Patent Issued for Social Media Data Aggregation To Optimize Underwriting (USPTO 10,776,878)

2020 SEP 30 (NewsRx) -- By a News Reporter-Staff News Editor at Sports Research Daily -- State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States) has been issued patent number 10,776,878, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Zeglin, Andrew Joseph (Normal, IL); Shull, Jessica Lynn (Bloomington, IL); Turrentine, David (Bloomington, IL); Breitweiser, Edward W. (Bloomington, IL); Magerkurth, Melinda Teresa (Utica, IL).

This patent was filed on June 29, 2017 and was published online on September 28, 2020.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Underwriting in the insurance industry often relies on voluntarily provided biographical information regarding a customer, followed by an assessment of risk based on past claim histories available to a particular company. Because underwriting processes and claim history data, containing personally identifiable information, confidential health information, and proprietary business processes, companies prudently protect and isolate such data. However, in the absence of a full-picture of a prospective customer including all of the available public and private data relevant to, an assessment of underwriting risk may result in substandard and inefficient determinations.

“Inaccurate risk assessments may impact availability or affordability of insurance coverage or financial services for some individuals and organizations. Thus, opening the aperture of available data used in underwriting risk assessments, while carefully controlling access to private data allows more accurate risk assessments, improved availability of services, and improving affordability of insurance coverage for users. For organizations providing insurance and financial services, improved risk assessments may increase an accessible customer pool, and provide a better perspective of long term of expected cash flows and lower discount rates associated with those cash flows.

“Social media data, both in the public and private space, provides an additional source of risk assessment as well as a variable in correlation to verify provided biographical data. For example, private or publically available social media, when aggregated may provide individual indicators or cumulative effect to assess a mortality risk. Such assessment of mortality risk may provide a threshold for underwriting a life insurance policy, or indicators of risky behavior may prevent underwriting of an auto-insurance policy.

“Aggregation engines may collate such private and public social media data on a large scale across a variety of social media outlets to provide a more comprehensive picture. These aggregation engines may require opting in with consent to collect private social media data on a voluntary or temporary basis, or may mine data from publicly available sources on a large scale. Opting to provide access to private social media may require a temporary login credentials, authorized by an account owner in accordance with social media outlet user terms of condition.

“Mining public data may require biographical information provided by a prospective customer to correlate with publically available data using a number of markers to establish an identity to a reasonable threshold. On a whole, whether privately or publicly available, the aggregation engine may generate an overall numeric score to compare against a risk assessment threshold. Calculation of the numeric score may relate to a number of variables weighted by the presence of social media activity markers, such as participation in sporting events, wellness initiatives, smoking, alcohol consumption, or other factors that may impact suitability for underwriting an insurance product.

“Traditional underwriting processes have typically included an interview between a providing organization and a prospective customer, where biographical information is provided, and an assessment of risk is performed. In some instances, this may be followed-up or preceded by a medical examination to verify or provide information required to perform a risk assessment for an insurance product. The recent availability of wearable device activity data may supplement or in some instances replace more traditional medical assessments in a less invasive and more convenient way. Such wearable data may exist in the public space, where the data may be mined by aggregation engines, or privately opted-in using social media outlets or provided otherwise electronically. This wearable activity data may likewise provide untraditional markers that correlate with provided biographical information or indicate lifestyle habits that suggest or prohibit underwriting insurance products.

“In some instances, a mere name and email address may provide sufficient identity information to publically mine available social media outlets to source activity data. Such data may act as a triage first step in an underwriting decision-making process, or fall later in the process to verify and confirm information provided directly by a customer. Still further, organizations may leverage publicly mined and opted-in private social media data to suggest or solicit additional services, or make changes to existing services. Such data may exist on a large scale and require significant computing resources to access, analyze, and generate suggested next steps or prospective product offerings.

“Exemplary embodiments may include public or private social media markers for lack of tobacco use allowing a savings for a customer, participation in sporting events such as 5K races or checking-in at sporting events or fitness centers to assess lowered underwriting risk for life insurance policies, etc. Still further, publicly available criminal records, or private credit assessments may indicate activities that prohibit or increase a risk to an underwriting decision for insurance or financial products. Alternatively, use of markers and indicator data may function as a correlation factor to provided data that strengthens or weakens a underwriting risk assessment once a prior assessment has been performed using traditional methods.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “One exemplary embodiment includes a computer-implemented method that includes retrieving a plurality of biographical data related to at least one individual, and providing at least one of the biographical data to a social media data aggregation engine. The method may include retrieving a plurality of social media activity scoring data from the social media data aggregation engine, retrieving a plurality of wearable device activity data related to the at least one individual, and calculating a credibility correlation between the biographical data, the social media activity scoring data, and the wearable device activity data. In some embodiments the method may include calculating an individual underwriting risk assessment score for the at least one individual that includes at least one future cash flow and at least one discount rate. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“Another exemplary embodiment includes a computer system that includes a computer processor, a memory device, a network device, a human-machine interface, and a display device. In some embodiments, the computer processor may be configured to retrieve a plurality of biographical data related to at least one individual using the human-machine interface and store the plurality of biographical data in the memory device and provide at least one of the biographical data to a social media data aggregation engine using the network device. In other embodiments the computer processor may be configured to retrieve a plurality of social media activity scoring data from the social media data aggregation engine using the network device and store the plurality of social media activity scoring data in the memory device and retrieve a plurality of wearable device activity data related to the at least one individual using the human-machine interface and store the plurality of wearable device activity data in the memory device. The computer processor may in some embodiments be configured to calculate a credibility correlation between the biographical data, the social media activity scoring data, and the wearable device activity data and calculate an individual underwriting risk assessment score for the at least one individual that includes at least one future cash flow and at least one discount rate.

“Exemplary embodiments may include computer-implemented methods that may in other embodiments include apparatus configured to implement the method, and/or non-transitory computer readable mediums comprising computer-executable instructions that cause a processor to perform the method.

“Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.”

The claims supplied by the inventors are:

“What is claimed is:

“1. A computer-implemented method, executed with a computer processor, comprising: retrieving a plurality of biographical data related to at least one individual; providing at least one of the biographical data to a social media data aggregation engine; retrieving a plurality of social media activity scoring data from the social media data aggregation engine; retrieving a plurality of wearable device activity data related to the at least one individual; calculating a credibility correlation between the biographical data, the social media activity scoring data, and the wearable device activity data; calculating an individual underwriting risk assessment score for the at least one individual that includes at least one future cash flow and at least one discount rate; determining services available with risk assessment and ability to bypass full underwriting process; analyzing risk assessment using a risk assessment algorithm; training, with the processor and by analyzing samples of the social media activity scoring data, the risk assessment algorithm to identify patterns; identifying a pattern using the risk assessment algorithm; and enabling the individual to bypass full underwriting activity based on the risk assessment analysis and the identified pattern.

“2. The method of claim 1, comprising calculating a price of a life insurance product using the at least one future cash flow and the at least one discount rate.

“3. The method of claim 2, comprising calculating an ability of an individual to bypass a full underwriting procedure using the price of the life insurance product, the at least one future cash flow, and the at least one discount rate.

“4. The method of claim 3, comprising: retrieving a plurality of biographical data related to a plurality of individuals; providing the plurality of biographical data related to the plurality of individuals to the social media data aggregation engine; and generating a list of prospective customers using the individual underwriting risk assessment score, calculated for at least one of the plurality of individuals.

“5. The method of claim 1, wherein the plurality of biographical data comprises at least one of an individual’s tobacco use, alcohol use, age, gender, criminal activity, creditworthiness, activity level, history of medical procedures, participation in wellness activities, and physical fitness.

“6. The method of claim 1, wherein the individual underwriting risk assessment score correlates to a mortality risk.

“7. The method of claim 1, wherein the social media aggregation engine comprises publicly available data related to the at least one individual.

“8. The method of claim 1, comprising: retrieving a consent to retrieve private social media data; retrieving a plurality of private social media activity scoring data from the social media data aggregation engine; and calculating the individual underwriting risk assessment score using at least one of the private social media activity scoring data.

“9. The method of claim 1, comprising retrieving the plurality of biographical data related to at least one individual from a human-machine interface that renders a plurality of interview questions on a display device.

“10. The method of claim 1, comprising retrieving a plurality of wearable device activity data from a wearable device that records an activity level of the least one individual.

“11. A computer system, comprising: a computer processor; a memory device; a network device; a human-machine interface; and a display device; wherein the computer processor is configured to: retrieve a plurality of biographical data related to at least one individual using the human-machine interface and store the plurality of biographical data in the memory device; provide at least one of the biographical data to a social media data aggregation engine using the network device; retrieve a plurality of social media activity scoring data from the social media data aggregation engine using the network device and store the plurality of social media activity scoring data in the memory device; retrieve a plurality of wearable device activity data related to the at least one individual using the human-machine interface and store the plurality of wearable device activity data in the memory device; calculate a credibility correlation between the biographical data, the social media activity scoring data, and the wearable device activity data; calculate an individual underwriting risk assessment score for the at least one individual that includes at least one future cash flow and at least one discount rate; determine services available with risk assessment and ability to bypass full underwriting process; analyze risk assessment using a risk assessment algorithm; train, by analyzing samples of the social media activity scoring data, the risk assessment algorithm to identify patterns; identify a pattern using the risk assessment algorithm; and enable the individual to bypass full underwriting activity based on the risk assessment analysis and the identified pattern.

“12. The system of claim 11, wherein the processor is configured to calculate a price of a life insurance product using the at least one future cash flow and the at least one discount rate.

“13. The system of claim 12, wherein the processor is configured to calculate an ability of an individual to bypass a full underwriting procedure using the price of the life insurance product, the at least one future cash flow, and the at least one discount rate.

“14. The system of claim 13, wherein the processor is configured to: retrieve a plurality of biographical data related to a plurality of individuals; provide the plurality of biographical data related to the plurality of individuals to the social media data aggregation engine; and generate a list of prospective customers using the individual underwriting risk assessment score, calculated for at least one of the plurality of individuals.

“15. The system of claim 11, wherein the plurality of biographical data comprises at least one of an individual’s tobacco use, alcohol use, age, gender, criminal activity, creditworthiness, activity level, history of medical procedures, participation in wellness activities, and physical fitness.

“16. The system of claim 11, wherein the individual underwriting risk assessment score correlates to a mortality risk.

“17. The system of claim 11, wherein the social media aggregation engine comprises publicly available data related to the at least one individual.

“18. The system of claim 11, wherein the processor is configured to: retrieve a consent to retrieve private social media data; retrieve a plurality of private social media activity scoring data from the social media data aggregation engine; and calculate the individual underwriting risk assessment score using at least one of the private social media activity scoring data.

“19. The system of claim 11, wherein the processor is configured to retrieve the plurality of biographical data related to at least one individual from the human-machine interface that renders a plurality of interview questions on the display device.

“20. The system of claim 11, wherein the processor is configured to retrieve a plurality of wearable device activity data from a wearable device that records an activity level of the least one individual.”

For the URL and additional information on this patent, see: Zeglin, Andrew Joseph; Shull, Jessica Lynn; Turrentine, David; Breitweiser, Edward W.; Magerkurth, Melinda Teresa. Social Media Data Aggregation To Optimize Underwriting. U.S. Patent Number 10,776,878, filed June 29, 2017, and published online on September 28, 2020. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=10,776,878.PN.&OS=PN/10,776,878RS=PN/10,776,878

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