The R&D Tax Credit Aspects of AI in the Insurance Industry

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        Artificial intelligence (AI) has the unique ability to process mass amounts of structured and unstructured data—a task that is unparalleled by the human brain. Since over 90% of the world’s information was created only recently from the volume of information stemming from intelligent, enhanced computer systems, various industries are adopting AI as a means to tap into this data source and gain a competitive advantage.   

Other Industries that Use Artificial Intelligence

        Hedge funds rely on artificially intelligent, advanced machine learning algorithms to predict trends, produce client-specific responses, suggest future plans, and display reports in visually appealing layouts. For example, the leading hedge fund manager, Bridgewater Associates, invested in an AI unit to make predictions based on historical data and probabilities.  The Healthcare industry also implements AI to determine treatments and foresee sickness before a doctor can. Studies indicate that in terms of diagnosing lung cancer, AI’s success rate is an astounding 90% in comparison to the 50% of human doctors.

        After much success in the financial and healthcare sectors, the insurance industry is considering AI adoption as a means to estimate risk associated with individual clients and enhance claims processing. Now, insurers are subject to federal and state tax credits made available to stimulate efforts in AI research, development, and adoption.

The Research & Development Tax Credit

        Enacted in 1981, the federal Research and Development (R&D) Tax Credit allows a credit of up to 13 percent of eligible spending for new and improved products and processes. Qualified research must meet the following four criteria:

  • New or improved products, processes, or software
  • Technological in nature
  • Elimination of uncertainty
  • Process of experimentation

        Eligible costs include employee wages, cost of supplies, cost of testing, contract research expenses, and costs associated with developing a patent. On December 18, 2015 President Obama signed the bill making the R&D Tax Credit permanent.  Beginning in 2016, the R&D credit can be used to offset Alternative Minimum Tax and startup businesses can utilize the credit against $250,000 per year in payroll taxes.

Development of AI in the Insurance Industry

        One of the first questions is whether AI would be beneficial or detrimental to the insurance industry. Insurance companies are convinced that AI will aid them in enhancing their data-processing capabilities, determining risk, and predicting the future of claims and complaints. These tasks can be accomplished more efficiently and arguably more accurately than with a human. The time and resources saved from AI integration would be unprecedented in the insurance industry. According to the Financial Times, “AI was one of the most popular themes in insurance tech investments in 2016, capturing more than $500m of funds.”  These investments are intended to provide numerous benefits for the industry, namely in cutting time and costs in claims processing.   

Improving Claims Processing

        Artificial intelligence would play an integral role in enhancing claims processing. Similar to its application in hedge funds, robo-advisors can benefit insurers in predicting claim durations. This is made possible by tapping into historic data about previous claims. Strategic planning is also more effective when implementing predictive analytics. The historic data sets a good precedent for determining costs of specific claims. An insurer can derive prices for a new claim based on a previous claim of similar magnitude. Without AI, this is still possible, however it would take the insurer a considerably longer time to find a correlative claim. This undoubtedly increases costs in time, profit, and resource management.

        AI’s relationship with historic data has positive impacts on other aspects of the insurance industry. Its predictive capabilities help anticipate repeat claims on an individual client basis. An AI system would conduct an in-depth review of a claimant’s personal information and previous claims history.  In so doing, the insurer can cater to the specific needs of a client in order to prevent the repeat claims from occurring in the first place. Furthermore, with all these predictive capabilities of AI, the insurer can discern the next best actions to address a claim.

        The most intrinsic way that AI will benefit insurers is through the elimination of repetitive, manual tasks, most notably in linking claims with client profiles. This enhances end-to-end automation, with a trickledown effect in synthesizing valuable information instantaneously, decreasing a claims cycle duration, improving customer satisfaction, and diminishing overall operational costs.   

        Customer satisfaction, one of the most pivotal aspects for success in an organization, is more challenging to maintain in the insurance industry. As long as insurance companies continue to maintain manual and repetitive human-driven processes, closing the gap between customer expectations and actual experiences will be a significant roadblock. AI algorithms are expected to be 15 times more productive in accomplishing repetitive tasks than the average human, which is promising should insurance companies pursue revamping their services to better meet the needs of their client base.

AI Predictive Analytics Assess Risk Associated to Individual Customers

        Several factors are under consideration when determining how to insure a customer who is considered risky. For example, accidental and unintentional loss is examined, but quantifying it is more resource-consuming in the absence of predictive analytics. Another factor that would be facilitated by AI is determining mutuality of the claim.6 Drawing a conclusion that there is a large number of similar exposure units, thus implying a different premium rate, is a timely process that, with the assistance of artificial intelligence and predictive analytics, can be resolved within a matter of minutes.

        Since AI can generate detailed client profiles almost instantaneously, the base of insured units can be scaled down. Without the assistance of a computer system, an insurance company, subject to human error, may incorrectly associate clients with exposure units that do not actually fit their specific needs, gender, age, or other provisions required by a state’s Department of Insurance. Thus, AI can benefit insurers in creating a base of exposure units that are thorough, precise, and accountable to clients with similar claims.

        In the most fundamental aspects of AI, forecasting the probability and magnitude of potential losses is possible by accessing a repository of historic data. According to the Financial Times, AI will “look for relationships that traditional techniques would not necessarily pick up,” and would greatly impact underwriting, “where the insurers assess—and price—each customer’s risk.” Because of this new ability to assess risk on a more granular level, insurers may change the scope of insurable risks by eliminating some but making others more insurable.

        Artificial intelligence can uncover data about potential clients that may make them uninsurable. In other words, they would be too risky to be fairly priced and covered by Insurance companies.  On the other hand, AI may be more beneficial to insure those who were previously deemed uninsurable. An insurance company can adjust policies based on individual profiles. With AI’s predictive capabilities, insurers can procure predictions and profiles in a matter of minutes. Providing clients with speedy service and personalized policies will improve the customer experience and expectations.

Increasing Transparency in Insurance

        The Insurance Industry has the opportunity to greatly improve its reputation in achieving customer expectations and maintaining trust in comparison to other customer service oriented companies. However, AI can potentially enhance the industry’s lead time to produce services that will undoubtedly increase the industry’s transparency and improve customer satisfaction. For example, Lemonade Insurance Company, based in NYC, developed algorithms to quickly sign and approve claims that insure renters and homeowners. Tasks that previously would have taken days to complete can be settled in a matter of minutes. It has been reported that the company is “fast and transparent, rather than slow and opaque.”  Such attributes make the company more attractive to potential clients.

        Because of the transparency, Lemonade reported 81% of their customers being between 25 to 44 years old. This is a younger demographic than most insurance companies attract and maintain. Furthermore, since 2014, consumers under 44 years old are two times more likely to buy life insurance than those over 65 years old7.  This is reflective of the ease of use in internet services, which younger generations are more familiar with.

        Another way that customer satisfaction is enhanced stems emerging chatbots. Chatbots are another form of robo-advisors that handle communications with clients. They can discern the perceptions and concerns of clients via algorithms, such as a sentiment analysis. In a sentiment analysis, words are given ratings pertaining to positive, negative, or neutral connotations. After all ratings are gathered, the algorithm adds up the values and produces an overall rating. As a result, the virtual assistant, or chatbots, targets specific needs or concerns per client.

        AI company CogniCor Technologies Inc., based in Barcelona, Spain, offers chatbots to various industries around the world. CogniCor develops a personalized assistant to effectively understand and respond to the needs of customers within a designated industry. This translates into costly savings and increased efficiency. In the insurance industry, it can offer a humanlike conversational interface with customer care that immediately answers questions, resolves complaints, and deals with claims.  Incorporating a sentiment analysis into the technology achieves individualized services based on analyzing customer intentions and comments.  

Crop Insurance Matures with AI

        It is expected that the farming insurance sector will greatly benefit from artificial intelligence. Traditionally determining coverage for crops damaged during adverse weather conditions is a difficult assessment. Damages tend to be expensive and inaccurate since they rely on photographs and eyewitness accounts from farmers and insurance assessors. However, artificial intelligence offers a new way to assess crop damage by incorporating satellite and drone imaging with in-field sensors. The process of quantifying damage becomes more reliable, quick, and effective.

        One of the most popular AI plans for crop insurance is Aerobotics. This plan instigates “applying AI to aerial photos of farms.” This is part of an effort to decrease costs for farmers but also improve the accuracy of crop insurance policies. Aerobotics were first introduced to South African farms in recent years to determine where crops suffer in the fields. These completely autonomous drones have the capability to “detect leaks and determine whether crops are receiving too little or too much water…[they] also count each individual plant as well as track crop maturity and test drainage.”  It is speculated that such services can be extended to incorporate assessing risk and damage in crop insurance.

Determining Auto Insurance in a Time of Driverless Cars

        The automobile insurance sector is beginning to invest in artificial intelligence portals in order to keep up-to-date with changes made in the automobile industry. Solaria Labs, which works with Liberty Mutual Insurance, began developing their own portal that collates public data. Users access this portal to determine the safest route when driving somewhere.

        Solaria Labs, based in Boston, Massachusetts, intends to diminish the possibility of accidents and claims from arising in the first place. The portal also incorporates an AI Auto Damage Estimator, so that in the event of a collision, the user can immediately determine the damage costs.  The damage estimator is effective because it sifts through a depot of anonymous claims photos to identify ones similar to the current claim. As a result, it can estimate a cost based on historic trends in damage claims.  

        Services such as this one offer enough “insurance expertise and consumer testing to help guide the decision of what services to make available and how to organize the data.”  This will especially be the case in the future when cars become more automatic and rely on software instead of people to determine actions.

        Berkshire Hathaway Inc., based in Omaha, Nebraska, implements AI algorithms to offer policies to small and medium businesses over the Internet. With such algorithms, they tap into a plethora of data to create more personalized and specific policies. At the annual 2017 Berkshire Hathaway meeting, CEO Warren Buffet remarked on the impact driverless cars would have on the insurance industry. He explained that a future with driverless cars would relate to increased safety on the road, hopefully leading to fewer accidents. Although beneficial for society, a decrease in accidents can potentially hinder profit maximization in the insurance industry, because “the overall economic cost of auto-related losses [would] go down, and that would drive down the premium income of GEICO,” the auto insurance business for Berkshire.   

        Despite Buffet’s postulations, the insurance industry has not experienced such negative repercussions as a result of more widespread use of driverless vehicles. In this future scenario, the insurance industry will likely shift from insuring drivers to insuring the software in the self-driving cars. Thus, automotive insurance companies will merely broaden their scope of defined risks . Cyber security might be the biggest threat that driverless cars will be faced with in the future, thus creating new types of risk.

Insurers That Use AI

        Several insurance companies implement some form of artificial intelligence to enhance their services and better facilitate the needs of their clients. If they aren’t already, then they are finding ways to incorporate AI into their future business processes. For example, Swiss Reinsurance Company, the second largest reinsurer in the world, is currently working with IBM Watson. Together, they are developing an underwriting solution that will price risks more accurately on a case-by-case analysis.  This will decrease speed process times without compromising on efficiency, effectiveness, or accurate cost assessment.

        An Austin, Texas-based company named Buyonic Insurance implements a web interface contingent on artificial intelligence. The company’s robo-advisor, Siber, can “rate, bind, and issue policies on the spot, while simultaneously answering the phones and making robocalls to prospects.”  Siber solves many of the issues previously indicated in the insurance industry. This robo-advisor offers enhanced services to the clients, which enhances satisfaction and transparency within the industry.

How AI May Disrupt the Insurance Industry but Benefit the Insured

        Although speculations about job replacement by AI and robo-advisors are provocative, research indicates that AI acts as an enabler. This is primarily the case because machine learning and AI algorithms have a great degree of complexity that require oversight and input from humans.

        Furthermore, AI will provide more benefits for the insured. Clients, both individual and corporate, can discern whether or not they require insurance in the first place. AI would help assess their levels of risk in varying scenarios. In this regard, businesses can model risk to “decide what to retain on their balance sheets and what to transfer to insurers.” Artificial intelligence would be an asset to not only insurance companies but also those seeking to be insured.

Challenges that Insurance Companies will face in Adopting AI

        Despite the benefits of incorporating AI in the insurance industry, there are several limitations in implementation that ought to be addressed. These limitations are expected in any industry that pursues AI adoption.

        Regardless of industry, there are several challenges an organization is faced with when implementing AI into its business processes. The most fundamental aspect of AI adoption is building the foundation that will expose AI to an abundance of domain-specific data. For AI-based solutions to be effective and accurate, a system would need access to constantly updated information that pertains to all possible business scenarios. A popular route to resolve this issue is by investing in cloud services that utilize built-in, iterative algorithms to remain up-to-date. Then, users are no longer concerned with the data collection aspect of risk and claim analyses.

        As previously mentioned, implementing AI into an organization does not necessarily mean human employees will lose their jobs. In fact, they will be required, with a new skill set, to ensure the AI system is glitch-free. It is noted that “technologies such as speech recognition and machine learning require human oversight for their work to equate with human capabilities.” AI systems can potentially return unpredictable or inaccurate results. Early implementation of AI may demand significant oversight from a human team. The team would have to condition the AI system to think before acting. The last thing any industry wants is for an AI system to make a rash decision derived from literal commands without first understanding and then incorporating the intentions of those the system directly interacts with.

        The next challenge in accepting AI integration is gaining enough support from the employees and clients. A reallocation of tasks, management, and overall performance objectives is required because the organization should yearn for a smooth transition with artificial intelligence. It is required that employees develop new skills to work with, but also monitor, an AI system. To gain AI acceptance from clients, insurers should put themselves in their shoes and address key questions. These questions include determining if AI can be trusted to make long-term decisions, if it can effectively discern emotions and relate them in the context of a complaint or claim, and if it is as reliable as talking to an actual person to solve issues all while maintaining or improving customer satisfaction.

        Perhaps one of the biggest concerns in integrating artificial intelligence into any industry regards privacy and regulations. There is no doubt that “the insurance industry will have to battle data privacy concerns since most AI solutions are likely to reside on the cloud of a third-party technology provider.” Data must be protected from all levels of breach and unintended use. This requires significant system oversight and reconfiguration of security systems within the company.

        The bottom line is that insurance companies will have to leave their historic ways to support new technological development. Incorporating new artificial intelligence functionality into old, outdated systems might prove more resource intensive than leapfrogging to a new system that integrates AI from the get go.

        There is a market for hyperconverged packages that incorporate storage, servers, and network into one platform. One virtualized storage company called Nutanix Inc., headquartered in San Jose, California, created Xtreme Computing Platform to merge computing, virtualization, and storage. Because of its extensive automation and system-wide monitoring for data-driven efficiency, it “is designed to tolerate component failures through fault isolation and automatic recovery without bringing down the overall system.”  

        A service such as Nutanix’s Xtreme Computing Platform would be beneficial to the insurance industry in determining what type of package to purchase. For an industry that is more reluctant to make the move towards technology, a platform that offers all-in-one would be more beneficial and inexpensive to implement. This system might require only some additional adjustments to cater to insurance domain-specific needs.

        Insurance companies are not dissuaded by the challenges inherent in implementing AI. A survey conducted by Accenture, a consulting firm, indicated that 204 of 550 insurance company executives, approximately 37%, anticipate investing in extensive machine learning efforts over the course of the next 3 years. An additional 44% plan to pursue moderate investments in machine learning integration.  


        Artificial intelligence has seen recent growth and adoption by a variety of industries. Now, the insurance industry realizes the benefits of using AI, which demonstrates an interest in making the move from their traditional ways to a more modern, AI-enhanced approach. More effective risk analysis, predictive capabilities, and claims processing are byproducts of AI adoption in insurance. Such integration would positively impact customer expectations and satisfaction, a crucial aspect for insurance industries to gain and maintain a young client base. Insurance companies that incorporate Artificial Intelligence into their businesses are now eligible for federal and state tax credits.

Article Citation List



Charles R Goulding Attorney/CPA, is the President of R&D Tax Savers.

Michael Wilshere is a Tax Analyst with R&D Tax Savers.

Chloe Margulis is a Tax Analyst with R&D Tax Savers.

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