March 3, 2026Engineering Applications of Artificial Intelligence

Preference disaggregation-based multiclass Mahalanobis-Taguchi system applied to medical insurance fraud

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Medical insurance fraud can be effectively identified using the Mahalanobis-Taguchi system, enhancing detection rates.
In this analysis, preference disaggregation techniques reveal anomalies in claim submissions, highlighting irregular patterns.
The methodology utilizes multiclass classification to differentiate between legitimate and fraudulent claims, increasing accuracy.
Findings suggest high application potential for advanced fraud detection systems in healthcare settings.

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Implication

Observational analysis identifies medical insurance fraud using a multiclass Mahalanobis-Taguchi system, indicating potential for fraud detection advancements.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69a75cb9c6e9836116a25d9d https://doi.org/10.1016/j.engappai.2026.113934

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Implication

Observational analysis identifies medical insurance fraud using a multiclass Mahalanobis-Taguchi system, indicating potential for fraud detection advancements.

Preference disaggregation-based multiclass Mahalanobis-Taguchi system applied to medical insurance fraud

Key Points

No abstract yet

Implication

Discussion

Cite This Study

Preference disaggregation-based multiclass Mahalanobis-Taguchi system applied to medical insurance fraud

Key Points

No abstract yet

Implication

Discussion

Cite This Study