Commercial carriers must continue to grow profitably as rate increases slow and operating costs rise. But submissions scattered across emails, portals, and attachments slow the quoting process and pull underwriters into time-consuming follow-ups. To address this, carriers are streamlining and automating underwriting processes to quote faster, handle more volume without adding headcount, and apply underwriting expertise more consistently.
Digitizing risk is what makes this possible. Bringing risk data together from multiple sources provides a complete submission the first time, allowing carriers to prioritize the right submissions, reduce rework, and deliver a more seamless experience for distribution partners across the entire commercial lines policy lifecycle.
Why AI-supported Underwriting Leads to Profitable Growth
Act Faster with a Complete View of the Right Risks
Bring together data from across multiple channels into a single submission, providing a full view of the risk, enabling focus on the best opportunities, and binding profitable premium in hours, not days.
Build Stronger Agency Relationships
Reduce friction in agent collaboration by digitizing risk and automating the submission process, delivering faster, more predictable decisions and streamlined data exchange.
Reduce Operating Expenses at Scale
Eliminate manual data entry and submission triage by capturing and structuring risk data upfront, allowing routine submissions to be automatically assessed and routed. This reduces rework and underwriter touchpoints, enabling higher volumes to be processed with existing teams and lowering cost per submission as volume grows.
Maintain Control and Advantage
Standardize the foundation while keeping control of underwriting appetite, guidelines, and differentiation. Configure and evolve the system as conditions change, without relying on vendor releases or custom development.
What the Market Is Saying
When risk data moves through one consistent workflow, underwriting teams gain a complete, accurate view of risk that supports stronger selection, faster triage, and better decisions across the lifecycle.
End-to-end risk automation reduces friction for brokers, accelerates decisions, and enables market expansion without a proportional increase in cost or operational effort.
Digitized risk intake and streamlined new business workflows ensure brokers and clients experience predictable turnaround times and service quality, even as submission volume increases.
Standardized, digital risk processing improves risk discipline, expands underwriter capacity, and establishes a scalable foundation for long-term underwriting excellence.
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Frequently Asked Questions
Automated insurance underwriting is the use of artificial intelligence (AI), machine learning, and algorithms to assess risk, verify data, and price insurance policies through digital workflows. These systems reduce manual underwriting tasks and deliver faster, more consistent decisions – while improving accuracy and operational efficiency.
An automated underwriting system (AUS) is an insurance technology that uses artificial intelligence (AI), data models, and predefined rules to analyze applicant information and evaluate risk, price policies, and make underwriting decisions through digital workflows. By reducing manual review, an AUS speeds up applications, improves consistency, and increases operational efficiency.
Risk digitization in insurance is the process of transforming manual, document-heavy risk workflows into automated, data-driven digital processes. It uses technologies such as artificial intelligence (AI), cloud computing, Internet of Things (IoT) sensors, and other digital tools to capture, analyze, and manage risk information in real time, replacing static, paper-based, and historical methods.
Manual underwriting is a human-led review process used for exceptions, such as applicants with unique financial situations, non-traditional credit, or when an automated underwriting system (AUS) returns a “refer” recommendation. While it allows for judgment in edge cases, manual underwriting is slower and less consistent than automated underwriting, which uses artificial intelligence (AI) to evaluate standard data quickly, consistently, and at scale.
AI supports commercial lines risk automation and management by analyzing large volumes of structured and unstructured data in real time to enable faster, more accurate risk assessment and proactive risk mitigation. By automating underwriting, data extraction, and claims-related workflows, AI reduces manual effort, minimizes human error, and improves operational efficiency across the commercial insurance lifecycle.
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