The insurance industry boasts some of the most sophisticated modeling capabilities in the world. And yet the average property underwriter does not have access to the kind of predictive tools that carriers use at a portfolio level to manage risk aggregation, streamline reinsurance buying and optimize capitalization.
Detailed probabilistic models are employed on large and complex corporate and industrial portfolios. But underwriters of high-volume business are usually left to rate risks with only a partial view of the risk characteristics at individual locations, and without the help of models and other tools.
Many insurers invest in modeling post-bind in order to understand risk aggregation in their portfolios, but Ross Franklin, senior director of data product management at RMS, suggests this is too late. “From an underwriting standpoint, that’s after the horse has bolted — that insight is needed upfront when you are deciding whether to write and at what price.”
By not seeing the full picture, he explains, underwriters are often making decisions with a completely different view of risk from the portfolio managers in their own company. “Right now, there is a disconnect in the analytics used when risks are being underwritten and those used downstream as these same risks move through to the portfolio.”
Cut Off from the Insight
Historically, underwriters have struggled to access complete information that would allow them to better understand the risk characteristics at individual locations, manually gathering what risk information they can from various public- and private-sector sources. These solutions often deliver data via web portals or spreadsheets and reports — not into the underwriting systems they use every day.
Very few data providers burrow down into granular detail on individual risks. “Vulnerability is critical to accurate underwriting. Hazard alone is not enough. When you put building characteristics together with the hazard information, you form a deeper understanding of the vulnerability of a specific property to a particular hazard. For a given location, a five-story building built from reinforced concrete in the 1990s will naturally react very differently in a storm than a two-story wood-framed house built in 1964 — and yet current underwriting approaches often miss this distinction,” says Franklin.
In response to demand for change, RMS developed its Location Intelligence application programming interface (API), which allows pre-formatted RMS risk information to be easily distributed from its Risk Intelligence platform via the API into any third-party or in-house underwriting software. The technology gives underwriters access to key insights on their desktops, as well as informing fully automated risk screening and pricing algorithms.
The API allows underwriters to systematically evaluate the profitability of submissions, triage referrals to cat modeling teams more efficiently and tailor decision-making based on individual property characteristics. It can also be overlaid with third-party risk information.
“The emphasis of our latest product development has been to put rigorous cat peril risk analysis in the hands of users at the right points in the underwriting workflow,” says Franklin. “That’s a capability that doesn’t exist today on high-volume personal lines and SME business, for instance.”
Historically, underwriters of high-volume business have relied on actuarial analysis to inform technical pricing and risk ratings. “This analysis is not usually backed up by probabilistic modeling of hazard or vulnerability and, for expediency, risks are grouped into broad classes. The result is a loss of risk specificity,” says Franklin.
“As the data we are supplying derives from the same models that insurers use for their portfolio modeling, we are offering a fully connected-up, consistent view of risk across their property books, from inception through to reinsurance.”
While improved data resolution should drive better loss ratios and underwriting performance, automation can attack the expense ratio by stripping out manual processes, says Franklin. “Insurers want to focus their expensive, scarce underwriting resources on the things they do best – making qualitative expert judgments on more complex risks.”