Machine Learning in Insurance

Streamline operations, reduce costs, and turn data into insight and predictions with machine learning.  Optimize your market selection, underwriting, pricing, and claims management operations.

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AI in Insurance

Helping transform organizations with AI technology. Gradient in insurance enables you to deploy predictive models in a fraction of the time it usually takes, delivering enhanced speed to market, more accurate machine learning pricing, reduced loss ratios, and higher conversion rates. By adopting a model-driven approach, companies are able to improve profitability, becoming more efficient, and deliver an enhanced customer experience.

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Challenges

Insufficient access to scalable compute resources

Gaining access to compute resources and orchestrating workloads requires extensive experience in tooling that becomes a costly distraction for data scientists and ML engineers. Infrastructure bottlenecks reduce velocity and precision and increase model ops friction, time to market, and operational risk.

Lack of standardization, process, & centralized hub

Collaboration among distributed research teams without a unified tool is a liability. Workflows that lack a standardized process and a unified hub lead to re-work and hinders the ability for data scientists to find, understand, build-on, and contribute to the various models in R+D and production.

Consumed by menial & redundant tasks

Data scientists and machine learning engineers spend roughly 25% of their time developing models. This means that 75% of their time is spent on costly distractions related to tooling and infrastructure. A end-to-end ML pipeline enables a rapid model delivery cycle without the need to perform cumbersome routine tasks.

Gradient can help

Conduct AI without bottlenecks

Experiment faster and deliver more breakthroughs. Remove DevOps pain with independent access to compute. Use the right tools for the job. Share and repurpose work across the team. More easily get models from development into production.

Build a modern AI practice

Create an organizational capability out of machine learning. Gain visibility into work happening across the organization. Accelerate the data machine learning management lifecycle. Reduce regulatory and operational risk while maintaining and updating models more frequently, with greater precision.

Become your unified AI hub

Support data science without sacrificing governance or security. Future-proof your machine learning stack. Gain transparency with complete model reproducibility. Offer a centralized platform for collaboration. Reduce model ops friction for rapid model delivery and iteration.

AI Applications

High Value Use Cases in Manufacturing

  • Strategic risk selection
  • Mitigating litigation risk
  • Reducing churn
  • Fraud detection
  • Price & demand forecasting
  • Optimized claims management
  • Precision pricing and reserving

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