Leading insurers have automated underwriting for small commercial lines by applying new data sources and advanced analytics, following the successful automation of personal lines. While few disclose underwriting automation, we see online quoting offered by InsurTechs like Next or CoverWallet and established brands including Allstate, Farmers Insurance and Chubb.

Yet, the landscape changes dramatically when underwriting activities move up to habitational and midsize property underwriting, with risks ranging from a small townhouse community to corporate entities with multiple offices and warehouses. Because commercial property risks are far less homogeneous, they require varied levels of underwriting expertise and risk engineering. For instance, an apartment building in a bustling city demands different insights and underwriting scrutiny than a manufacturing facility in a rural area.

To state the obvious, certain large commercial properties like oil refineries or airports will continue to require expert risk engineering and specialized underwriting. The opportunity lies in effective segmentation and advanced analytics to automate what can be automated to lower expenses.

Segmenting Property Submissions for Automation Opportunities

Segmenting property submissions and identifying automation opportunities is crucial, given the broad spectrum of commercial properties. For instance, apartment buildings, franchises and small businesses are potential automation targets due to their large number of comparable risks. Before diving into property risk, let’s review typical commercial property risks.

• Residential/Habitational: Multi-structure properties for residential living; typical examples include housing developments, multi-family apartment buildings and senior housing complexes.
• Small Business: Main Street or “mom and pop” business locations and property covered by business’s owner policy (BOP) packages.
• Middle-Market Business: Office campuses, warehouses or universities.
• Large Commercial: Oil refineries, airports and infrastructure, typically requiring onsite inspections, risk engineering and expert underwriting.

Case Studies: Automation in Action

Two case studies illustrate the potential positive impact of automation on commercial property underwriting: habitational properties and restaurant franchises. Habitational risks include real estate portfolios bundling thousands of rentals into a single landlord policy, or restaurant franchises with hundreds of locations. Because housing developments and franchises comprise many comparable units, they share risk characteristics that can be identified and analyzed at a portfolio level. These more commodified portfolios also face greater pressure on the expense side, including rising reinsurance costs.

Automated Insights for Commercial Residential Properties

Commercial residential risk runs from a five-unit townhouse to a 200-building retirement community. In practical terms, this means there is no standard underwriting submission form: individual brokers and agents prepare the statement of value (SOV) submission differently.

The SOV can be a spreadsheet showing just one address as the anchor for a housing development or senior community that includes dozens of residences. If the SOV submission says there are five buildings but provides only one street address, the underwriter will likely pull up a map and look for five structures that probably belong together. What’s straightforward for five buildings quickly becomes time-consuming when there are dozens, even hundreds, of residential buildings. Either way, this is a manual step requiring a human.

Fortunately, it’s now possible to use advanced analytics, including AI, to ingest unstructured documents and to enhance strategic data prefill. Advanced property mapping technology can resolve issues with nonstandard submissions by analyzing the relationships between building appearance and drawing likely property boundaries. The underwriter can type in a single address, receive every address or structure of interest to that property portfolio, and then make adjustments as warranted. By eliminating tedious tasks previously done by a human, the mapping tool starts the automation process at submission.

Advanced Algorithms and AI-Driven Insights for Restaurant Franchises

By the typical measures of client revenue, employee count and premium size, a submission for 100-plus fast-food franchise locations is a mid-market insurance risk. Yet, while these locations share many risk characteristics, underwriters typically can’t look at hundreds of locations individually to analyze potential concerns.

Risks like restaurant franchises benefit immensely from data-driven automation. By analyzing data on a portfolio level and teasing out anomalies, advanced analytics can uncover risks such as unsafe parking lot conditions or significant roof damage. They also can direct underwriters’ attention to specific properties where inspections or additional inquiries are merited, reduce operational costs and address underwriting gaps.

Challenges and Opportunities in Automating Commercial Property Underwriting

As leading insurers seek to emphasize commercial property, progress on underwriting automation is timely. “We prefer to put our emphasis on the property insurance business. And the spread of risk we’re getting, we’ve never seen better pricing and better risk-adjusted returns than we see right now in large account, E&S [and] middle market,” said Evan G. Greenberg, executive chairman and CEO, Chubb Limited. (Source: Q2 2023 Chubb Ltd Earnings Call, July 26, 2023)

With its complex nature, commercial property underwriting calls for a nuanced approach to automation. By recognizing the diverse segments within commercial property and teasing out more uniform risks, insurers can effectively allocate human resources, maximizing automation where it’s most beneficial and devoting expert attention where risk complexities dictate. The journey toward automating commercial property underwriting is a testament to the industry’s resolve to innovate, paving the way for a more efficient, consistent and profitable future.