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Recession-Proof Strategies forScaling ML/AI in Insurance

As insurance companies apply AI across the entire insurance value chain over time, these decision making systems will have a greater impact on customers and expose enterprises to growing reputational, regulatory, and strategic/financial risk.

In this guide, learn how you can mitigate that risk, save money, and drive business goals through real-time optimization of MLOps models for top insurance use cases: underwriting, premium forecasting, pricing strategy, and customer servicing.

Arthur for Insurance

Learn why Arthur is the AI Performance Company for insurance and how we can deliver better results for top industry use cases.

Trusted by Fortune 100 Leaders

Fortune 100 leaders across financial services, healthcare, retail, and tech trust Arthur to monitor and improve their ML models to drive business impact.

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“Thanks to Arthur, we know that our preventative care models are fair, and that we can catch any potential issues before they impact our members…and the Arthur platform allows us to detect and fix data drift before it becomes a real problem.”
— Chief Analytics Officer, Humana

”The biggest challenge is looking at this from a reputational risk perspective. ​The last thing we want is to be on the front page of the news with a bias issue.”
— Global Tech VP

“Arthur is 6-9 months ahead of the competition and there was a clear preference for their UX among our data scientists.”— Head of Global Artificial Intelligence

Monitor, measure, and improve ML models with Arthur

Arthur helps data scientists, product owners, and business leaders accelerate model operations at scale. We work with enterprise teams to measure and optimize model performance in production for:

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Accuracy & Data Drift

Explainability & Transparency

Fairness & Bias Mitigation

Track model performance to detect and react to data drift, improving model accuracy for better business outcomes.

Build trust, ensure compliance, and drive more actionable ML outcomes with Arthur’s explainability and transparency APIs.

Proactively monitor for bias, track model outcomes against custom bias metrics, and improve the fairness of your models.

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