Artificial Intelligence (AI), machine learning, the Internet of Things (IoT), blockchain, robotics, quantum computing — the terminology of technology is staggering enough, let alone understanding what it is and how to use it. While some of the advances in technology are more noise than anything useful, many of these developments can be quite valuable for our industry — especially in claims management and risk control.
Fortunately, there are experts with a good understanding of insurtech and how we can make it meaningful for our companies and our injured workers. Three of them helped us break down the latest technological developments and provided insights into how they can benefit the workers’ compensation system during our most recent Out Front Ideas webinar:
- Guy Fraker, chief innovation officer for Insurance Thought Leadership
- Jason Landrum, global chief information officer for Sedgwick Claims Management Services
- Peter Miller, CPCU, president and CEO of The Institutes/Risk and Insurance Knowledge Group.
What Is Insurtech?
Simply put, insurtech is using technology to improve efficiencies and provide a better customer experience in the insurance industry. Many startup companies have entered this space in the last few years, although the majority are not as helpful as they may first appear. Companies come out with apps or, as our speakers said, shiny new toys that drive user experience and seem really cool but do not add value. One speaker said many companies offer solutions to problems that do not exist.
The more mature, robust companies — those with an actual product that covers the life cycle of the value chain or a significant gap in it — are in the minority but have the most potential to make a difference. They offer strategic and comprehensive solutions. There are not too many organizations with this capability, so there are tremendous opportunities.
The right technology, when properly deployed, has the capability of making a significant impact. Two of the most meaningful advancements for our industry are machine learning and AI. So what are these terms and how do they differ?
Machine learning is actually an application of AI. Where AI is basically computer-based logic, machine learning uses statistical techniques to allow computer systems to learn from data without being explicitly programmed. From the data, it defines a formula to predict an outcome, thereby making it meaningful.
See also: Insurtech Ecosystem: Who Will Eat Whom?
Some TPAs and carriers are using this technology to quickly flag claims that could be in danger of adverse development. The computer takes a claim, runs it against data on other claims and can determine if it is likely to become severe. As soon as the machine learning model detects something different about a claim — something a human would not be able to identify as a potentially huge loss — it alerts the claims manager to intervene and manage it more carefully. The effect is to drive the outcomes of claims in a more positive way.
For injured workers, technology is being leveraged to provide apps that provide easy access to claim information. Injured workers can find out where they are in each step of the process without having to call the adjuster.
In the consumer market, AI and machine learning are being used to apply natural language processing and determine what the person is actually saying or asking a computer. Think Alexa or Google Home. Our speakers predict it will soon become commonplace for humans to interact easily with machines.
Implementing this technology may seem overwhelming to organizations, especially if they try to adopt it on a large-scale basis. Instead, companies should have a narrowly defined plan and seek real solutions.
Industry Initiative and Blockchain
One of the exciting potential uses of newer technology in our industry is something called the RiskBlock Alliance. This not-for-profit industry consortium is meant to provide a framework that the industry owns: a standardized way of looking at data. It is based on three technologies:
- The Internet of Things
- Data analytics
The confluence of these technologies is profound. In a nutshell, the IoT is the network of electronic devices that can digitally capture and exchange data. Blockchain enables the storage of this data along with rule sets. It can execute automated instructions based on the data and the rules applied to it. It also allows for data sharing among organizations in a secure way. A couple of examples demonstrate the significant savings and benefits to the industry:
- Proof of coverage. For example, a short-haul trucking company must provide proof of insurance for every load each driver takes; approximately eight hauls per day, per driver. It adds up to about 200,000 times each day that proof of coverage must be executed. There are different insurers involved with each load. It takes the company about 30 minutes to get a proof of insurance for each load. However, using the sharable platform of blockchain means the proof of insurance per load can be available in a matter of milliseconds.
- Sharing policy information when subrogation comes into the equation. Say there is an auto accident between two vehicles, each with a different insurance carrier. Initially, both insurers start paying the insureds while they sort through the details to see who is at fault. Once fault is established, payment between the two carriers must be settled. Right now, this is done manually at an estimated annual cost of $300 million to the industry. Using blockchain, the policy information could be housed in a secure environment and the settlement done instantly. Putting policy information in an automated process on an aggregate basis could save tremendous amounts of money and time.
Challenges and Opportunities
While there are some challenges in implementing new technologies, there are also many opportunities. Many of the more rote tasks of handling claims can be done faster by technology, freeing claims managers to provide the human touch that is so necessary in so many workers’ compensation claims. Spending more time with injured workers, showing them concern and empathy, results in better outcomes for them and lower costs for payers.
One challenge is the need for data standardization, something RiskBlock is targeting. This could level the playing field and provide opportunities for smaller insurers to grow more quickly.
Incorporating aspects of insurtech into the daily workflow can be challenging, especially because there are so many innovations and ideas at play. It is important to try to harness that enthusiasm and apply it to a framework that captures the best ideas and develops them into solutions.
Another potential challenge is that our industry is so heavily regulated, and regulated differently in each jurisdiction. That means that some insurtech solutions may work in one area but not another. Caution is required before jumping on something that may not be workable.
One challenge that can be easily overcome is changing the mindset that implementing new technology requires many people. It does not. Moving into the insurtech space is best done in a constrained way, with just two or three people involved. As one speaker said, “It’s not about thinking outside the box. It’s about building the box.” Every game-changing organization like Microsoft and Facebook started with a team of just three or four people.
See also: Key Challenges on AI, Machine Learning
From a healthcare standpoint, one of the best opportunities from insurtech is the ability to get in front of pain, which can also be referred to as pre-pain or pre-hab. As healthcare technology advances, we will be able to help workers and their families understand what to expect in terms of pain before they undergo surgery, for example. We can help them be better prepared, facilitating better and shorter recoveries.
With the maturation of insurtech companies, our experts expect the number of startups will slow in the next couple of years. Instead, existing companies will return with innovations.
The tremendous amount of data available in the future will help level the playing field between larger and smaller carriers. This is because the smaller carriers will be able to participate in data sharing initiatives to have access to analytics way beyond what their own data could provide. Data aggregation insurtech companies are going directly to the source for data, such as partnering with auto manufacturers to access data from their onboard computer systems.
Insurtech will also allow pharmacists to match DNA to prescriptions to determine if they are feasible. Also, robotics can be used to handle riskier or repetitive tasks. Rather than replacing workers, the technology allows them to engage in more meaningful responsibilities. Using AI to process routine, medical-only claims may even result in eliminating some steps. We may find straight-through processing can be done quickly and efficiently.
One of the most exciting uses of new technology is to eliminate losses by removing risks. Insurtech can be used to detect when and how certain actions will likely lead to injuries, allowing humans to set up systems to prevent those conditions. The ability to avoid losses would truly transform our industry.