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This is a Quantitative Study study.

October 27, 2025

The Interpretable Artificial Neural Network in Vehicle Insurance Claim Fraud Detection Based on Shapley Additive Explanations

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The proposed model enhances fraud detection accuracy through the integration of machine learning and interpretability.
Achieving high accuracy in fraud detection demonstrates the effectiveness of artificial neural networks in this domain.
Analysis of feature importance provides crucial insights that improve understanding of decision-making processes.
This approach calls for increased transparency and trustworthiness in the insurance industry's fraud detection efforts.

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Implication

Observational analysis models fraud detection accuracy in vehicle insurance claims, indicating the importance of interpretability and feature importance.

Cite This Study

Wilson et al. (2024) studied this question.

synapsesocial.com/papers/68ff87f5c8c50a61f2bdd81e https://doi.org/10.62836/jitp.2024.367

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Implication

Observational analysis models fraud detection accuracy in vehicle insurance claims, indicating the importance of interpretability and feature importance.

The Interpretable Artificial Neural Network in Vehicle Insurance Claim Fraud Detection Based on Shapley Additive Explanations

Key Points

Discussion

Implication

Cite This Study

The Interpretable Artificial Neural Network in Vehicle Insurance Claim Fraud Detection Based on Shapley Additive Explanations

Key Points

Discussion

Implication

Cite This Study

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