Predictive Analytics in Insurance: Types, Tools, and the Future

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The insurance industry is based on a simple equation:

Better data = more accurate risk calculations = higher profits

In the age of big data and artificial intelligence (AI), insurance companies compete to have the highest-quality data and topflight analytics tools to convert the data into business intelligence. The state of the art in data analytics is predictive analytics, which SAS defines as the use of data, statistical algorithms, and machine learning techniques to forecast future outcomes based on historical data.

Predictive analytics is reshaping the insurance industry, which has relied on data from its inception centuries ago. This reliance on data analysis makes insurance uniquely suited to the use of predictive analytics. For example, a survey conducted by Willis Towers Watson found that life insurers who use predictive analytics reported a 67% reduction in expenses and a 60% increase in sales.

The impact of predictive analytics on the insurance industry is so profound that Towards Data Science forecasts the rise of “InsurTechs,” which are small, entrepreneurial companies with experience in applying data, AI, and mobile applications. These firms can pose a serious threat to established insurers that fail to “reinvent and modernize the applications that have been the source of their competitive advantage.”

Investopedia answers the question, What is predictive analytics? as the process of identifying patterns in data to determine whether those patterns are likely to recur. Businesses and investors adjust their placement of resources to take advantage of future events based on the likelihood of past patterns repeating.

From the perspective of the insurance industry, predictive analytics is the most versatile of AI applications currently available to solve business problems, according to AI research firm Emerj. While actuaries have always applied mathematical models to predict the likelihood of property loss and damage, insurance firms now see data analytics as a way to maximize the value of their data investments.

Insurers see prescriptive analytics as a way to improve customer experience while simultaneously reducing the time and cost of claims handling and eliminating fraud, according to Information Age. In addition, insurance firms are integrating external data sources with their own data stores to gain more insight into claimants and injured parties. However, they face challenges in combining identity verification, social media, and other external data with internal systems.

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Insurance premiums are the rates insured parties pay for the coverage they receive. Insurance companies employ actuaries to calculate the risk of insurance policies covering life, property, liability, and other kinds of insurance. The level of risk that the insurance company must assume determines the price of the insured party’s premium.

Actuaries rely on risk analysis software that applies statistical and mathematical models to demographic data about the insured party combined with a variety of data from external sources to calculate the likelihood of a death, sickness, injury, disability, or property loss, as The Balance explains. Based on the recommendations of actuaries, insurance companies invest the premiums, so they’re assured to have sufficient funds to pay out any potential claims.

Information technology (IT) services firm LatentView Analytics describes the three steps involved in insurance underwriting:

Predictive analytics and other AI technologies improve the accuracy of the risk models used by insurers by automating the process of adjusting data models, which saves actuaries a great deal of time and effort. Use of data models based on predictive analytics allows underwriters to make more accurate predictions about a client’s risk profile.

Underwriters gain “cognitive insight” to identify elements relevant to risk evaluations that traditional modeling methods miss. Much of this data is unstructured, such as emails, geospatial location, video, images, and data from smartphones and social media. Data sources include credit services, government agencies, financial services, and other third-party vendors.

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A large portion of the world’s population remains uninsured because they can’t afford the premiums required to be covered. The International Actuarial Association explains that inclusive insurance, which includes microinsurance, is intended to bridge the gap in coverage by making policies more affordable.

Developing economies lack a sufficient supply of actuaries, so the industry relies increasingly on “proportionate actuarial services” that are based on the level of risk and availability of risk mitigation. Predictive analytics tools are seen as a way to price insurance given a limited range of factors. Nonexperts can use the tools to project required reserves to cover a five-year policy, for example, by providing an automated risk profile and liability structure.

GoodData lists four ways insurers and their customers are benefiting from the use of AI-based analytics:

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Predictive analytics is a natural extension of the methods insurers have used for several hundred years to calculate risk and set premiums for their policies. The manual underwriting methods that life insurers relied on in the past are time-consuming and expensive, as Oklahoma State University researcher Sai Gopi Krishna Govindarajula explains.

Predictive analytics in life insurance streamlines the underwriting process and improves risk assessment, which increases insurers’ profitability and customer retention rates. For example, use of predictive analytics combined with genetic profiling can help life insurance firms distinguish risk related to unhealthy lifestyles from risks associated with genetic factors that are beyond the insured party’s control, and then adjust the policy accordingly.

The Willis Towers Watson Life Predictive Analytics Survey Report from September 2018 shows the tremendous impact predictive analytics has already had on the life insurance industry. Four factors were rated highly important by life insurance companies in the drive to adopt predictive analytics:

The survey identified three areas in which predictive analytics has had the greatest impact on life insurers’ performance:

The expanded use of predictive analytics by life insurers can be applied to four specific functions:

The Milliman IntelliScript predictive analytics-based tool generates a risk score for future mortality based on information from a person’s prescription drug history. Optimum Re, a company that provides technical services to the insurance industry, applied the Milliman IntelliScript algorithm to more than 25 million insurance applications. The analysis found that Milliman IntelliScript was a “good predictor of future mortality” and could be used to “replace traditional underwriting requirements.”

Intensifying competition and raising customer expectations are driving investment in predictive analytics in life insurance. Companies offering individual life insurance and group life insurance are expected to  lead adoption of predictive analytics in the industry, according to the Willis Towers Watson survey.

The accuracy of predictive analytics methods depends on the availability and conditioning of reliable data to apply to the models. The Willis Towers Watson survey identified the most common sources of data for life insurers using predictive analytics as of September 2018 (and planned to use in two years):

Despite the potential of predictive analytics to improve life insurers’ operations and profitability, the industry faces formidable challenges in realizing the technology’s benefits. Principal among the obstacles to widespread use of predictive analytics by the industry is developing the infrastructure to accommodate the massive amounts of data required to run predictive analytics models.

The Willis Towers Watson survey found that 82% of large life insurers and 50% of midsize and small carriers were using or exploring the option of using cloud-based environments for their big data needs as of September 2018. Similarly, 45% of large life insurance carriers, 50% of midsize carriers, and 29% of small carriers were either using or exploring the option of using the Apache Hadoop framework for managing big data.

Another obstacle to widespread adoption of predictive analytics by life insurers is helping business stakeholders understand and act on the modeling results. Only 13% of the insurers surveyed believed the models were well understood or very well understood by people outside of data science and actuarial areas, and 40% stated that widespread understanding is very limited or nonexistent.

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Recent years have seen a tremendous increase in the amount of electronic health data, including medical records and claims information. However, the health insurance industry hasn’t yet found ways to take advantage of this valuable resource. Predictive analytics in health insurance is seen as an opportunity for the industry to improve patient outcomes, enhance the efficiency of health claims processing, and reduce operating costs and patient premiums.

The challenge for health insurers in applying predictive analytics is to ensure high-quality data in addition to high-volume data, according to LexisNexis Risk Solutions. The article identifies three areas where health insurance firms benefit from the use of predictive analytics:

According to the National Academy of Medicine, 5% of all patients account for nearly 50% of all healthcare spending. Predictive health analytics is seen as a way for healthcare providers to identify factors in their patients that are precursors to chronic illnesses and conditions. Similarly, health insurers are increasingly relying on predictive analytics to identify and engage high-risk patients in an effort to reduce inpatient admissions and emergency room visits.

An example of the application of predictive analytics to identify high-risk patients is the work done by the Health Care Transformation Task Force to develop care management programs directed at high-need, high-cost populations. The program uses qualitative information collected from physicians and patients and quantitative data from claims, demographic data, and other public sources.

Healthcare legislation such as the Affordable Care Act of 2010 and the TRICARE program for current and former military members emphasize value-based insurance design (V-BID). V-BID is intended to increase the quality of healthcare and reduce healthcare costs via financial incentives promoting efficiency and consumer choice.

The V-BID approach has been adopted by many state governments and large insurers, such as Blue Cross and Blue Shield plans. Predictive analytics plays an important role in overcoming the obstacles to implementing value-based reimbursement models for healthcare providers and insurers. Dimensional Insight describes five ways data analytics support value-based care models:

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Reinsurance provider Gen Re lists six ways predictive analytics is used by health insurers to optimize their claims processing operations:

A novel application of predictive analytics for health claims processing is in managing “outlier” claims that appear routine but run the risk of developing into high-value losses. An example is workers’ compensation claims that have the potential for long-term disability and permanency. Some seemingly minor claims, such as those involving soft tissue injury, may worsen over time and result in claims skyrocketing from the $8,000 to $10,000 range to the $200,000 to $300,000 range.

By applying predictive analytics to review historical claims data for similarities and other characteristics of such losses, insurers are able to identify “creeping catastrophic” (or creeping Cat) potential. Strategies and resources designed to minimize losses from such claims can then be applied early in the claims process to mitigate the potential for ballooning costs.

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Among the types of predictive analytics tools used by insurance companies are “what-if” modeling, claims prediction, and collection of external data from social media and other digital sources. These are among the popular applications for these and other analytics tools by the insurance industry.

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Application of predictive analytics by the insurance industry is in its infancy. The future of predictive analytics for insurers promises to deliver more efficiency, higher profitability, and more engaged customers.

While current use of predictive analytics by insurers focuses on life, health, and vehicle coverage, other types of underwriting have proven more difficult to adapt to this and other AI-based technologies. In particular, the work of actuaries is challenging to convert to a machine-learning approach because the data models at the core of the analysis must be tweaked continually to account for variations in data, as NS Insurance explains.

One area in which predictive analytics is forecast to have a positive impact for insurers is in gaining insight into customer behavior and preferences. For example, by allowing insurers to create more detailed risk profiles of customers, the companies are able to offer affordably priced policies to high-risk clients, rather than having to deny them coverage outright.

Forbes lists the six key reasons why companies should invest in predictive analytics:

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The insurance industry is at the forefront of predictive analytics use to make operations more efficient and more profitable. However, the industry’s use of this and other AI-based technologies is in its infancy. While there will likely always be an important role for human actuaries and underwriters in insurance firms, their duties will continue to evolve as new technologies and data sources emerge.

At its heart, the insurance industry depends on the ability to predict outcomes and behavior changes. In the future, the only way to outperform the competition in this vital area is by taking advantage of predictive analytics tools and other AI products. As these tools become easier for business decision-makers to use, they’ll have a profound impact on all insurance providers and their customers.

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Additional Sources

Daily Nation, “Big Data Has Quick Solutions for Insurance”

Duck Creek Technologies, “10 Ways Predictive Analytics in Insurance Will Shape the Industry in 2020”

Ethics and Insurance, “Why Predictive Analytics in Claims Is So Dangerous for Insurers”

InetSoft Technology, InetSoft Webinar: Predictive Analytics Examples in the Insurance Industry

PMA Companies, “Matching Predictive Analytics with Human Intelligence”

Toolbox, “How Can the Insurance Sector Benefit from Predictive Analytics?”

Virtusa, “Predictive Analytics in Insurance Claims”

Willis Towers Watson, “Predictive Analytics Speeds Innovation for Life Insurers”