Artificial intelligence is accelerating each component of underwriting for optimal decision-making at every level
There is a reason that all the top 25 insurance carriers are using at least one artificial intelligence (AI) application in some form or have an in-flight AI development initiative in progress. The cost of not using AI in insurance underwriting is too high for insurance companies to not have an AI roadmap.
Legacy workflows are simply less efficient.
Without AI, underwriting processes are manual and thus susceptible to human error. It can also be overwhelming and time-consuming to get all the data you need for a full understanding of the risk on every property without AI processing and analyzing all datasets.
Often, without AI in insurance underwriting, underwriters must go to different sources for each different piece of information they need about a property to perform a proper risk analysis.
With the increasing number of insurance carriers leveraging AI from insurtech companies in their underwriting workflows, introducing AI into underwriting will soon make the difference between staying in business and becoming insolvent.
Learn More About AI In Property Insurance
Underwriting and AI At a Glance
Underwriting is a key early-stage workflow in the property insurance policy lifecycle. The process of evaluating insurance applications and issuing policies on properties is a crucial step; after all, the precision of a carrier’s underwriting determines the company’s potential to make a profit.
Underwriting requires good data to effectively support multiple layers of decision-making, and artificial intelligence can play a pivotal role in driving underwriting precision.
For each step of the underwriting process, AI can assist underwriters in doing their jobs more accurately and quickly. Stages where professionals can use AI in underwriting include:
- Application review
- Property valuation
- Risk assessment
- Decision-making (on whether to issue a policy)
- Policy issuance
- Policy renewal
However, AI solutions must learn from and process good data in order to work successfully.
Using AI in Underwriting to Understand Risk
The main purpose of underwriting is evaluating risk to understand the potential of financial loss if a property suffers damage. AI solutions help underwriters evaluate risk so that they make more informed decisions surrounding the structural, location-based, and occupant-driven risks of every property.
In turn, underwriters can make more confident decisions about whether to absorb a property into company portfolios and at what rate. AI technology also helps underwriters make decisions on policy renewal opportunities.
“Carriers should leverage AI capabilities to better select properties based on their risk appetite and improve underwriting efficiency,” JJ Jagannathan, VP of Product Management for CoreLogic Insurance Solutions, explained.
“Imagery-based AI solutions help better quantify property characteristics and condition hazards to get the insurance-to-value ratio and condition risk assessment right. Generative AI capabilities help drive more underwriter productivity. The real magic happens when these two types of AI capabilities are seamlessly delivered through a modern underwriting platform that can automate the decisioning process.”
In addition to traditional AI capabilities, generative AI in insurance underwriting is becoming increasingly common. Generative AI models learn patterns and structures from existing data to create new, original outputs as text, image, code, or music, among other formats.
What Is AI?
AI is the inherent ability of a machine, computer, or software platform to mimic human logic and thought patterns to take actions and initiate workflows based on large quantities of data.
AI Solutions for Property Underwriting
1. A popular AI technology solution in underwriting is data collection prefill. The AI in prefill solutions analyzes estimates to understand the intelligence they need and then mines a database to provide the appropriate data points for each field of commercial or residential property estimates. Prefilling data helps underwriters with application review by accelerating their ability to make proper decisions about policies and premiums when they review applications.
2. Another AI use case that is growing in popularity is risk assessments for specific hazard scenarios. For example, CoreLogic has AI-driven models that determine risk scores for properties, with the scores indicating the likelihood of a property being damaged in the event of certain types of natural or manmade disasters. These AI-driven risk scoring models process all the relevant datasets on properties to calculate an objective, simple-to-understand data point using a score on a scale of 0.01 to 100.
3. There are also virtual surveys that leverage AI to process the most current property data, including aerial imagery, so that underwriters can conduct inspections remotely and alleviate the time and hassle of an onsite inspection. This enables underwriters to make more data-driven decisions in an expedited manner. It also minimizes policyholder non-disclosure for those who may not self-report certain additions to their properties that would increase the risk of damage to their homes. Examples of these often-unreported changes are pool installations or home reconstruction projects that expand surface area. Virtual surveys can also reveal current hazardous conditions that may affect the probability of a homeowner making a claim.
While these are just a few types of AI-based solutions that touch the underwriting process, the pace of innovation will certainly lead to the development of other solutions in the years to come.
Succeeding With AI in Insurance Underwriting
While AI underwriting can dramatically improve outcomes for insurers, carriers must leverage high-quality data to build their in-house AI solutions. This means, carriers must pay attention to how their data is sourced and curated, working only with data and technology vendors that have superior data management practices and who have developed industry domain expertise through proprietary research and primary data collection.
Also, to use AI successfully, you need experts to validate and provide oversight into the decisions that AI solutions make. Involving humans is the only way to ensure that AI performs the right way in insurance and underwriting workflows. This is a major component to ensuring that your organization uses AI ethically.
Finally, you need your compliance department involved in implementing AI models into your digital infrastructure to validate that AI technologies won’t lead to biased underwriting decision-making.
Build an AI Roadmap for Your Underwriting Function
AI is a force that can transform and optimize underwriting processes for carriers of all sizes and specialties. It enables underwriters to better understand risk so that they make decisions that further their company’s strategy.
Don’t get left behind in incorporating AI into your underwriting workflows. Build an AI underwriting roadmap to more quickly and comprehensively understand the multiple risk dimensions of every property. By building a comprehensive view of risk, you can more efficiently build your ideal portfolio.
AI is a technology that will only grow in influence. It’s time to build a plan for incorporating it into your underwriting processes so that you can maximize your organizational resiliency.
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