The AI-and data-empowered underwriter: 7 ways to boost commercial underwriting capabilities
Carriers that embrace AI can see enhanced underwriting results and significant growth.
Imagine a streamlined commercial underwriting process free of persistent phone calls and extensive fact-finding. Imagine much less time spent searching for business and exposure details to develop an accurate quote. This can be a reality with the integration of cognitive technology, which incorporates AI tools and techniques to deliver robust data for classification and risk evaluation. Just as policyholders demand more accurate policy, coverage, and pricing information quickly and virtually, intelligent automation is helping to modernize outdated risk-assessment methods.
Increasingly, the rise of AI-empowered underwriting is a reality for broad-minded insurance organizations. By combining leading-edge technology and precise, transparent data sourcing with underwriters’ expertise, the process can become more efficient, policies can be quoted and issued faster, and the evaluation of routine risks can be further automated to boost productivity. Carriers that embrace the AI-driven transformation of underwriting are seeing significant growth.
Technology-empowered underwriting processes — and particularly AI-enabled data availability for risk intelligence — offer numerous benefits for carriers and policyholders alike. The use of transparent data sources and improved access to both structured and unstructured data allow for more complete risk analysis. Technology galvanizes underwriters to evaluate diverse and emerging risks with confidence, expand risk appetites, and increase profitability. By investing in training, carriers can ensure their underwriters are adept and confident with AI-sourced risk and exposure data, leading to more efficient underwriting outcomes.
Here are seven ways carriers can further enhance tech and AI empowerment to reinforce underwriters:
- Make key information easy to access through the implementation of a unified underwriting workbench. Leading platforms, typically powered by AI and data analytics, can marshal all necessary risk information about insureds, including operational, property, and location details. This AI-powered technology can automate processes to save time and increase productivity for all facets of complex underwriting workflows. Such platforms can incorporate AI capabilities for data acquisition, so underwriters can boost visibility into potentially numerous exposures and risk factors when quoting commercial policies.
- Cast a wide net by leveraging and collating varied data sources beyond historical information, including near real-time structured and unstructured sources such as government databases, web information, and social media. AI can consolidate information and present results back in a productive and dynamic view, allowing underwriting to spend less time researching and more time analyzing results.
- Put a premium on transparency by using technology that openly displays its data sources so underwriters can have considerable confidence in their decisions. Opening the proverbial black box of data sourcing, especially including access to a variety of data elements, is critical for underwriters to easily check data validity and confirm classification and exposure accuracy for pricing and quoting. Underwriters and company management will also appreciate transparency as a vital factor in regulatory compliance, and particularly the evolving data privacy parameters.
- Don’t overlook the small-data problem by using technology that can find and correlate information from various sources to provide a much clearer risk profile to underwriters. Depending on the commercial market focus, this can be a big differentiator. For example, there is much data available on midmarket businesses, as well as those in the top end of the small commercial segment. But for insurance organizations that specialize in small commercial or micro-businesses (or wish to enter this market), accurate classification and risk information is often constrained and spread across a wide spectrum of sources. AI-empowered data access can increase visibility into nuanced risk-qualification factors for in-appetite small and micro businesses.
- Keep decision-making in the underwriter’s hands by automating specific risks. Technology isn’t about replacing underwriters, it’s about making them more effective. Underwriting is both an art and a science, requiring analytic skills, underwriting expertise, and general insurance workflow experience especially for complex and emerging risks — all of which can be augmented with the right combinations of structured and unstructured data. Work with the underwriting team to determine which data assets and technology tools are most useful, and which workflows for smart risk assessment can and should be more automated.
- Invest in training to boost skills and confidence within the underwriting team to leverage AI-driven technology, especially for data that feeds and promotes proficient risk evaluation. Invest in developing and demonstrating real use cases so everyone can see the technology in action. Training should never be once and done. Technology and data mastery priorities need ongoing support, allowing the underwriting team to stay sharp and assured in the functional tools and assets of modern underwriting.
- Know that trust takes time through process evaluation and real-life testing, underscoring the importance of ongoing training and feedback with the underwriting team. Straight-through processing and automation don’t materialize productively overnight. Frequently, iterative collaboration and validation among underwriting team members is necessary to finetune optimal outcomes. The team needs to ensure exposure information is accurate and transparent regarding the sources of data, so the combinations of data assets and platforms aren’t affirming bad risks.
The fast and continuous progress of technology, coupled with the increasing prevalence of rich data about classes of business and their associated risk factors, allows carriers with AI- and data-empowered underwriters to forge ahead in both market share and profits, while those relying heavily on manual underwriting are more likely to fall behind.
New risks will continue to emerge, and the pressures of competitive differentiation will continue to press carriers to accelerate digital transformation. Likewise, to accurately assess risks, underwriting expertise and access to useful, transparent data will need to keep pace. Investments in technology and quality data energize underwriters to work faster and with more precision, leading to productive outcomes for carriers and their policyholders.
Sathish Kumar Manimuthu serves as the CTO at NeuralMetrics, an insurtech offering AI-powered risk-assessment intelligence for P&C carriers, brokers, and agents. With experience in launching innovative technology and products for startups and Fortune 500 companies, Sathish is responsible for the company’s suite of data-delivery engines and AI models.
Opinions expressed here are the author’s own.
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