Tag Archives: data analytics

New Analytics for Small Commercial

Analytics can be a great equalizer in every industry. It’s why 90% of respondents to a McKinsey survey call their analytics investment “medium to high” and another 30% referred to the investment as “very significant” proof that the surveyed understand the value that analytics possesses.

Those investors—especially the commercial insurers—understand the value of analytics and get their money’s worth. In addition to improving sales targets and reducing churn, analytics can increase profitability when it comes to underwriting and selecting risk.

Still, the full potential of analytics goes beyond the insights it provides insurers. When merged with modern technology, data and analytics can fuel efficiency, accuracy and productivity. When used within the decision engine to drive automation, for example, data and analytics can help insurers expedite processes and improve customer experiences, even without human intervention.

Automated reports and actions provide insurers new ways to optimize their day-to-day operations. However, the marriage of automation and analytics is especially vital for the small commercial market as they contend with higher volumes of policy quoting and writing. Using predictive models, automation can reduce the amount of human effort it takes to sell and service policies for small businesses.

Analytics and automation present opportunities to optimize every facet of growing market share for small commercial insurers if properly applied. The sooner that insurers embrace the two, the better off they—and their customers—will be.

Analytics and Automation Can Deliver

When it comes to risk assessment for small businesses, insurers are usually hampered with limited or even misleading information. Unfortunately, this can result in a gap between a risk-appropriate rate and the quoted premium. Thanks to automation and analytics, however, that sort of disparity can be a thing of the past.

See also: What Predictive Analytics Is Reshaping  

While there are many ways analytics and automation can be used to improve the small commercial insurance industry, there are three particular areas where major improvements have been demonstrated. For insurers that are on the fence about committing to analytics and automation, here’s where their influences will likely be most visible:

1. Simplified Applications

By automating customer quoting and underwriting, insurers can phase out the process of collecting troves of information on an application. With reliable third-party data sources, automation can fill in many of the blanks present on typical applications. Insurers will then only need to ask for what’s relevant for the predictive model to assess the risk and provide direction on pricing.

In the same vein, the automation of processes and decisions empowers insurers to use straight-through processing for new applications—quoting and binding policies entirely through an e-commerce experience, without involving staff or consuming staff time. Typically, this is a far more streamlined process for both the insured and insurer, and delivers improved customer experiences.

2. Expedited Claims Processing

Small businesses are acutely sensitive to how long it takes insurers to pay claims and how good (or bad) their experiences are. Analytics helps insurers triage claims while suggesting different processing options.

According to a LexisNexis study, the availability of this data helps shorten processing cycle times by up to 15%. For example, through IoT (internet of things) devices, an insurer can detect water heater leaks and other high-risk problems in real time, enabling the insurer to anticipate potential claims and possibly even prevent them.

Of course, being fast is only part of the equation—the process must also be accurate. Thankfully, automation and analytics improve processes by catching overlooked data points. When sophisticated analytics are applied against a large sample of detailed claims data, the resulting insights can, for example, highlight the best way to get an injured employee back on his or her feet and offer a customized plan to do so.

See also: What’s Beyond Robotic Process Automation  

3. Improved Risk Identification

By using reliable third-party data, such as information available through a data consortium, insurers can more quickly and accurately identify risk—even if it’s in a sector where they have little or no experience—and ensure that risk-appropriate pricing is quoted. Analytics thus becomes a valuable growth engine for insurers to confidently expand into different business lines and regions.  In an environment where 40% of the smallest organizations have no business insurance whatsoever, insurers that embrace modern technology could reap significant rewards. By combining analytics with automation, the small business insurance market could be transformed—which would be welcome news for both insurers and their customers.

3 Technologies That Transform Insurance

The combination of artificial intelligence (AI), robotic processing automation and predictive data analytics is fundamentally redefining how businesses operate, how consumers engage with brands and, indeed, how we go about our daily lives. The field of insurance is no exception. Outlined here are three ways smart technology is affecting insurance, with a focus on identifying lessons learned and defining specific keys to success.

Back Office Robotic Process Automation

The impact of rules-based robotic process automation (RPA) on insurance operations has been well-documented. RPA tools are driving efficiency and productivity gains in generic back-office functions such as F&A and HR, and insurers are tackling processes related to claims administration and account management.

One key challenge is scalability. In many cases, concept initiatives have failed to gain traction, resulting in isolated pockets of automation that yield limited benefit. In others, overly ambitious enterprise-wide projects struggle with boil-the-ocean syndrome. A well-defined center of excellence (CoE) model that develops and documents best practices and then propagates them across different business units has proven effective.

Another critical lesson has been the importance of CIO involvement. This was lacking in many early RPA projects. For one thing, because RPA tools focus on process and business functions rather than programming skills, CIOs often weren’t interested. Business unit heads, moreover, feared that CIO involvement would lead to bureaucratic logjams and derail aggressive adoption schedules. Practice has shown, however, that CIO oversight is essential, to avoid both general shadow IT problems as well as specific interoperability, stability and security issues related to RPA functionality.

See also: Using Technology to Enhance Your Agency  

Leading early adopters have also continually pushed the envelope of automation levels. In a claims processing environment, 70% of claims may be simple and straightforward, and therefore ideally suited to an RPA application. At the other end of the spectrum, 5% to 10% of claims are complicated and unusual, and therefore require a human’s expertise and judgment to evaluate. While doable, automating these complex outlier claims isn’t cost-effective. The challenge then becomes to focus on the remaining 20% to 25% of claims. By analyzing the frequency of different types of claims, insurers can identify cases where the time and effort needed to configure a bot will yield a return.

Applying Cognitive Capabilities to RPA

RPA has delivered impressive benefits to insurance operations in terms of cost reduction, accuracy and auditability. That said, the tools are limited to the specific if/then rules they’re configured to follow. If a bot encounters a scenario that doesn’t align with what it’s been taught, it gets stuck.

More advanced cognitive systems apply pattern recognition to analyze unstructured data to identify key words and phrases in context. This promises to take insurance automation to the next level. While an RPA bot can extract a specific piece of data such as a policy number from a specific form, it can’t interpret underwriting rules or aberrations from a form on which data is unstructured and organized differently.

A cognitive application, meanwhile, can scan documents of various types and formats and apply machine logic and learning to identify relevant data in spite of discrepancies in how the data is structured or presented. This allows people to focus on policy/claim exceptions rather than formatting issues. More specifically, by injecting cognitive applications into operational workflows at key “intelligent gates,” insurers can more easily identify aberrations in unstructured data and highlight the policies and claims that require further human involvement.

IoT, AI and Insurance Underwriting

The combination of Internet of Things (IoT) and artificial intelligence will have perhaps the most transformational impact on insurance. By deploying networks of smart, connected IoT sensors, insurers can collect and analyze volumes of data at the point of critical business activity. Leveraging the pattern recognition and predictive analytics powers of AI, meanwhile, creates insights that insurers can use to refine actuarial tables and improve the rules of underwriting.

Consider these examples:

  • Sensors in vehicles ranging from commercial trucks to passenger cars monitor and document speed and driver behavior. Insurers can analyze data to calculate accident probabilities of safe vs. risky drivers over time. Based on those calculations, premiums could be adjusted. Smart sensors and cameras can also detect drowsy drivers or erratic behavior, triggering alarms.
  • Smart home technology that monitors suspicious activity and automatically shuts off water pumps in the event of a burst pipe can lead to lower homeowner policy costs, particularly for premium coverage such as insuring valuable artwork from theft and damage.
  • Pharmacies that store and transport medicines can deploy temperature and humidity monitors to ensure that supplies stay within required guidelines. Reducing the risk of tainted medicine reaching consumers could reduce liability risk.
  • Smart video analytics can determine wear and tear of roofs, oil pipe damage from foliage and animal migration and levels of water and soil contamination. Such insights enable corrective action before catastrophes strike and reduce the level of unforeseen risk for underwriters.
  • By monitoring pressure or fluid flow in an oil pipeline, sensors can trigger shut-off valves if limits are exceeded, thereby preventing costly environmental damage.

Innovative insurers are exploring how to deploy these capabilities into policy formulation. For instance, customers who adopt the technology could qualify for discounts. (Given the privacy issues surrounding driver monitoring, the voluntary aspect would seemingly be critical for auto insurance policies.) Another option: Insurers team up with technology partners to offer smart sensor services, thereby helping policyholders while creating revenue streams.

See also: Smart Home = Smart Insurer!  

The combination of sensor array and intelligent technology is refining underwriting and claims payment. Insurers can tune actuarial tables and pricing models to cover potential losses before they occur, as well as avoid incidents by advising policy owners to take corrective actions. In other circumstances, sensors can take action on their own.

Ultimately, these capabilities will enable insurers to fundamentally redefine their operational and customer engagement models.

Could Risk Analysis Win the Lottery?

I started writing yet another article trying to convince risk managers to grow their quant competencies, to integrate risk analysis into decision-making processes and to use ranges instead of single-point planning, but then I thought, why bother? Why not show how risk analysis helps make better risk-based decisions instead?

After all, this is what Nassim Taleb teaches us. Skin in the game.

So I sent a message to the Russian risk management community asking who wants to join me to build a risk model for a typical life decision? Thirteen people responded, including some of the best risk managers in the country, and we set out to work.

We decided to solve an age-old problem – win the lottery. With help from Vose Software ModelRisk we set out to make history. (Not really: It’s been done before. Still fun, though).

Here is some context:

  • Lotteries are an excellent field for risk analysis because the probabilities and range of consequences are known
  • In Russia, as in most countries, lotteries are strictly regulated.
    There is a rule: When a large amount accumulates, several times a year it is divided among all the winners. This is called roll-down.
  • If no one takes the jackpot before or during the roll-down, then the whole super prize is divided among all other winners
  • So the probability of winning is the same as usual, but the winnings for each combination can be significantly higher if no one wins the jackpot.

We set out to test our risk management skills in a game of chance.

June 8, 2019

Whatsup group created. Started collecting data from past games. Some of the best risk managers in the country joined the team, 15 in total: head of risk of a sovereign fund, head of risk of one of the largest mining companies, head of corporate finance from an oil and gas company, risk manager from a huge oil and gas company, head of risk of one of the largest telecoms, infosecurity professionals from Monolith and many others.

June 9, 2019

Placing small bets to do some empirical testing.

June 10, 2019

First draft model is ready…

June 11, 2019 

Created red team and blue team to simultaneously model potential strategies using two different approaches: bottom up and top down. Second model is created…

June 12, 2019

Testing if the lottery is fair, just in case we can game the system without much math. Yes, some numbers are more frequent than others, and there appears to be some correlation between different ball sets but not sufficient to produce a betting strategy. The conclusion – the lottery appears to be fair, so we will need to model various strategies.

June 13, 2019

Constantly updating red and blue models as we investigate and find more information about prize calculation, payment, tax implications and so on. The team is now genuinely excited. Running numerous simulations using free ModelRisk.

June 14, 2019

Did nothing, because all have to do actual work.

June 15, 2019

After running multiple simulations, we selected a low-risk, good-return strategy. Dozens more simulations later, here are the preliminary results, using very conservative assumptions:

  • probability of loss 9.8%; worst-case scenario, we lose 60% of the money invested
  • probability of winning 90%; 80% of the time, winnings would be between 50% and 100% of the amount invested, after taxes (this means there is a high possibility to double invested cash at relatively low risk)
  • potential upside significantly higher than downside

Red and blue team models produced comparable results.

June 16, 2019

Started fundraising.

If we manage to collect more than the required budget, we decide to make two bets: one risk management bet (risk management strategy) and one speculative bet with much higher upside and as a result greater downside (risky strategy).

Full budget collected within just a few hours. Actually collected almost double the necessary amount and, as agreed, separated 50% of the funds into the second investment pool. Separate team set out to develop the risky strategy. While I was an active investor in the risk management strategy, I decided to play a role of a passive investor in the risky strategy and only invested 16% into the risky.

June 17, 2019

Continued to develop the model, improving estimates every time. Soon, we felt the financial risks were understood by the team members, and we needed to take care of other matters before the big day.

First, took care of legal and taxation risks. Drafted a legal agreement clearly stating the risks associated with the strategy, the distribution of funds and the responsibilities of team members. Each member signed. Agreed to have an independent treasurer.

Then started to deal with operational risks. Apparently transferring large sums of money, making large transactions and placing big bets is not plain vanilla and required multiple approvals, phone calls and even a Skype interview. Five team members in parallel were going through the approvals in case we needed multiple accounts to execute the strategy.

Probably the biggest risk was the ability of the lottery website to allow us to buy the tickets at the speed and volume necessary for our low-risk strategy. This turned out to be a huge issue, and we found an ingenious solution. The information security team at Monolith did something amazing to solve the problem, and I mean it, amazing. I have never seen anything like this. It’s a secret, unfortunately, because, you guessed it, we are going to use it again.

The strategy that the lottery company recommended for large bets is actually much riskier than the one we selected. How do we know that? Because we ran thousands of simulations and compared the results.

June 18, 2019

The lottery company changed the game rules slightly. Ironically, this slightly improved our 90% confidence interval and reduced the probability of loss. So, thank you, I guess.

More testing and final preparation. The list of lottery tickets waiting to be executed.

In the true sense of skin in the game, team members who worked on the actual model put up at least double the money of other team members.

June 19, 2019

8am. We were just about to make risk management history. A lot of money to be invested based on the model that we developed and had full trust in. I felt genuinely excited: Can proper risk management lead to better decisions? I am sure other team members were excited, too.

By about lunch time, the strategy was executed. We bought all the tickets. Now we just had to wait for the 10pm game. Don’t know about the others, but I couldn’t do any work all day. I couldn’t even sit still, let alone think clearly. Endorphins, dopamine, serotonin and more.

At 9:30pm, we did a team broadcast, showing the lottery game as well as our accounts to monitor the winnings, both for excitement purposes and as full disclosure.

Then came the winning numbers. Two team members actually managed to plug them into the model and calculate the expected winnings. We had the approximation before the lottery company did.

You guessed it: We won. Our actual return was close to 189% on the money invested after taxes (or 89% profit; remember, our estimate was 50% to 100% profit, so well within our model). We almost doubled our initial investment. Not bad for risk management. (Good luck solving this puzzle with a heat map.)

June 20, 2019

More excitement, model back-testing and lessons learned — and, perhaps the most difficult part, explaining to non-quant risk management friends why, no, this was not luck; it was great decision making.

In fact, our final result was close to P50. We were actually unlucky, both because we didn’t get some of the high-ticket combinations and, more importantly because five other people did, significantly reducing our prize pool.

Let me repeat that: We were unlucky and still almost doubled our money.

June 21, 2019

Job well done!

Changing Nature of Definition of Risk

As the foothold of innovation across industries grows stronger by the day, insurers are witnessing the advent of tech-based economies, and with them a fundamental shift in the very definition of risk. Every advancement stands to revolutionize how property, businesses and employees will be insured. Consider automated cars and workplace automation tools, such as Amazon warehouse robots, or the emergence of shared ownership business models, like Lyft and AirBnB. Traditional risk calculation models need to evolve to keep up with rapid change.

How shall insurers prepare for this shift? According to Valen Analytics’ 2019 Outlook Report, a key part of the answer lies in the need to weave data and predictive analytics into the fabric of their business strategies. The report, which employs third-party and proprietary data to identify key trends, revealed:

Insurers Are Heavily Relying on Advanced Use of Data and Analytics to Fuel Growth

Valen’s Underwriting Analytics study found that 77% of insurers are incorporating predictive analytics into their underwriting strategy. This marked an increase compared with the steady 60% of insurers during the past three years, demonstrating a clear emphasis by the industry on data-driven decisions.

While many factors have fueled the demand for sophisticated data and analytics solutions, one stands out. Insurers have a growing desire to reap a share of the underserved small commercial market, which represents over $100 billion of direct written premiums. Data analytics tools enable insurers to reduce the number of application questions, verify necessary information and ascertain risk much more quickly and accurately. This is particularly important in creating effective business models that align with the needs of small business owners.

The rise in insurers looking to employ advanced data analytics techniques has also resulted in the growth of data aggregation services and consortiums. With new primary customer data sources emerging, insurers have access to better insights on consumer risk and behavior. This has contributed to insurers’ appreciation of the predictive horsepower that large pools of data offer. In fact, Valen’s proprietary research found that the synthetic variables appended with consortium data are as much as 13 times more predictive than policy-only data. Synthetic variables are built from computations of more than one variable, made possible by leveraging large and diverse datasets.

See also: Understanding New Generations of Data  

Regulation and Innovation Must Go Hand-in-Hand

With a rise in advanced predictive analytics and robotic process automation in insurance, regulators are paying close attention to the industry. To ensure this oversight doesn’t stifle innovation, it is important that insurers build and document their analytics initiatives so they can be explained and understood by regulators. Being collaborative and responsive will help ensure that regulators can discern the small percentage of use cases that need to be reviewed for consumer fairness protection. In doing so, insurers have the opportunity to take the industry to Insurance 2.0 — the next phase in technology adoption and innovation.

Talent and Infrastructure Challenges

While insurers are looking to integrate data and predictive analytics into their business strategies, what will truly determine their success is their ability to hire and nurture the right talent. Unfortunately, the industry continues to suffer from a lack of the talent needed to support fast-paced innovation. Seventy-three percent of insurers surveyed indicate moderate to extreme difficulty in finding data and analytics talent, and the reasons haven’t changed over the years. While geographic location of the job is the primary reason cited by the survey respondents, more and more prospects are either looking for better compensation packages, are simply not interested in an insurance career or opt for opportunities in tech startups or data-driven companies in other fields.

Another roadblock for insurers is their dated IT infrastructures, which cause massive backlogs. While most insurers suffer backlogs of two years or more, others cannot identify how long their IT backlogs are.

See also: Insurance and Fourth Industrial Revolution  

Both of these problems go hand in hand. Clearly, there is a need to foster an innovation mindset, and, to do so, the industry needs a mix of new thinking and engaging work culture. Insurers should follow the footsteps of leading tech companies and cultivate a culture that appeals to high-level talent. By making small changes, such as embracing diversity and a remote workforce, insurers can make themselves attractive to the talent they need. This will build a workforce capable of overcoming IT infrastructural issues.

In short, to maintain a competitive advantage, insurers must not only put data and analytics at the forefront of their businesses, but also make strategic decisions on how best to employ them to enhance all aspects of their businesses, from customer service and information handling to risk calculations and claims processing.

Insurtech: Revolution, Evolution or Hype?

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:

  1. The Internet of Things
  2. Blockchain
  3. 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:

  1. 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.
  2. 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.

The Future

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.