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Artificial Intelligence: To Fraud Detection and Analysis for Insurance Claims in USA using machine learning algorithms

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Analysis reveals XGBoost enhances accuracy in insurance claim predictions, suggesting AI may transform processes.

The XGBoost model achieved the highest accuracy with a 79% R-squared value, significantly boosting prediction efficacy.
Feature importance analysis identified smoking, BMI, and blood pressure as crucial factors affecting claims predictions.
Six machine learning algorithms including SVM, DT, RF, and KNN were assessed for their predictive performance on insurance claims.
Results indicate that integrating AI into healthcare insurance can optimize processes and reduce prediction errors.

Shilpa Kottapally (2025) studied this question.

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View Full Paper

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Authors

Analysis reveals XGBoost enhances accuracy in insurance claim predictions, suggesting AI may transform processes.

The XGBoost model achieved the highest accuracy with a 79% R-squared value, significantly boosting prediction efficacy.
Feature importance analysis identified smoking, BMI, and blood pressure as crucial factors affecting claims predictions.
Six machine learning algorithms including SVM, DT, RF, and KNN were assessed for their predictive performance on insurance claims.
Results indicate that integrating AI into healthcare insurance can optimize processes and reduce prediction errors.

Shilpa Kottapally (2025) studied this question.

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