Underwriter capacity is a scarce resource resulting in dropped submissions, rushed quotes, inefficient allocation of resource, and a drag on profitable business growth. Underwriting capacity is difficult to scale; adding skilled underwriters is expensive, time consuming, and carries the risk that more profitable growth won’t be delivered. Instead, insurers can get more out of their existing underwriting capacity by using tools to prioritize the best submissions and line up the right actions. The approach incorporates experienced management priorities, underwriter judgment and analytic models.
For insurers with a focus on profitable growth, this approach could boost the new business book by 10-20% by increasing bind rates and lower new business loss costs by 1-8% by directing underwriters away from less profitable risks.
Illuminating the prioritization and triage process
For many insurers, triaging is a manual, unsystematic process that relies heavily on first in-first out, “squeaky wheel” responsiveness to brokers, and underwriter judgment. This process is time consuming, inconsistent, and difficult to align with business priorities. Further, tasks may take time to get to the right resource, forcing a misalignment of activities and skillsets as experienced resources spend time on simple file preparation tasks, increasing the time to quote or decline.
Underwriters need a better way to prioritize their attention on the submissions that have the highest probability of being written and retained while also being profitable. Prioritization drivers can include:
- Probability of Bind
- Estimated premium size
- Estimated profitability
- Underwriting effort
- Renewal probability
- Cross sell opportunities
- Agency relationship quality
For many firms, the fact that the current triage process does not take these drivers into account means that insurers are missing out on some of the best opportunities, distribution is not managed as effectively as it could be, and underwriters spend their time on less than optimal activities.
However, a more effective triage process can be built using an automated rule based approach that is supported by analytics and technology. Automation reduces the time spent by underwriting team members on prioritizing and assigning submissions; a rules based approach aligns the prioritization with the correct drivers dictated by business needs; the practical use of analytics ensures that the rules will be effective; and the implementation of better technology allows underwriters to focus on the most relevant risks associated with each submission.
Building a more intelligent triage process
Many insurers have been building loss ratio models as guidance to underwriters, but leaving other business priorities aside. An improved triaging approach takes input from multiple source models for estimating bind probability, premium size, and profitability as well as non-modeled characteristics like agency status or cross-sell opportunity.
The first step is to develop a prioritization score based on the analysis of the model results along with input from experienced managers. By developing a set of test cases and using management input to rank a final integrated scoring scheme, managers can build a prioritization methodology that orders the sample risks according to business priorities.
This process allows management to make trade-offs in ways that were unavailable before – how many risks should be reviewed from poorly performing agents; how good does a risk have to be if it is in a higher aggregation zone. Tools should allow managers to adjust these priorities over time without a significant new modeling process with the ability to increase or lower scores based on state, class, etc.
Optimizing the triage process requires more than determining how to sort risks from low to high priority – a good triaging approach should orchestrate the next right response by immediately getting the right submissions to the most appropriate resource and determining the next steps. Analytics and business rules can be used assign activities to the right underwriting resources based on expertise, cost, and availability including file preparation by an assistant underwriter, geocoding, class code determination, and report ordering. High scoring risks can be immediately sent to an underwriting assistant for file prep; risks with incompatible characteristics (e.g., large building with low TIV) can be sent for specific review.
Displaying the submission ‘strike zone’
Displaying the right information to underwriters can be critical for guiding the underwriters to the right action for an individual risk. Multiple data points can be displayed graphically, allowing the underwriter to get a quick picture of why the score came the way it did and what it means for the next action.
For example, the below illustration contains multiple easy to read data points that can help an underwriter assess next steps:
- The low estimated success rate can tell an underwriter to work with the agent before undergoing a potentially wasted quote process
- The excellent profitability for the class indicates that a lower level of investigation may be required
- The size of the circle shows higher TIV, an early indicator of premium size
- The green in the circle indicates the catastrophe aggregation level are within acceptable ranges
Such charts can be configured according to data availability and business need, communicating the right information at a glance to get the right actions from underwriters in a time efficient fashion.
The benefits of intelligent triage
The benefits of intelligent triage can best be illustrated when taken in context of an actual business scenario. In the first scenario, a specialty carrier uses intelligent triage to capture growth while in the second, a multi-line business insurer uses it to address reduced margins; a third scenario balances these two priorities.
Scenario 1: Growth need
Situation: A specialty carrier has a fixed number of experienced underwriters, and a high number of incoming submissions. Less than 50% of incoming submissions are actively reviewed by an underwriter, 20% are quoted, and only 3% of incoming submissions are actively bound; on many submissions the broker never receives any response at all. Overall, the book is profitable, leading to an emphasis on growth. Agency relationships are also critical in order to ensure a steady flow of submissions.
Solution: Improved triage models can target opportunities based on characteristics available at the earliest stages of submissions, targeting the agents, geographies, and business types that have the highest probability of success if quoted. Agency characteristics can be used as well to ensure that larger, higher quality agency relationships are rewarded with higher prioritization in the queue. Studies of insurance company data indicate a possible lift in new business premium of 10-20% without any increase in underwriting effort.
Scenario 2: Profitability pressure
Situation: A multi-line business insurance carrier has experienced a reduction in profitability, and is worried about excessive exposure in catastrophe prone areas. Growth pressure also remains, but is a lower priority.
Solution: A better triage model can incorporate total account profitability as a primary driver, with additional business rules that penalize areas that have excessive exposure while also giving increased prioritization to higher premium and bind ratio risks. Estimates with typical loss cost models show that denying or focusing underwriting scrutiny on the least likely to be profitable could lower loss costs of new business by 4-8%.
Scenario 3: Complex trade-offs
Situation: A property insurance line within a larger business insurance carrier has to manage complex trade-offs: profitability, growth, aggregations, agency relationships, and growing new business classes and territories.
Solution: Models are built around win ratio and loss costs, and the output of those models is put in front of the underwriting management team together with other vital characteristics. The management team prioritizes the risks, making the complex trade-offs that underwriters have to make every day; that sorted list can then be used to build an over-arching model that incorporates those trade-offs. Managers are also given tools to adjust those trade-offs; if a portion the book is growing too fast for comfort (e.g., too much in a new class code with limited experience) the models can be tuned to a lower prioritization in real time.
The resulting output will show trade-offs; sometimes a risk will be highly prioritized despite being in an area with higher aggregations, because everything else is in line – important agent, highly profitable, and with a good likelihood of success.
In the above scenarios a triage model was able to solve critical business needs without requiring additional underwriting resources. By using more intelligent triage insurers are able to capture the additional revenue and profits that their distribution is already providing, but which their operating models are unable to process. The application of triage tools helps build efficiencies in one part of the underwriting value chain and represents one of the best options insurers have to improve both growth and profitability while keeping their operations lean.