September 10, 2025

Harnessing Artificial Intelligence for Risk Assessment and Fraud Detection in Insurance: A Modern Approach to Predictive Modelling

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AI models showed superior performance in risk assessment compared to traditional techniques such as logistic regression.
XGBoost achieved an accuracy of 87.3% and an F1 score of 0.86, outperforming other models in classification tasks.
The study utilized a simulation-based experimental methodology with a synthetic dataset of 10,000 insurance client profiles.
Findings indicate that integrating multiple AI models may enhance precision and sensitivity in fraud detection.

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Implication

This research demonstrates AI's impact on improving risk assessment and fraud detection in insurance, suggesting innovative predictive modelling techniques.

Cite This Study

Mihai Vriscu (2025) studied this question.

synapsesocial.com/papers/68c23f63b210217d64795edb https://doi.org/10.2478/picbe-2025-0179

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Implication

This research demonstrates AI's impact on improving risk assessment and fraud detection in insurance, suggesting innovative predictive modelling techniques.

Harnessing Artificial Intelligence for Risk Assessment and Fraud Detection in Insurance: A Modern Approach to Predictive Modelling

Key Points

Discussion

Implication

Cite This Study

Harnessing Artificial Intelligence for Risk Assessment and Fraud Detection in Insurance: A Modern Approach to Predictive Modelling

Key Points

Discussion

Implication

Cite This Study