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Using Data to Improve Long-Term Care

In the last 20 years, the insurance industry has rapidly become one of the most data-driven and complex industries in our global economy. With the advent of wearable technologies, improved data-collecting capabilities and the increasing dominance of behavioral economic theories, insurance companies are inundated with data. Used well, these large sums of data can greatly benefit insurance companies and consumers. Returns on policies will increase, along with efficiency, while risk and overall costs will decrease.

However, using all this data well is extremely difficult and requires years of work and expertise. Through my more than 25 years of actuarial and statistical modeling experience, I have seen insurance companies use data well, increasing their profitability in the process. Big data can be a significant asset for insurance companies over the next 100 years, or it could bog down the industry, exacerbating issues that are currently affecting companies across the globe. All this really depends on how the insurance market adapts to and uses big data today, in the early stages of this big data era.

See also: Understanding New Generations of Data  

My current focus is the application of behavioral psychologies to build predictive models to maximize the effectiveness of insurance technologies in the design of new products. Insurance is becoming mediated more and more by mobile, wearable and artificial intelligence (AI) technologies. As generations become more connected through media technologies, leveraging media psychology, actuarial science and data science will be vital to the predictive future of insurance. This is particularly true with regards to attracting new, younger customers to life insurance and other insurance products. Young people are demanding a customer experience centered on quick and easy app-driven solutions over traditional, slower, life insurance models.

There is great potential for the long-term care industry to benefit from innovative technologies that leverage big data, machine learning and artificial intelligence. For example, home care can be improved through the use of robotics and interactive telehealth technologies to mediate the interaction between patients and medical professionals in real time, improving patient outcomes. Wearable technology to monitor biometrics, other than steps, in real time can instantaneously inform of a pending health event requiring medical attention. Big data and computing power are exploding at factorial rates, enabling algorithms to search for significant correlations in seconds rather than months, and the difference has proven to be life-saving. However, it is critical to understand how these algorithms work to prevent abuses of consumer protections.

The GIS advanced regulator training will equip regulators with a conceptual understanding of the machine learning algorithms leveraging big data being used to develop consumer insurance rates. They will learn how to test the appropriateness, power and validity of these statistical modeling tools against the data companies that are using it to build pricing algorithms and fuel AI algorithms. Regulators will also receive training in how to interrogate data for completeness and how to identify hidden biases that may unfairly discriminate against consumers. This training will also engage regulators in discussions of the ethical use of big data, machine learning and AI in preparation for a future where insurance is nearly 100% mediated by technology.

See also: Healthcare Data: The Art and the Science

Companies will have to become good digital citizens and work with regulators to ensure an industry that fosters innovations beneficial to consumers without compromising legal standards and the ethical treatment of all consumers. A future of insurance mediated by big data, predictive algorithms and AI will have great benefits for the human experience. The industry and regulators through cooperative efforts can ensure this promising future for consumers.

I will be moderating the “Can Big Data Save Long-Term Care” breakout panel on Wednesday, April 24, and am organizing and leading the big data and advanced modeling training on April 22-23 and April 25-26 at the 2019 Global Insurance Symposium in Des Moines. To register to attend GIS please go to: https://globalinsurancesymposium.com/register/