The present study introduces a health insurance prediction system that leverages machine learning methodologies. In contemporary times, there has been a notable increase in endeavors focused on tackling this matter since the significance of health insurance as a research topic has markedly escalated following the pandemic. The dataset employed in this research comprises 1338 observations 7 columns and corresponds to individual medical expenditures in the United States, available at the Kaggle platform. The dataset encompasses a variety of variables utilized in the prediction of insurance prices, including age, gender, BMI, smoking status, and number of children. The researchers used machine learning models, including neural networks, XAI, and auto modeling, to determine the correlation between pricing and the attributes. The training process involved partitioning the dataset into an 80-20 ratio for training and evaluation. Consequently, the system achieved an accuracy rate of 97% by Gradient Boosting, but we corrected it to 92% by Gradient Boosting Regressor by encoding and hyper-tuning. Also, among predictive machine learning models, Random Forest had the best accuracy i.e., of 83.44%.Â

Abstractâ\euro”An effective fraud detection system must protect millions of clients for a secure banking system, which can be achieved using machine learning and AI. In this article, we have applied four supervised machine learning models: k-nearest neighbors (KNN), random forest (RF), decision tree, and logistic regression (LR) algorithm to detect bank fraud for a synthetic dataset having 1,00,000 rows and 32 columns. Adequate preprocessing, decoding, rigorous feature engineering, validation, performance evaluation, and explanation have allowed the readers to understand the whole study. Though the algorithms’ accuracy is similar, logistic regression shows a higher accuracy of 0.98921 for label encoding, which is not prescribed. Still, a significant AUC of 95% has been achieved in XGBoost and LGBM. Further application of this study can be done in real-life cases of banks, insurance, and finance institutions.