Transform Underwriting to Improve Agility and Profitability

Transform Underwriting to Improve Agility and Profitability

As insurers navigate a rapidly changing macro environment and evolving customer expectations, the need to reimagine insurance has become more urgent than ever. One area that requires particular attention is underwriting, which is often less visible to customers than other functions in the insurance value chain. However, upgrading underwriting can have a significant impact on capturing new business and driving profitability.

Transformez la fonction de souscription de votre entreprise avec Underwrite-It

Transformez la fonction de souscription de votre entreprise avec Underwrite-It

Pour devenir véritablement agiles et atteindre leurs indicateurs clés de performance, les assureurs doivent transformer la souscription et l’intégrer entièrement à la tarification.

Earnix Price-It for Auto Finance

While there are multiple applications of AI-powered pricing analytics solutions in the auto lending industry, this eBook showcases some of the most common use cases that helped Earnix customers create cutting-edge, competitive pricing strategies and meet their spread targets and KPIs.

Earnix Model Accelerator

In the quest to be agile, insurers want to experiment and innovate with machine learning (ML) models in a mode that gives them maximum flexibility. They need an open platform such as Earnix that allows for mixing and matching the best models of all kinds – existing, new, or whatever the analytics team can dream up.

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A Guide to Unsecured Lending Growth

This Earnix guide takes a closer look at this emerging opportunity and offers six ways lenders can win more business and increase growth in unsecured personal lending – without losing sight of profitability.
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Improving the Effectiveness of Model Development and Deployment

In concert with Earnix Price-It, Model Accelerator provides a comprehensive, end-to-end model management workflow, including model building, orchestration, deployment, and monitoring.