Due to the comprehensiveness of data needed, the underwriting and claims processes are often frustrating and time-consuming for insurers. The underwriting process takes place before a policy is issued and approved, when the cost and risk of the insuree is assessed. The claims process occurs after the insuree has submitted a claim, and insurers must decide if the claim is covered and the payout amount.
Luckily, insurers can employ a number of data-powered tactics to streamline and simplify the underwriting and claims process. For example, insurers can use a variety of data points to improve risk assessment, which means they can analyze claims data more accurately to predict future risk. With accurate data, insurers can use modeling to estimate premiums and predict future demand for products. It’s easy to see why 66% of traditional insurers are reporting they invest in and adopt their own AI and technical solutions to streamline claims process.1
Here are three ways insurance companies can use data to transform the underwriting and claims process:
The underwriting process is so time-consuming because it requires a myriad of details, such as the policy holder’s income or revenue (if they’re a business), assets, debt, and property details that all need to be carefully verified and manually recorded before a policy is issued or a premium set. Human error causes around 20% of the data collected by agents or entered by policy holders being to be inaccurate.2 This is where external data and artificial intelligence (AI) come in. Third-party data and AI used together can help insurers automatically validate information provided by agents and customers and can also enable the pre-filling of application forms to cut down on time. For example, a third-party data provider, such as Data Axle, can supplement applicant data in real-time, and that verified data can be used to pre-populate the application.
Shorter underwriting times benefit both the insurer and their clients. Clients will have a smoother, more satisfactory customer experience if the underwriting process is as simple for them as possible.
Case study: A leading US P&C insurer reduces underwriting time with artificial intelligence
A leading US P&C insurer wanted to cut down underwriting times to increase efficiency and improve their bottom line. They used an AI-based underwriting platform to streamline the process of assessing new policies for existing clients. Before, the insurer was using multiple spreadsheets to juggle client information. The software helped them centralize their data and provided them with a 360-degree view of client accounts, including what policies the client currently held, which content for similar products they had downloaded and which emails they had opened or interacted with. This enabled the insurer to do three things – automate workflows, reduce human errors, and increase sales.
Having the data easily accessible and in one place allowed underwriting teams to verify client data and finish reviews faster. The software also assisted with streamlining workflows, rule-based underwriting review and seamless referral processing on complex accounts.
The single customer view also provided more information to agents, allowing them to pitch multi-line quotes on a single submission view. This helped agents identify what other products fit that customers’ needs so they could better upsell and cross-sell.
The results: the insurer reduced underwriting time by 60%.3
Data can also be used to identify high-risk prospects that require additional vetting before a policy cost is quoted. For example, as policy pricing requests come in, a third-party data provider can build out a more robust profile of the consumer, then run that profile through a model to assign a risk score to the prospect. If a prospect receives a low-risk score, the prospect can be moved forward through the pricing process. This analysis can potentially save carriers time and money by avoiding the need to run in-depth credit reports and shortening the vetting process.
Data and modeling can also help take the risk score of a prospect. This would significantly cut down on the amount of human interaction needed to assess risk, set premiums, and produce more accurate risk assessments in a shorter time span.
Case study: Progressive Insurance
Progressive insurance, a US-based auto and vehicle insurer, is known for their creativity. Progressive’s Snapshot®’ tool electronically records driving patterns for a vehicle. Providing valuable information that Progressive can use to adjust insurance premiums and estimate potential claims and claim payouts (more on this later). They have promised their clients that the data collected would only be used to lower rates, not raise them.4
Predictive technologies allow companies to use advanced analytics techniques, such as data modeling, statistical algorithms, and machine learning, to learn from past customer behaviors to predict future needs. This is especially helpful when insurers estimate the amounts they can expect to pay on claims. By looking at customers’ past claims, insurers can use predictive analytics to assess how often they would have to pay out claims and how much each claim would be. This enables them to predict how much they can expect to spend on claims.
Insurers can also use third-party data delivered through an Application Programming Interface (API) to append specific information from the policyholder. For example, as a claim comes in, a third-party data provider, through an API, could append specific home or vehicle information to verify the data. This technique can also be used to verify the data submitted by the other party involved in a car accident or other altercation.
Case study: Lemonade sets a world record with a 3-second claim payout turnaround
Insurer Lemonade set a world record by paying out a claim 3 seconds after the claim was filed.5 How did they turn this claim around so quickly? An AI-powered chatbot named Jim. In the case of the 3-second claim payout, the customer logged into the mobile Lemonade app on his phone and chatted with Jim about what had just happened (his expensive coat had been stolen). Jim then used an AI powered algorithm to instantaneously search the Lemonade database for descriptions of similar claims, which allowed the bot to determine if the claim was legitimate or not. When a claim is too complex, it is sent to a human for review. However, in the case of the stolen coat, Lemonade’s system was able to easily cross-reference his claim against his policy, run anti-fraud algorithms on it, approve it, calculate the payout, and wire it to the customer’s bank account.6
This is an extreme case, as Lemonade is an exceptionally digitally-savvy insurer. They claim to use bots instead of brokers and rely on algorithms instead of paperwork. But even if other insurers don’t immediately go all-in on this model, there’s still massive opportunity to get claims out to customers quickly and painlessly thus improving customer satisfaction and retention.
Image via Lemonade
Advancements in data and technology mean that insurers don’t need to struggle with antiquated underwriting and claim processes. Real-time data, supported by automated systems means easier access to crucial policy-holder information, shorter verification times, more accurate risk assessments and quicker claims payouts.
Insurers – we have more resources to help you utilize data to streamline processes and acquire high-lifetime value customers. Download our customer acquisition playbook or get in touch to learn how we can help.
As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.