Why Is Social Data Critical for Group Life and Disability Insurance Underwriting?
Insurance companies provide clients with financial protection and compensation if the risk insured occurs. One crucial function that helps life insurance carriers assume clients/businesses’ (underwriting of groups of people) risks is disability and life insurance underwriting with a well-defined risk selection process and fair pricing.
Just like with other industries, the adoption of big data and technological advancements are transforming the insurance industry. Today, insurance companies can use social data for many purposes, including pricing group life and disability products, providing insurance against bullying on social media, customer attrition, agent attrition, renewal premium forecasting, determining a customer’s lifetime value, customer segmentation, among other uses.
Previously, social media monitoring only came into play when the insured was suspected of fraud — a social media feed filled with enthralling rock climbing pictures of a policyholder who doesn’t usually engage in such sporting activities could raise some red flags. However, with AI speeding up the accumulation and verification of social data, social media feeds are gradually becoming part and parcel of the whole policy formulation process. Ahead, we’ll expound on the role of social data in life insurance underwriting.
How Do Insurance Companies Use Social Media for Underwriting?
In January 2019, New York transformed the insurance industry when it became the first state to provide guidance on how life insurers can use algorithms to comb through social media feeds to scrutinize an applicant’s risk. Since then, insurance companies across the US have begun to explore ways to adopt the use of social data in their underwriting process.
According to Mike Fitzgerald, a senior analyst at a Boston-based information technology consultancy firm, underwriters can use social data to identify any discrepancies with various policies. The speediness with which social data can be interpreted enables underwriters to alter the underwriting process from a fixed process that depends upon backward-facing information to a more active activity that relies upon near-time and real-time data. It helps insurers not only with the setting of premiums and the underwriting process, but it also helps them to better manage the risks.
It’s clear that social data provides insurers with useful insights which they can use to base their underwriting decisions. However, looking through social media data can be time-consuming, given its large volume. Using machine learning and artificial intelligence (AI) to analyze the social data for business insurance applications not only quickens the process but also enables underwriters to gain a more accurate picture of the risk that they are assuming. In short, insurers that use AI to analyze social data during the underwriting process can accurately predict the risk of the group business and set premiums that match the risk.
Useful Social Media Data in Disability and Life Insurance Underwriting
Social media data has been used for credit underwriting in the finance FinTech for a while and now insurance is beginning to embrace it. By combing through various social media platforms, insurers can gain useful insights about a business and use these insights to set the premiums.
However, insurers face an obstacle in applying social media data to underwriting, given that there are no social media guidelines for insurance agents to use when collecting data. That said, the issues with privacy and regulatory obscurity diminish when applying this data to group products (with businesses in the contract), rather than impacting individual premiums.
As social media users (employees of businesses in the contract) share pictures and affiliations, communicate with each other in public forums, or purchase items online, they leave behind valuable information about their lifestyle, preferences, and habits. Insurers can use this data in their underwriting processes such as the verification of an applicant’s financial status.
Social Data Eliminates Ethnic Discrimination from the Underwriting Process
One advantage that social media holds over traditional methods of collecting insurance data is that it is the only data source with access to ethnicity. Using social media data ensures that policy seekers are not turned down based on race, ethnicity, sexual orientation, or faith. As such, it is most compliant with regulatory regulations because it can demonstrate that it does not discriminate based on ethnicity/race.
How Lifestyle Analytics Can Improve Risk Scoring Methods and Models
Risk scoring enables insurance companies to understand their clients based on various risk factors. It also helps them anticipate the chances of the risks assured against occurring in the future. By using an applicant’s or policyholder’s social media information such as their lifestyle choices or habits, insurance companies can develop risk score metrics that they can use to predict the likelihood of the risk insured occurring.
Also, they can use various risk adjustment techniques to accurately calculate the premiums of various applicants. For instance, a business (applying for a group product) that has employees who are either obese, heavy smokers, or participants in adventurous sports such as rock climbing, as indicated in these employees’ social media posts, is likely to pay higher premiums than a business whose employees lead healthier and less risky lifestyles.
Insurance companies need to ensure that they consider various risk scoring models such as financial soundness, hobbies, health status, and age, among others, to increase the efficiency of their underwriting process. Carriers should also ensure that they thoroughly go through policyholders’ or applicants’ social media to ensure that they set adequate premiums based on an insured person’s risk score.
What Changes Can Lifestyle Analytics Bring to the Group Risk Scoring Process?
Insurance carriers looking to better manage risks should utilize social media data when making various decisions or undertaking tasks such as calculating premiums. By augmenting social media feeds with big data projects, carriers can gather vital information about members who work for an insured business that would otherwise take time to gather using traditional insurance data collection techniques.
Besides that, social media data analytics can give insurance companies a competitive edge, leading to lower claims, greater sales, and increased customer satisfaction. That said, insurers should be careful when collecting social media data lest they cross the creepy line and get on the wrong side of the law.
Among the benefits that carriers can accrue from using lifestyle analytics include:
- Accurate risk assessment: Insurers can continuously assess the risk of employees insured by expanding their analysis beyond an insured person’s behavior, as evidenced in their social media communications. They can check updates on employment status, family, and travel circumstances when reevaluating to renew a policy.
- Claim and fraud detection: Carriers can identify fraud activities by monitoring language, geolocation, and other data elements on social media to ascertain an insured person’s language in public communication.
- Personalized service and pricing: The analysis of social data can help insurers offer underwriting services and set premiums more efficiently. For instance, by basing life insurance on big data, insurers can take into account perceived habits and other information of a given client and use this data to determine the pricing model that not only profits the insurance company but also fits into the client’s budget.
In today’s dynamic insurance industry environment, the incorporation of social data in life insurance underwriting is essential to thwarting fraud cases, enhancing operational efficiency, and improving the profitability of an insurance firm. To achieve this feat, carriers need to partner with a top-notch analytics firm like Pilotbird. Our technology will enable you to use social data points to deliver value in critical parts of the insurance chain. When you are ready to harness the power of predictive analytics for insurance, contact Pilotbird for a free demo.
Chiang, F. (2014). Analysis of Insurance Underwriting Using Social Media Networking Data. International Journal of Academic Research and Reflection. Vol. 2, No 4. ISSN: 2309-0405. ANALYSIS-OF-INSURANCE-UNDERWRITING-USING-SOCIAL-MEDIA-NETWORKING-DATA.pdf (idpublications.org)
Experian Aite Data Report (2018). Alternative Data Across the Loan Life Cycle: How FinTech & Other Lenders Use It & Why. https://www.experian.com/assets/consumer-information/reports/Experian_Aite_AltDataReport_Final_120418.pdf?elqTrackId=7714eff9f5204e7ca8517e8966438157&elqaid=3910&elqat=2
Geospatial World. (2020, June 18). The top 5 technologies impacting the insurance industry. https://www.geospatialworld.net/blogs/the-top-5-technologies-impacting-the-insurance-industry/
Ha, Y. (2011, November 7). Underwriters May Soon Be Using Social Network Data. Insurance Journal. https://www.insurancejournal.com/magazines/mag-features/2011/11/07/223008.htm
Huddleston, C. (2021, March 26). How Life Insurance Companies Get Intel On You. Forbes Advisor. https://www.forbes.com/advisor/life-insurance/personal-data/
Scism, L. & Byron, E. (2019, March 18). Can a Facebook Post Make Your Insurance Cost More? The Wall Street Journal. https://archive.is/afziF
Weston, H. & Wells, B. (2020). Social Media as a Factor in Personal Injury Underwriting: Risk, Rate & Regulation. Journal of Insurance Article. https://content.naic.org/sites/default/files/inline-files/JIR-ZA-39-01-EL_0.pdf
Danise, A. (2021, July 19). The Best Life Insurance Companies Of 2021. Forbes Advisor. https://www.forbes.com/advisor/life-insurance/best-life-insurance-companies/
Nagpal, H. (2020, January 5). Data science use cases in Life Insurance. Analytics Vidhya. https://medium.com/analytics-vidhya/data-science-use-cases-in-life-insurance-8cfbfd8b2fff
Mani Kaur is an industry thought leader in the insurance industry with numerous articles on technology, innovation, and financial services.
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