For years, insurance companies have taken steps to improve the life insurance underwriting experience in the hope of removing obstacles and decreasing not-taken ratios. To that end, some have forgone the traditional exam altogether in favor of simplified issue. But the truth is, consumers still aren’t flocking to life insurers, and the results of these efforts have been incremental.
Force Diagnostics has taken a different approach. We’ve developed a consumer-centric process featuring rapid testing that delivers results in 25 minutes. Tests are performed outside of the home in retail clinics and pharmacies, and results are immediately transmitted directly to the carrier’s underwriting engine for immediate processing. Because of the speed to results, innovative insurers and reinsurers could offer an accurate quote for life insurance to their consumers within 24 hours. And with the benefit of testing with fluids (HbA1C for diabetes, cotinine for nicotine, lipids for cardiovascular risk and the presence of the HIV virus, as well as body mass index and blood pressure), insurers may offer the majority of their products quickly and with assurance.
Once the calculator is downloaded, you may select a typical life insurance policy from a dropdown menu and enter assumptions that reflect an existing underwriting process. The calculator then shows a comparison on underwriting costs, internal rate of return (or IRR) increases, issued policy increases and the potential effects on persistency. At the end, total costs per app are calculated, as are total profits.
There is tremendous value in improving the customer experience throughout the underwriting process.
Rupandeep Kaur, 20 weeks pregnant, arrived at a medical clinic looking fatigued and ready to collapse. After being asked her name and address, she was taken to see a physician who reviewed her medical history, asked several questions and ordered a series of tests, including blood and urine. These tests revealed that her fetus was healthy but that Kaur had dangerously low hemoglobin and blood pressure levels. The physician, Alka Choudhry, ordered an ambulance to take her to a nearby hospital.
All of this, including the medical tests, happened in 15 minutes at the Peeragarhi Relief Camp in New Delhi, India. The entire process was automated — from check-in, to retrieval of medical records, to testing and analysis and ambulance dispatch. The hospital also received Kaur’s medical records electronically. There was no paperwork filled out, no bills sent to the patient or insurance company, no delay of any kind. Yes, it was all free.
The hospital treated Kaur for mineral and protein deficiencies and released her the same day. Had she not received timely treatment, she may have had a miscarriage or lost her life.
This process was more efficient and advanced than any clinic I have seen in the West. And Kaur wasn’t the only patient; there were at least a dozen other people who received free medical care and prescriptions in the one hour that I spent at Peeragrahi in early March.
The facility, called the “mohalla” (or people’s) clinic, was opened in July 2015 by Delhi’s chief minister, Arvind Kejriwal. This is the first of 1,000 clinics that he announced would be opened in India’s capital for the millions of people in need. Delhi’s health minister, Satyendar Jain, who came up with the idea for the clinics, told me he believes that not only will they reduce suffering but also overall costs — because people will get timely care and not be a burden on hospital emergency rooms.
The technology that made the instant diagnosis possible at Peeragarhi was a medical device called the Swasthya Slate. This $600 device, the size of a cake tin, performs 33 common medical tests including blood pressure, blood sugar, heart rate, blood haemoglobin, urine protein and glucose. And it tests for diseases such as malaria, dengue, hepatitis, HIV and typhoid. Each test only takes a minute or two, and the device uploads its data to a cloud-based medical-record management system that can be accessed by the patient.
The Swasthya Slate was developed by Kanav Kahol, who was a biomedical engineer and researcher at Arizona State University’s department of biomedical informatics until he became frustrated at the lack of interest by the medical establishment in reducing the cost of diagnostic testing. He worried that billions of people were getting no medical care or substandard care because of the medical industry’s motivation in keeping prices high. In 2011, he returned home to New Delhi to develop a solution.
Swasthya Slate is a mobile kit that empowers front-line health workers with usable technology for prevention diagnosis care and referral of diseases. The Swasthya Slate kit was launched in the state of Jammu and Kashmir by the Ministry of Health in 2014. (Swasthya Slate)
Kahol had noted that, despite the similarities between medical devices in their computer displays and circuits, their packaging made them unduly complex and difficult for anyone but highly skilled practitioners to use. They were also incredibly expensive — usually costing tens of thousands of dollars each. He believed he could take the same sensors and microfluidics technologies that the expensive medical devices used and integrate them into an open medical platform. And with off-the-shelf computer tablets, cloud computing and artificial intelligence software, he could simplify the data analysis in a way that minimally trained front-line workers could understand.
By January 2013, Kahol had built the Swasthya Slate and persuaded the state of Jammu and Kashmir, in Northern India, to allow its use in six underserved districts with a population of 2.1 million people. The device is now in use at 498 clinics there. Focusing on reproductive maternal and child health, the system has been used to provide prenatal care to more than 22,000 mothers. Of these, 277 mothers were diagnosed as high-risk and provided timely care. Mothers are getting care in their villages now instead of having to travel to clinics in cities.
A newer version of the Slate, called HealthCube, was tested last month by nine teams of physicians and technology, operations and marketing experts at Peru’s leading hospital, Clinica Internacional. They tested its accuracy against the Western equipment that they use, its durability in emergency room and clinical settings, the ability of minimally trained clinicians to use it in rural settings and its acceptability to patients. Clinica’s general manager, Alvaro Chavez Tori, told me in an email that the tests were highly successful, and “acceptance of the technology was amazingly high.” He sees this technology as a way of helping the millions of people in Peru and the rest of Latin America who lack access to quality diagnostics.
The opportunity is bigger than Latin America, however. When it comes to healthcare, the U.S. has many of the same problems as the developing world. Despite the Affordable Care Act, 33 million Americans ,or 10% of the U.S. population, still lacks health insurance. These people are disproportionately poor, black or Hispanic, and 4.5 million are children. They receive less preventive care and suffer from more serious illness — which are extremely costly to treat. Emergency rooms of hospitals are overwhelmed by uninsured patients seeking basic medical care. And even when they have insurance, families are often bankrupted by medical costs.
It may well be time for America to build mohalla clinics in its cities.
It was an event maybe even more anticipated than Neil Armstrong’s Moon shot in 1969. I had never tuned into one before, yet there I was, sitting in my pajamas at 1 a.m., frantically trying to get back onto the streaming podcast that my iPad had just dropped, as millions of other nerds the world over were trying to do the same thing.
Apple’s product announcement event on Sept. 9, 2014, had drawn unprecedented interest. I certainly was expecting Apple to “do it again” – you know, change the world in a subtle yet pervasive way, as I am sure many others struggling to get onto the live webcast also believed would happen. After all, the company that Steve built had done it with iTunes, with the iPhone and with the iPad. And now we all wanted to see if Apple’s first wearable device – the Apple Watch, was going to change our lives in the same way.
Well, we definitely saw something that early morning in September, but the realization of the promise still lies ahead, with the first retail delivery of Apple Watches not until late April 2015. What is certain is that Apple has successfully moved the idea of a connected wrist health and fitness tracker from the niche arena of health-conscious individuals to the mainstream “Joe Public.”
Interestingly, even if Apple falls short this time, it has set in motion a great race with Microsoft, Google, Samsung, Fitbit and many others to fulfill and surpass the vision that we all saw in September. In 2014, world-wide revenue from the sale of wearables was roughly $4.5 billion, but, in 2015, expectations are sky-high. Some experts predict sales will increase as much as three times, fueled in the most part by the Apple Watch.
So why are wearables a good thing for insurance?
The rise of wearable fitness trackers as part of corporate wellness programs has been an emerging trend over the last 10 years. In the past, enlightened companies were giving out Fitbits to help employees track their own fitness. More recently, companies have been trading program participation and fitness data captured from such programs for discounts on their corporate health insurance. For example, Appirio, a San Francisco-based cloud computing consultancy, was able to get a 5% discount ($300,000) off its insurance bill in 2014, while BP America distributed around 16,000 Fitbits to employees as part of an integrated wellness program and claim to have put a brake on corporate healthcare cost increases by slowing them to below the U.S. national growth rate in 2013.
A key ingredient to the success of these programs is the engagement of the members, so that healthy behaviors are encouraged and rewarded. In the BP example, the Fitbit data was easy to “gamify” because of the connected nature of the device. Members competed on a number of challenges, including the “1 million step” challenge, simply by wirelessly “syncing” their devices. Cory Slagle, the spouse of a BP employee, was able to trim $1,200 off his insurance bill through participation in this program — dropping nearly 32 kilograms and 10 pants sizes and reducing his high blood pressure and cholesterol back to normal range in just 12 months.
Vitality of South Africa has recognized the importance of a holistic health and wellness program for well over a decade and has built up an impressive array of statistics, including:
The only trouble is that participation in such programs remains minuscule, with opt-in rates in some cases of just 5% for those eligible to join. Despite the programs’ value propositions being augmented with an affinity network of providers supplying goods and services at a discount for participating members, opt-in rates and persistency remain problematic.
A recent survey by PWC found that, if the connected wearable device was free to the member, then about two-thirds said they would wear a smart watch or fitness band provided by their employer or insurer. Cigna completed a connected wearable pilot in 2013 involving 600 subjects, which indicated 80% of the participants were “more motivated to manage their health at the end of the study than at the beginning.” In the U.S., United Health, Cigna and Humana have already created programs to integrate connected wearables into their policies, to create reward systems based on data sharing. In one innovative program, a “wager” penalty system was found to be three times more effective in motivating healthy behavior than the typical rewards these programs offer. The “wager” involved the member’s signing up to achieve and then maintain reasonable fitness targets over the course of the year to avoid having the cost of the health screening be deducted from their salary.
A key hurdle to overcome with the data generated from connected wearables is privacy and security. Individuals want to know what insights are being generated from the data being collected and want to selectively share with the program based on the perceived value they get back. They also need to know that the data continues to be secure and private once shared. Apple is working this angle through its HealthKit, which is positioned as the data control room for consolidating and securely sharing health- and fitness-related data to selected parties. There are already in-the-field health trials in progress with Stanford and Duke universities that are being powered by HealthKit. Google, Samsung and several others have also launched similar competing frameworks, so the data privacy issue is understood and being addressed by the technology companies offering products in this space.
I want to mention an innovative, data-driven, life insurance program that currently doesn’t use any wearables but easily could. AllLife of South Africa provides affordable life and disability insurance to policyholders who suffer from manageable chronic diseases, such as HIV and diabetes, and who sign up to a strict medical program. Patients get monthly health checks and receive personalized advice on managing their conditions. Data driving the program is pulled directly from medical providers, based on client permission. If a client fails to follow or stops the treatment, then the benefits will be lowered or the policy will be canceled after a warning. The company assesses its risk continuously during the policy period, contrasting with the approach of other companies, which typically only assess risk once, in the beginning. This approach allows AllLife to profitably serve an overlooked market segment and improve the health and outlook for its customers. It plans to cover more than 300,000 HIV patients by 2016.
The video of AllLife’s CEO, Ross Beerman, on YouTube is quite inspirational, and I recommend you see it. He says, “Our clients get healthier just by being our clients.” He also mentions the challenges of building an administration system to support AllLife’s customer-engagement model.
In summary, several intersecting trends have conspired to make this the perfect time to consider the launch of insurance programs and products powered by the new insights from the data being made available through wearable fitness and health trackers:
The whole fitness and healthy lifestyle perspective has entered into the mainstream culture
Devices like the Apple Watch have become fashionable, objects of desire
The data from these devices is easy to capture and share – no forms to fill in
–The data is of clinical quality, in at least some cases, and therefore useful for actuarial models
–Insurers have already started to jump on the idea of “telematics” for humans for risk pricing
–Feedback from this data is able to positively modify behavior to reduce health risks and improve the quality of life for those participating
I am still undecided if I’m going to be up at 1am again, this time outside the Apple Store, waiting for the Apple Watch to go on sale. However, the line outside the Apple Store that night could be very fertile ground for agents selling polices driven by the data these new devices will provide, if only companies act now and get their programs in place.
Individual life insurance ownership in U.S. has been decreasing over the past decade, and the figures are even more depressing when we look at the figures over the past 50 years. Life insurance ownership (both group and individual) among U.S. adults has dropped from 70% of individuals in 1960 to 59% in 2010. The number of individual policies owned by U.S. adults has dropped from 59% in 1960 to 36% in 2010, according to the Life Insurance and Market Research Association (LIMRA). The world has seen accelerated change over the past several decades, and, as entire industries transform, even leading and innovative companies can get trampled. The life insurance industry is no exception. The figures clearly demonstrate the slowing demand for life insurance. Are we seeing the “death” of life insurance, or is this just a temporary “blip” as the industry re-designs itself for changing demographics? Are there innovative business models that can change the situation?
The Case for Big Data and Analytics
The life insurance industry needs to innovate and needs to innovate fast. Innovation has to come from understanding end consumer needs better, reducing distribution costs in addressing these needs and developing products that are less complex to purchase. By leveraging new technologies, particularly new sources of data and new analytics techniques, insurers will be able to foresee some of these changes and prepare for disruptive change.
There are at least two distinct ways in which new sources of data and analytics can help in the life insurance sector.
Underwriting: Identifying prospects who can be sold life insurance without medical underwriting (preferably instantaneously) and speeding up the process for those who do require medical underwriting
New non-standard classes: Identifying and pricing prospects who have certain types of pre-existing conditions, e.g., cancer, HIV and diabetes.
Predictive Modeling in Underwriting
A predictive model essentially predicts a dependent variable from a number of independent variables using historically available data and the correlations between the independent variables and the dependent variable. This type of modeling is not new to life insurance underwriters as they have always predicted mortality risk for an individual, based on variables of historical data, such as age, gender or blood pressure.
With the availability of additional data about consumers, including pharmacy or prescription data, credit data, motor vehicle records (MVR), credit card purchase data and fitness monitoring device data, life insurers have potentially a lot of data that can be used in the new business process. Because of privacy and confidentiality considerations, most insurers are cautious in using personally identifiable data. However, there are a number of personally non-identifiable data (e.g., healthy living index computed by zip code) or household level balance sheet data that can be used to accelerate or “jet-underwrite” certain classes of life insurance.
Some insurance companies are already using new sources of sensor data and applying analytics to personalize the underwriting process and are reaping huge benefits. For example, an insurer in South Africa is using analytics to underwrite policies based on vitality age, which takes into account exercise, dietary and lifestyle behaviors, instead of calendar age. The insurer combines traditional health check-ups with diet and fitness checks, and exercise tracking devices to provide incentives for healthy behavior. Life insurance premiums change on a yearly basis. The company has successfully managed to change the value proposition of life insurance from death and living benefits to “well-being benefits,” attracting a relatively healthier and younger demographic. This new approach has helped this company progressively build significant market share over the past decade and exceed growth expectations in the last fiscal year, increasing profits by 18% and showing new-business increases of 13%.
Pricing Non-Standard Risk Classes
In the past, life insurers have excluded life insurance cover for certain types of conditions, like AIDS, cancer and stroke. With the advances in medical care and sensors that monitor vital signs of people with these conditions on a 24×7 basis, there is an opportunity to price non-standard risk classes. Websites that capture a variety of statistics on patients with specific ailments are emerging. Medical insurers and big pharmaceutical companies are leveraging this information to understand disease progress, drug interaction, drug delivery, patient drug compliance and a number of other factors to understand morbidity and mortality risks. Life insurers can tap into these new sources of data to underwrite life insurance for narrower or specialized pool of people.
For example, a life insurance company in South Africa is using this approach to underwrite life insurance for HIV or AIDS patients. They use extensive data and research on their HIV patients to determine mortality and morbidity risks, combine their offering with other managed care programs to offer non-standard HIV life insurance policies. They have been operating over the past four years and are branching out into new classes of risk including cancer, stroke and diabetes.
Surviving and Thriving in the World of Big Data
The examples we have provided are just scratching the surface of what is likely to come in the future. Insurers that want to leverage such opportunities should change their mindset and address the challenges facing the life insurance sector. Specifically, they should take the following actions:
Start from key business decisions or questions
Identify new sources of data that can better inform the decision-making process
Use new analytic techniques to generate insights
Demonstrate value through pilots before scaling
Fail forward — institute a culture of test-and-learn
Overcome gut instinct to become a truly data-driven culture
In summary, life insurance needs to innovate to be a relevant product category to the younger and healthier generation. Using new sources of big data and new analytic techniques, life insurers can innovate with both products and processes to bring down the cost of acquisition and also open up new growth opportunities.
What cycle-time improvements have you been able to achieve in the life new-business process? How well are you exploiting new data and analytic techniques to innovate in the life insurance space?