AI For Insurance
AI is transforming the
Advances in AI offer insurers the promise of growth through innovative new products, cost reductions from new efficiencies, and risk mitigating thanks to improved analytics and modeling. But both established carriers and cloud-native insurtech startups face challenges in realizing AI’s full potential, even as AI regulations create new risks and costs.
Cross the AI Chasm
What does it mean to cross the AI chasm? Companies like Google and Amazon integrate AI into every customer experience and business process. In the insurance industry, incumbents and startups alike face challenges crossing this chasm:
- Established players that have focused their AI initiatives on supporting analytical (batch) use cases now need to enable and scale real-time capabilities.
- Insurtech startups have built tech stacks that make it easier to enable real-time use cases, but their challenge is one of scale: how to build their offerings and grow market share with lean operations supported by small ML teams.
Read Verta’s Guide to Operational AI for Insurance to learn how every insurer can cross the AI chasm successfully by adopting an Operational AI mindset.
Prepare for upcoming AI Regulations
The American Data Privacy and Protection Act (ADPPA) bill imposes new requirements (and creates new liabilities) for insurers and other companies that use machine learning and AI, including calling for “algorithm impact assessments” and “algorithm design evaluations.” Verta’s Model management tools help companies track and report on how their models were created, trained, deployed, and monitored — ensuring compliance and mitigating liabilities.
A Blueprint for an AI Bill of Rights
The White House Office of Science and Technology Policy (OSTP) has released a “Blueprint for an AI Bill of Rights,” which aims to address challenges posed by uses of “technology, data and automated systems” that could impinge on the rights of the American public.
Learn what makes Verta “cool”
In the Gartner Cool Vendor in AI Core Technologies report, Verta is recognized as a leading vendor in solutions for managing and scaling AI initiatives. Verta’s Model Catalog was highlighted for enabling the model reproducibility, traceability and auditability that are needed to comply with current and upcoming regulations. Insurance and fintech companies are increasingly turning to Verta to automate delivery, increase availability, scale workflows and ensure compliance in AI.
State of MLOps Research Report
The State of MLOps study from Verta Insights explores trends, best practices and opportunities shaping the industry — from model experimentation and deployment through real-time safe operations. Importantly, the study revealed differences in how successful organizations approach ML versus laggards.
Key topics include:
- Real-time explosion: Real-time use cases set for rapid growth
- Investment priorities: How companies are building their Operational AI tech stack
- Leaders vs Laggards: How successful companies are outperforming peers that have focused their AI initiatives on supporting analytical (batch) use cases now need to enable and scale real-time capabilities.
"Verta just works."
Data Science Leader
at Fortune 100 Insurance Company
Real customers, real results
Insurance companies rely on Verta’s Operational AI platform to power the machine learning built into their intelligent systems and apps. For example:
A Fortune 100 insurance company used Verta to streamline model deployment, reduce MLOps headcount by 75%, and gave them full visibility and control over their machine learning assets by consolidating multiple homegrown systems for model deployment.
A top-20 US auto insurance company is using Verta to enable real-time machine learning, which let the company launch its first-ever real-time AI applications.
Meet Verta’s founder
Manasi Vartak is the founder and CEO of Verta. While working on her Ph.D. at MIT CSAIL, Manasi created ModelDB, the first open-source model management system deployed at Fortune 500 companies to system to version machine learning models and track ML metadata across the model lifecycle. In addition to frequently speaking at AI/ML industry events and writing for industry publications, she frequently consults with companies seeking to accelerate the benefits of their ML initiatives.