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Workers’ compensation insurance for small businesses is a distinct niche with high stakes for both small business owners and insurers. Like with almost all insurance products for small businesses, insurers barely end up charging enough premium to cover their operating costs and expected losses, despite competitive pressures to provide a better customer experience and more comprehensive terms. Coupled with the highly regulated nature of this specific market, insurers writing workers’ comp are keenly interested in ways to stay profitable while providing the best coverage to business owners.

Given the unique circumstances around underwriting small businesses (e.g., poor data quality, consumer-like buying behaviors, disproportionately high cost of writing new business), it’s difficult for insurers to adapt traditional approaches to prospecting, underwriting, renewing, and managing small business policies. Still, while there’s been numerous advancements in artificial intelligence and machine learning, the adoption and integration of these tools into the insurance value chain has been incumbent upon internal resources at insurers and software providers to push forward.

Companies providing purpose-built software for insurance use cases have helped to improve the accessibility and ability to harness these technologies for insurers looking to stay ahead of the curve—these solutions can help enable insurers to move toward continuous digital underwriting approaches that speed up processing of policies and pare costs. Faster turnarounds improve the experience for small business owners and agents, while getting higher fidelity data on demand enhances risk selection and prospecting by quickly identifying good risks (and triaging worse risks for further review).

New Data Dynamics

These technology providers are changing the way insurers are interacting with data about an account across the lifecycle of a policy, and helping take advantage of the universe of available data out there today, ranging from:

• Non-obvious data: These advances can provide previously difficult-to-attain information, data that the industry may have disregarded before, or data that simply wasn’t available, offloading the burden of data collection to sophisticated machine learning algorithms that scour the web. Examples include foot traffic around a local business, proximity to nearby hospitals, customer satisfaction and sentiment, among others.
• Data on demand: To meet the needs of self-service delivery models, quoting decisions increasingly need to be made in real-time. While there’s a trade-off between perceived decision accuracy and speed, access to meaningful, company-specific data within seconds can further enable automation of the underwriting process.
• Historical trending and peer benchmarking: Moving beyond the physical ability to collect certain information ,such as accessing decades of historical data or comparing a business against its peers across hundreds of characteristics it’s non-trivial and often impossible to get these types of valuable data via web queries or manual research.
• Proprietary data: As technological advances slowly become commoditized and open-sourced, the value of proprietary data will be more and more important. Services that provide proprietary company data or those that leverage claims for training models (through partnerships between insurers and providers), will provide deeper and more uniquely differentiated insights.

Yet, simply collecting data alone is insufficient to bring immediate benefit to the insurance value chain. Cutting-edge analytics tools are needed to process the mountains of data with high fidelity and consistency, and associate data to the right entities. Robust data processing and data management capabilities transform raw data into valuable insights that can then be used by insurers to make faster, more intelligent business decisions, expand coverage to new industries and regions, and generate a competitive advantage in the SMB marketplace.

Demand for data will continue to rise as risks grow more complex. In other P&C lines, climate change has impacted insurers who are now challenged by dramatic rises in property damage from more severe storms, wildfires and floods—insurers are increasingly exploring and using nonconventional data sources that help them assess such potential risks, and data providers are moving to address that need.

Data at Work

Additional data, especially non-obvious data, is helping underwriters and actuaries make more informed decisions. With tools that deliver this information, underwriters are no longer constrained to manual inputs from application forms or manually searching for information.

Tools and data capabilities that affect the entirety of the policy lifecycle include:

• Prospecting: By capturing a wealth of company information using just a name and address, underwriters and agents can refine their segmentation. This lets insurers find accounts that are more likely to bind, focusing their marketing and lead generation efforts before ever receiving any information via submissions from the businesses themselves.
• Risk selection & underwriting streamlining: With access to a wide breadth of risk factors or datapoints, insurers can streamline underwriting so that it’s a low-touch or automated process by instituting rules based on key characteristics of each These range from the number of hours a business is open, to any previous workplace safety violations, to whether the SMB uses its own delivery vehicles.

Alternatively, insurers can also leverage data to reduce friction in the buying experience by minimizing the number of questions a business owner or agent fills out, and cross-referencing responses to see if anything has been misclassified or if a mistake occurred.

• Pricing: Actuaries can access data with much higher coverage, fidelity, and accuracy, without needing to pull that information from paper applications sent by policyholders or agents. This lets actuaries build pricing models that no longer must work with scattered datasets, while identifying more differentiation between similar risks.
• Renewals: On-demand risk assessments and data collection can enable an insurer to monitor the health and risk of a business throughout the policy term. Renewals can further be streamlined by reducing the amount of ‘fresh’ information needed to make a decision.

The responsibility of a workers’ compensation insurer is to help small businesses take care of their people. With ongoing advancements in data and analytics, insurers can continue to offer more competitive, compelling, and customized products to the market, passing on value created in the process to those insureds.

AI and New Technology Adoption in Commercial Lines and Workers Comp