efficient cost management are essential for the insurance sector to manage risks and provide competitive pricing. Traditional methods often miss the complex interplay of factors like age, medical history, and lifestyle, which heavily influence costs. This project introduces a 90% accurate machine learning model using advanced algorithms like XGBoost to analyze historical data and identify key cost drivers. Developed with Python tools such as Scikit-Learn and Pandas. The model improves pricing strategies, reduces financial risks, and enhances customer experience. It ensures transparent, scalable, and adaptable insurance pricing for a dynamic market.

Machine Learning, Insurance Pricing, Cost Estimation, XGBoost, Risk Management

"Predictive Analytics for Insurance Premium Forecasting Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.j437-j442, May-2025, Available :http://www.jetir.org/papers/JETIR2503957.pdf

"Predictive Analytics for Insurance Premium Forecasting Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppj437-j442, May-2025, Available at : http://www.jetir.org/papers/JETIR2503957.pdf