The purpose of an insurance policy is to lessen or eliminate the monetary impact of certain risks and unanticipated events. The significance of accurate cost prediction has grown due to the ongoing increase in healthcare prices and the fact that health insurance premiums are affected by several demographic and health-related factors. Insurance companies, lawmakers, and people may all benefit from accurate cost projections for healthcare when making coverage and budgeting decisions. To estimate insurance costs and analyses complicated correlations among health characteristics, machine learning (ML) and deep learning (DL) methods provide useful ways. The purpose of this research was to create ML and DL models that could predict health insurance premiums based on important demographic and health-related characteristics. With R²=92.94%, RMSE=0.27, MSE=0.0727, and MAE=0.143, the suggested Recurrent Neural Network (RNN) performed admirably. These outcomes prove that the suggested RNN can successfully identify patterns and generate reliable estimates of insurance premiums. Healthcare cost management and data-driven insurance planning can both benefit from the created method.

Medical Insurance, Feature Importance, Underwriting

N. R. Möllmann, M. Mirbabaie, and S. Stieglitz, “Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations,” Health Informatics J., vol. 27, no. 4, Oct. 2021, doi: 10.1177/14604582211052391.

N. K. S. Patel, “Enterprise AI Operations in Healthcare IT Infrastructure,” Int. J. Intell. Syst. Appl. Eng., vol. 14, no. 1s, pp. 2325–2338, Aug, 2026.

V. Dogra, A. Singh, S. Verma, Kavita, N. Z. Jhanjhi, and M. N. Talib, “Analyzing DistilBERT for Sentiment Classification of Banking Financial News,” in Lecture Notes in Networks and Systems, vol. 248, 2021, pp. 501–510. doi: 10.1007/978-981-16-3153-5_53.

G. N. Ahmad, H. Fatima, S. Ullah, A. Salah Saidi, and Imdadullah, “Efficient Medical Diagnosis of Human Heart Diseases Using Machine Learning Techniques With and Without GridSearchCV,” IEEE Access, vol. 10, pp. 80151–80173, 2022, doi: 10.1109/ACCESS.2022.3165792.

S. Pujari et al., “Artificial intelligence for global health: cautious optimism with safeguards,” Bull. World Health Organ., vol. 101, no. 06, pp. 364-364A, Jun. 2023, doi: 10.2471/BLT.23.290215.

K. Hanson et al., “The Lancet Global Health Commission on financing primary health care: putting people at the centre,” Lancet Glob. Heal., vol. 10, no. 5, pp. e715–e772, May 2022, doi: 10.1016/S2214-109X(22)00005-5.

B. Meessen, “The Role of Digital Strategies in Financing Health Care for Universal Health Coverage in Low- and Middle-Income Countries,” Glob. Heal. Sci. Pract., vol. 6, no. Supplement 1, pp. S29–S40, Oct. 2018, doi: 10.9745/GHSP-D-18-00271.

H. Kaiser, N. Rotigliano, S. Flessa, B. Ekman, and J. Sundewall, “Extending universal health coverage to informal workers: A systematic review of health financing schemes in low- and middle-income countries in Southeast Asia,” PLoS One, vol. 18, no. 7, p. e0288269, Jul. 2023, doi: 10.1371/journal.pone.0288269.

D. McIntyre, A. G. Obse, E. W. Barasa, and J. E. Ataguba, “Challenges in Financing Universal Health Coverage in Sub-Saharan Africa,” in Oxford Research Encyclopedia of Economics and Finance, Oxford University PressNew York, NY, 2018. doi: 10.1093/acrefore/9780190625979.013.28.

Chu, S. Kwon, and P. Cowley, “Health Financing Reforms for Moving towards Universal Health Coverage in the Western Pacific Region,” Heal. Syst. Reform, vol. 5, no. 1, pp. 32–47, Jan. 2019, doi: 10.1080/23288604.2018.1544029.

V. P. Rambabu, J. Sreerama, and J. T. S. Singh, “AI-Driven Data Integration: Enhancing Risk Assessment in the Insurance Industry,” Aust. J. Mach. Learn. Res. Appl., vol. 2, no. 2, pp. 130–179, July, 2022.

D. P. Dāsepalli, “Artificial Intelligence in Digital Banking: A Systematic Literature Review and a Novel Cross-Domain Resilience Framework for Customer Experience, Fraud Detection, and Risk Management,” Jul. 16, 2026, Research Squae. doi: 10.21203/rs.3.rs-10362812/v1.

N. Malaviya and S. B. Gogia, “Development, implementation and benefits of a rheumatology-specific electronic medical record application with automated display of outcome measures.,” Int. J. Rheum. Dis., vol. 13, no. 4, pp. 347–60, Oct. 2010, doi: 10.1111/j.1756-185X.2010.01551.x.

W. L. Ho, J. Ali, and K. Caals, “Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance,” Bull. World Health Organ., vol. 98, no. 4, pp. 263–269, Apr. 2020, doi: 10.2471/BLT.19.234732.

Thesmar, D. Sraer, L. Pinheiro, N. Dadson, R. Veliche, and P. Greenberg, “Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges,” Pharmacoeconomics, vol. 37, no. 6, pp. 745–752, Jun. 2019, doi: 10.1007/s40273-019-00777-6.

R. A. Sowah et al., “Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs),” J. Eng., vol. 2019, pp. 1–19, Sep. 2019, doi: 10.1155/2019/1432597.

S. Panda, B. Purkayastha, D. Das, M. Chakraborty, and S. K. Biswas, “Health Insurance Cost Prediction Using Regression Models,” in 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON), IEEE, May 2022, pp. 168–173. doi: 10.1109/COM-IT-CON54601.2022.9850653.

M. Jödicke et al., “Prediction of health care expenditure increase: how does pharmacotherapy contribute?,” BMC Health Serv. Res., vol. 19, no. 1, p. 953, Dec. 2019, doi: 10.1186/s12913-019-4616-x.

T. Panch, H. Mattie, and R. Atun, “Artificial intelligence and algorithmic bias: implications for health systems,” J. Glob. Health, vol. 9, no. 2, Dec. 2019, doi: 10.7189/jogh.09.020318.

T. T, S. H. T, V. K. V, and K. R, “Medical Insurance Cost Analysis and Prediction using Machine Learning,” in 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), IEEE, Mar. 2023, pp. 113–117. doi: 10.1109/ICIDCA56705.2023.10100057.

V. Mehta, A. Panchal, K. Shah, and S. Purohit, “Medical Insurance Pricing and Affordability: Patterns, Risks, Cost Drivers,” in 2026 2nd International Conference on Big Data & Machine Learning (ICBDML), IEEE, Feb. 2026, pp. 1–6. doi: 10.1109/ICBDML68582.2026.11544600.

K. Mishra, K. Mishra, L. Kumar, K. Kumar, and A. Kumar, “Enhancing the Forecasting of Medical Insurance Premiums Through the Use of Multiple Methods,” in 2026 International Conference on Intelligent Systems in Engineering, Secured Systems and Cybersecurity (ICISESSC), IEEE, Apr. 2026, pp. 1022–1026. doi: 10.1109/ICISESSC68634.2026.11542626.

S. Mohan, S. Sharma, S. Agrawal, and S. Kamatchi, “Optimization of Insurance Claim Cost Prediction Through Health Data and Machine Learning,” in 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), IEEE, Jan. 2025, pp. 1–7. doi: 10.1109/ICAECT63952.2025.10958928.

M. R and M. Arunraj, “Enhancing Healthcare Insurance Cost Prediction with a Metaheuristic Optimization Approach: Comparative Analysis of Machine Learning Models,” in 2025 3rd International Conference on Inventive Computing and Informatics (ICICI), IEEE, Jun. 2025, pp. 1225–1232. doi: 10.1109/ICICI65870.2025.11069726.

R. Kandula, S. Kalyanapu, S. N. Rayapalli, K. Rao Veerabathina, V. Modugumudi, and S. R. Kanikella, “Medical Insurance Predictive Modelling: An Analysis of Machine Learning Methods,” in 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE, Mar. 2024, pp. 1–5. doi: 10.1109/IATMSI60426.2024.10502643.

R. Mary Chittilappilly, S. Suresh, and S. Shanmugam, “A Comparative Analysis of Optimizing Medical Insurance Prediction Using Genetic Algorithm and Other Machine Learning Algorithms,” in 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India: IEEE, May 2023, pp. 1–6. doi: 10.1109/ACCAI58221.2023.10199979.

K. Bhatia, S. S. Gill, N. Kamboj, M. Kumar, and R. K. Bhatia, “Health Insurance Cost Prediction using Machine Learning,” in 2022 3rd International Conference for Emerging Technology (INCET), IEEE, May 2022, pp. 1–5. doi: 10.1109/INCET54531.2022.9824201.

K. Tanusha, M. Varsha, E. Naveen Kumar, K. Pavan Kalayan, and P.M. Suresh, “Medical Insurance Price Prediction Using ML,” Int. Res. J. Adv. Eng. Manag., vol. 3, no. 05, pp. 2111–2116, 2025, doi: 10.47392/irjaem.2025.0333.

S. Tejaswi, D. S. Sirisha, D. L. Likhita, S. Ganesh, and C. H. Nirmala, “Prediction of Health Insurance Premiums Using ML,” vol. 11, no. 11, pp. 4571–4576, 2025.

M. Shanif et al., “Premiumpulse : AI-driven Insurance Cost Modeling,” Int. J. Eng. Res. Technol., vol. 15, no. 07, pp. 1–6, July, 2026, doi: 10.5281/zenodo.21588600.

S. Gangane, S. Khadse, Y. Wahane, K. Durugkar, S. Sheikh, and P. R. Bambodkar, “Health Insurance Premium Prediction System Using Machine Learning,” Int. J. Sci. Res. Eng. Manag., vol. 10, no. 04, pp. 1–9, 2026, doi: 10.55041/ijsrem61098.