by Element AI
Empower Underwriters to better serve customers through more timely and personalized service using AI
The commercial underwriting lifecycle — from submission to binding — is a complex process with many challenges and inefficiencies. A high volume of submissions rife with unstructured data can take high-skilled and expensive talent away from higher value-add work, stretching workforce capacity to focus on repetitive tasks to quote and bind. In the end, this can lead to suboptimal customer experience and potential business left on the table.
Element AI’s Underwriting Partner product streamlines underwriting decisions for leading property and casualty insurance companies by reducing processing costs and improving your ability to consistently and accurately assess risk, resulting in a healthier Combined Ratio.
Helping People Work Smarter
- Submissions: Digitize and structure data, including forms, documents, and more.
- Segmentation: Categorize applications rapidly, enabling auto-denials, while estimating processing time and complexity.
- Assignment: Optimize application assignment based on specialization and workload.
- Risk Assessment: Improve ability to assess risk by comparing applications quickly, supporting decision making and identifying anomalies
- Coverage Recommendations: Generate coverage recommendations based on application characteristics automatically.
- Message and Authority: Optimize message verification and authority level based on characteristics of messages. Element AI is Your End-to-End Partner for AI Transformation
- Advisory Services: Helping you get started in your AI journey, from business and data strategy to governance and people
- Technical Enablement: With a suite of enablement tools, ensuring that your data and technical infrastructure is ready to adopt AI at scale
- Onboarding: Helping you integrate and scale products through flexible deployment models (public cloud, private cloud, on-premises) and change management
- Support and Customer Success: Ensuring you receive ongoing support and the latest model learnings and developments