Research
Interpretable ML for Insurance Fraud (SOA)
A framework for interpretable machine-learning algorithms for fraud detection in health insurance.
This research delivers a framework for interpretable machine-learning tailored to fraud detection in health insurance. Machine-learning algorithms build intricate models by discerning patterns in data, but the risk of overfitting demands rigorous testing; this work brings transparency to the models that flag suspicious claims.
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