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“Domino encourages us to experiment more. The more projects we can get through, the less time and effort we have to spend recreating results or doing the same thing twice.”

Amy Gu, Data Scientist at Allstate

What are the challenges of data science in insurance?

  1. Reproducibility: Without historical systems of record or version controls, it could take months to recreate or understand past models.

  2. Collaboration: Communication delays between data scientists and stakeholders slow down the pace of innovation. IT bottlenecks prevent data scientists from keeping up with emerging technologies and from choosing the ideal computing environments for their work.

  3. Regulatory Compliance: Non-technical users waste time tracking down answers to regulators’ questions, due to a lack of institutional knowledge needed to access and understand current predictive models.

How does the Domino data science platform help insurance companies?

  • Accelerate research: Instead of re-inventing the wheel or rediscovering results, employees build on past experiments and test more ideas in parallel, so they deliver new models faster. The Domino Compute Grid gives data scientists one-click access to powerful hardware, including large GPU machines, to test more ideas faster.

    The patent-pending Domino Reproducibility Engine saves every model revision automatically, including code, data sets, summary statistics, and discussion around results. This reduces wasted effort reproducing past work, while keeping research moving forward.

  • Enable collaboration: The collaboration tools within Domino include shared environments, forking, code merge requests, integrated discussion and more. Small and large teams find that Domino increases shared context, leading to better ideas for model improvement.

  • Enable open-source: Data scientists have the agility to use their tool of choice, which is critical to staying on the cutting edge and can reduce costs. This also allows the company to hire, ramp, and keep the best people who will innovate faster using the technologies that work best for them.

  • Ensure regulatory compliance: All data science work is saved with 100% reproducibility of experiments and results. “Insurance is a heavily regulated environment. We can go back to any project at any point in time, see what decision was made and recreate that model if needed,” said Rick Bischoff, director of Data Science at Allstate.

  • Accelerate employee onboarding: Data scientists spend very little time getting data or tools ready for experimentation in Domino, and they have the flexibility to use languages they’re most comfortable with. The intuitive interface is easy to use by employees across the organization, and encourages experimentation.

Trusted throughout the insurance industry.

Domino lets data science and analysis teams get insights faster, produce better risk models, meet auditing requirements, and reproduce past work. They use it for developing and deploying pricing models, loss models, model-driven claim insights, marketing models calculating customer value and churn predictions, delivering API-accessible risk models, and more.