Now that we've been talking about the Internet of Things for a decade-plus and have been deploying it for several years, reality and fantasy are separating out. A new report from McKinsey offers some sharp insights both into how the IoT will develop from here and into how companies -- including many insurers -- should adjust as they try to use the IoT in products and services.

First, the lessons. The McKinsey report says that companies that have used the IoT successfully took seven main steps:

• They assign a clear owner in the organization for the IoT. "At present," the report says, "many organizations have... decision making dispersed across functions, business units, and levels."
• They design for scale from the start. McKinsey says many companies get caught up in the technology and focus only on pilots, resulting in “pilot purgatory.”
• They commit. "Deploying multiple use cases at the same time forces organizations to transform operating models, workflows and processes to ensure value capture," the report says.
• They invest in technical talent, both by recruiting aggressively and by retraining their current data science workforces.
• They change the entire organization, not just the IT function. "Too often," McKinsey says, "IoT deployments are regarded as technology projects run by the IT department rather than business transformations. Technology alone will never be enough to unlock the potential of the IoT.... Instead, the core operating model and workflow of the business must be redesigned."
• They push for interoperability. "The IoT landscape is dominated by fragmented, proprietary, supplier-specific ecosystems," the report says. "While effective within the ecosystem, such an approach limits the ability to scale and integrate, constraining the impact of IoT deployments and driving up costs. Corporate customers can specify interoperability as a buying criterion."
• They shape their environment. "For example," McKinsey says, "prioritizing cybersecurity from the beginning and starting with the hardware layer is critical to developing end-to-end security. Working with trustworthy suppliers can reduce the likelihood of a breach, but adopting a cybersecurity risk-management framework that incorporates not only technical solutions but also business processes and procedures that fit a company’s environment and requirements can be much more effective."

In terms of how the IoT market will develop, McKinsey acknowledges that the predictions in a 2015 report on the IoT were too optimistic -- but the impact was still massive. The consulting firm estimates that the IoT unlocked $1.6 trillion in value in 2020, including the value captured by consumers and customers of IoT products and services, and says that figure could grow to between $5.5 trillion and $12.6 trillion by 2030.

McKinsey said that five factors have asked as headwinds, restricting the development of the IoT:

• Not enough focus on change management. The report says, "Companies and governments often treat the IoT as a technology project rather than an operating-model transformation," so they don't pay enough attention to the need for "cross-functional actors to change people’s behavior, systems, and processes, as well as introduce vigorous performance management."
• Lack of interoperability. And the report cautions that "ubiquitous operating systems for the IoT are still far off," meaning that "the IoT landscape contains numerous proprietary, 'walled garden' ecosystems."
• Cost and complexity of installation. "Almost every at-scale deployment requires customization, if not an entirely bespoke solution."
• Concerns about cybersecurity "as the rising number of connected end points offer vulnerable points for hackers to exploit."
• Worries about privacy. Not only do companies have to contend with the adoption of the California Consumer Privacy Act and the European Union’s General Data Protection Regulation, but, the report says, "companies are grappling with what customers are willing to give up in return for lower prices or special offers in a retail setting."

On the plus side, McKinsey reports that three factors are accelerating the progress of the IoT:

• Customer perceptions. They see real value in the IoT, a marked change from the study McKinsey did in 2015.
• Vast improvements in technology. "Sensors now cover the entire spectrum, from visual to acoustic and everything in between; computing is more than fast enough; storage is ubiquitous; battery power has improved," the report says. "Progress in hardware has been matched by significant developments in advanced analytics, AI and machine learning that enable faster, more granular insights and automated decision making from data provided by sensors."
• Better networks. 4G wireless networks are reaching more people, and 5G networks are being rolled out quickly.

The report singles out two areas that will affect many insurance companies. McKinsey says factories could generate 26% of the gains from the IoT by 2030. Those gains will likely involve greater automation and create opportunities for improving safety -- considerations that will ripple through workers' comp, P&C and other forms of insurance. The report also says that safety, in general, will improve greatly. It says, for instance, that while we all wait for our autonomous cars to arrive we will increasingly be driving vehicles with much-enhanced safety features, courtesy of the IoT. Insurers will, of course, want to enhance safety as much as possible and will need to adjust pricing as risks change.

It may well be that McKinsey is too optimistic in this report, just as it was in 2015. That seems to be how predictions go in the early days of a massive technology trend -- even though we've seen for decades that the tendency is to overestimate change in the short run while underestimating it in the long run.

But the change will clearly be massive, and we've seen enough by now that we in the insurance industry can start to finetune our approach as we try to drive that transformation.

Cheers,

Paul

Disruption in the insurance industry is being driven from two sides. Externally, consumers are demanding better digital solutions and are replacing agents and paperwork with mobile apps and integrated experiences. Internally, companies are rushing to leverage machine learning and artificial intelligence to improve underwriting models, which requires tighter data integration. Failure to innovate technologically could mean losing policyholders to more nimble digital-native competitors. In fact, a report from PwC says more change occurred in the insurance industry in 2021 than the combined past years.  

Insurers must quickly advance their digital capabilities and focus on customer experience rather than products. This means becoming more agile, reducing the complexity of legacy products and processes, changing delivery models, right-sizing cost infrastructure and collaborating with partners for innovation.  

APIs - Critical to Delivering Digital Consumer Experiences

As consumers flock to using digital platforms, insurance companies are now required to double as app developers. This requires building entirely new technological frameworks and skill sets, while working at a very different pace and with different methodology.

Where internal system engineering traditionally works on the company’s on-premise systems, with legacy data stores and slow and stable release cycles, delivering best-in-breed digital experiences requires adopting new technologies, deploying to the cloud and releasing in agile iterative cycles.

The marriage of internal legacy systems with new agile development can prove challenging -- and often hinders digital transformation. This is where an internal application programming interface (API) hub can accelerate the process.

APIs enable different pieces of software to “talk” to each other by sharing data and functionality. APIs serve as the “bridge” between the internal systems and new consumer apps, enabling the development of consumer software to work separately from the rest of IT while maintaining a clear integration.

APIs - Powering the Next Generation of Business Relationships

It is not just better apps that consumers expect. Modern consumers expect services to come to them and be integrated into their existing buying flows. A consumer renting an apartment, for instance, will expect a renters’ insurance offer to be available as part of the leasing process. A user buying a car will expect an insurance quote to appear in the checkout flow. A business buying machinery or hiring employees will expect insurance to tie seamlessly into their ERP/payroll systems with a 360-degree view of all data integrations.

See also: Open Banking APIs: A New Growth Engine

These integrations provide ample opportunities to introduce insurance into existing flows but also require tight integrations between businesses. The only way to power these integrations is by offering APIs and allowing partners to use those API to tie insurance into their systems. APIs thus open up crucial revenue streams.

APIs Are Necessary to Introduce New Technologies

On top of these consumer expectations, there’s also tremendous innovation happening at the core of the business, introducing more advanced AI and ML into the underwriting processes. As companies rush to hire top talent to build these models, it is critical to ensure that their models will have easy access to the data and systems they need to be useful. Even the best neural network is useless if it can’t integrate into the underwriting system.

APIs serve as that technological pedestal, acting as the layer connecting data stores and systems to new models and decision-making systems. An investment in good APIs and API tooling will accelerate the introduction of new systems by multiple factors.

APIs, APIs, APIs...

Whether you are thinking of delivering better digital experiences, creating better channel partnerships or improving internal processes, an investment in APIs is critical. In our company’s annual survey of API usage, we found that 94% of developers in the financial services industry (which includes insurance) plan to increase or maintain their usage of APIs. While many companies are funding digital transformational programs with APIs at the core of their strategy, they struggle to use APIs effectively. 

A key hurdle for many organizations is they don’t know the APIs that exist across their business. Whether it’s due to organizational silos, disparate infrastructure and tools or lack of resources and ownership, companies fail to effectively implement an API strategy. Legacy API management vendors do not address modern API requirements and just lock organizations into their technology, restricting expansion and growth. That’s where API hubs offer an exceptional advantage.

API Hubs to the Rescue

Successful insurance companies are adopting next-generation API hubs to discover and connect to all their APIs more easily. This open, self-service, single hub is where development teams can publish and share APIs so others can quickly find and use them. A hub speeds innovation. For the broader organization, it helps to reduce the time to market of products—which brings new revenue streams.

Additionally, a hub enables initiatives like “open insurance” for providers that want to create an ecosystem with a common approach to securely share data through APIs to drive value. 

Insurtech firms like Lemonade, Cuvva and CoverWallet are using APIs to gain footing in the insurance marketplace and provide simple and easy consumer experiences. We are seeing more and more big insurers incorporating APIs in their business models to remain competitive and provide service excellence. 

Are you an insurer considering an API hub to serve as a catalyst for digital innovation in your company? Here’s how to get started:

1. Inventory your APIs. Begin by understanding the APIs that exist throughout your organization. It is important to catalog your APIs and collect all the resources so you have an accurate inventory. Make sure you include all teams, including engineering, product design and back-office functions.
2. Document your APIs. Next, for each API, generate documentation (preferably in an open format, like an OpenAPI Spec file). Without proper documentation, your team can’t see or use the APIs that are available.
3. Establish a hub or platform for your APIs. Once you have the accurate inventory and associated documentation, the next step is to establish an API hub where all APIs can be seen and accessed. The structure can be simple, such as a web page in your intranet. Or it can incorporate advanced features like integrated testing and provisioning capabilities. You can build the API hub yourself or use an external vendor. Often companies soon realize the benefit of having an outside vendor create their API hub after trying unsuccessfully to build their own, mainly due to lack of resources, time, skills or competing business priorities. Once your API hub is in place, it’s important to make sure it is well-facilitated, orchestrated and managed while ensuring security.   

See also: Making Inroads With Open APIs

APIs are the foundation for any application modernization effort, and they have the potential to help change the velocity of innovation for the insurance industry. Digitally enabled insurers that take an API-first strategy are more able to enhance the experience for their customers, policyholders, employees, partners, shareholders and others while keeping pace with changing market demands.

Sometimes, it seems as if more insurtechs are getting funded than one can keep track of, and every one of them wants to work with a big insurance carrier to prove out their solution. Carriers are by their nature risk-averse, and these startups are pushing the envelope of what’s possible with new technologies. There are risks associated with working with insurtechs. But insurers that take too conservative an approach to working with insurtechs may miss out on opportunities to gain competitive advantage. 

First, let’s talk about the advantages of working with startups. A well-run startup can use its small size and scrappy culture to a carrier’s advantage, because it enables the carrier to be nimble. Need a change to the solution or identify something that can be improved? The insurtech provides rapid turnaround, giving you the equivalent of a custom-built product.

Over my career, I’ve worked with both startups and carriers, and the most successful relationships are forged when the insurer seeks transparency and is willing to be very hands-on. Communication between product and engineering must be near-constant from beginning to end, and early vetting should make sure that the startup and carrier teams can work together successfully.  

Remember, startups are forging new paths, building new technologies and creating novel use cases. In most cases, they won’t have a polished solution to deploy, unless they’re very mature. They’ll be developing their product or service as you move forward to a pilot. If you’re spending a lot more time with product discovery than you would with an established vendor, that’s a sign that you’re doing it right. Startups need to gain a full understanding of the challenge they’re addressing and what roles will need to be established to tackle it. Once they’ve got that down, the entire team will be able to move forward with technology that quickly delivers the value you’re looking for.

Mistakes to avoid

Startups move fast, and sometimes they move too fast for insurers, which typically work at a more measured pace. This mismatch can cause the relationship to break down. Both sides need to be on the same page with the same expectations about timelines, deliverables and priorities from the very start. 

Don’t send line-of-business managers to work with the insurtech alone. A product person or someone who is technically literate and knowledgeable about the carrier’s technical capabilities needs to work as a liaison. Without a strong technical background, the carrier’s representative may green-light projects that will cause engineering serious headaches later down the road. Technology needs to be at the table from the start.

Startups are ambitious, and it’s not at all uncommon for them to take on more than they can realistically handle. They simply have a different tolerance for risk than a carrier does. To mitigate this misalignment, start small. Identify a project that can be completed and deliver value in a short time. You will limit your risk and be able to test-drive the working relationship to see how well the startup can live up to its promises before you get in too deep.

See also: How Infrastructure Is Reshaping Insurtech

Security and technology evaluation

It’s important to get the relationship off on the right foot, and that starts with evaluation. When you first meet with a young insurtech, ask the following questions:

• How will we deploy your solution, and how long will it take?: Unless there’s a very good reason for an on-premises deployment, modern software is now deployed as a service. So, if they’re talking about a six-month or longer deployment, that’s not a good sign. 
• What’s innovative about this?: If you can get a similar solution from an established vendor, there’s no reason to take on the risk of working with a startup. The startup should be able to clearly explain the advantages of their technology compared with what's already commercially available — and those advantages need to be significant. 
• How easy is it to integrate your solution with other technologies?: Will they build their own application programming interface (API), or are they using APIs that already exist. You need to understand exactly what you may be in for when it comes to connecting their solution with the rest of your stack.  

You’ll also want to conduct a security audit, but don’t be surprised if the startup can’t meet all of your requirements at first. Voice your concerns. Smart startups will address your security concerns quickly. 

Concerning data, limit your risk by only providing the insurtech with the minimum it needs to meet your objectives. Be especially conservative with personally identifiable information. Do they really need full names of customers? Would a first name work? Do they need a full address or just a ZIP code? Don’t provide any more data than is required. 

The importance of transparency

The key to success is to remember that an insurer will be co-developing the solution with the startup, and this relationship only works if there is transparency. Work incrementally with an agile approach, and, once you’re into the main project, have the startup accomplish the hardest technical tasks first.

Insurtechs are a risk, but the potential rewards are great. Build a transparent relationship where each side works as a partner, and together you will build something amazing.

Prior authorization is the “single highest cost for the healthcare industry” in the U.S., totaling some $767 million a year, according to the CAQH index. 

Physicians spend nearly two full business days per week on prior authorization requests, and payers devote thousands of manhours reviewing and approving them in an antiquated, manual process involving phone calls and faxes. 

The arduous task often delays necessary treatment and sometimes results in treatment abandonment — patients just get tired of waiting, so they give up — both of which hurt patient outcomes and ultimately raise costs in the long run.

Prior authorization has been identified as one of the biggest opportunities for applying artificial intelligence (AI) to help lower the administrative burden and cost. Considering that 82% of healthcare leaders want to see their organizations be more aggressive in adopting AI technology, now may be the perfect time to take the leap toward applying AI to solve the prior authorization problem. Here’s how it can work:

1. Establish parameters for automatic approval.

A machine learning platform can look at previous prior authorization requests and identify the conditions most likely to result in an approval. For example, to be approved for an MRI of the knee, a patient must have already attempted anti-inflammatory medication and physical therapy and had an X-ray. Based on such parameters, payers can build a system for automatically greenlighting incoming requests that meet those conditions, greatly reducing the workload. Those that don’t meet the criteria would get flagged for manual review. 

2. Create a standard for prior authorization submission.

One of the biggest issues with prior authorization is that every payer has different requirements. Requirements can differ even within the payer’s own system based on variables like geography, provider group and more. That means providers must figure out the process each time, binding them to a manual, labor-intensive system. An automated system would establish a baseline protocol for submissions. For example, providers could see a checklist of what’s required in the submission workflow based on the payers’ specific parameters, drastically reducing the back-and-forth that frequently ensues when providers fail to initially submit the required documentation. 

See also: 3 Steps to Demystify Artificial Intelligence

3. Enable system and data interoperability.

Lack of interoperability has prevented automation of the prior authorization process. While the data required to easily approve prior authorization requests is very often contained in a provider’s electronic health record (EHR), the provider can’t easily share it with the payer for review. The data has to be relayed via fax. Interoperability is essential for the application of AI in prior authorization, and the right platform must have widespread interoperability with every major EHR to enable automated, electronic review of records. 

4. Digitize unstructured data.

Some 80% of the data contained in roughly 1.2 billion clinical documents created every year is unstructured, in the form of handwritten medical charts, physician notes, forms and scanned documents. But most payers’ systems can’t read and analyze this data, even though it contains vital details required for prior authorization review. The use of AI would require a system that could digitize and analyze this unstructured data to read and identify the requisite parameters for automated approval. This capability would also have additional data analytics benefits for overall population health and care planning, such as spotting trends, correlations and effective new treatments.

5. Consider social determinants of health (SDoH).

These contextual factors, like socioeconomic status, education and access to care, can play a significant role in patient outcomes. But most EHRs and payers’ systems don’t consider these factors, preventing providers and payers from making the most informed care decisions. By integrating SDoH data from established sources, AI-based prior authorization systems can consider these factors as part of the approval process and flag requests for manual follow-up that meet certain conditions. 

See also: The Intersection of IoT and Ecosystems

6. Put submission and approval at the point of care.

By streamlining and accelerating the prior authorization process, AI can slash time spent per transaction from 20 minutes down to just six and in some cases deliver near-instant approvals. This acceleration means the request for authorization can happen at the point of care within the patient visit workflow, reducing time to treatment and treatment abandonment.

Deploying AI to solve the prior authorization problem would dramatically reduce the time and cost associated with this necessary, but cumbersome, part of healthcare. In fact, studies show that automation could cut the cost by nearly 73%, from nearly $10 per transaction to under $3 — a huge impact on lowering the most costly healthcare expense.

With the push to implement AI reaching a crescendo, now is the time for organizations to act or risk getting left behind. We owe it to providers and patients to take advantage of every opportunity to reduce their burden and deliver better care with a better experience at a lower cost.

A 2020 report from LIMRA found 54% of all people in the U.S. were covered by some type of life insurance. A 2021 report from the same organization found a mere 14% of Americans were covered with a disability insurance policy, down from 16% in 2020.

But which product — life or disability insurance — is more likely to be needed by a policyholder? 

During the average career, someone is 3 1/2 times more likely to become disabled than they are to die.

It’s an eye-catching incongruity. 

Then consider that many Americans live paycheck to paycheck and are woefully unprepared for an injury or illness that prevents them from earning steady income for an extended period.

A recent study from Breeze found 47% of Americans couldn’t cover a $1,000 expense with just personal savings (57% of women and 38% of men). The study also found the plurality (29%) of employed respondents could only last one to four weeks on both savings and debt if they suddenly stopped earning an income. 

Despite the huge need, disability insurance remains a very small slice of the insurance marketplace. Only about $430 million in disability insurance premium was written in 2020 compared with almost $200 billion in life insurance premiums in the same year.

Why?

For too long, disability insurance has been hurt by outdated technology and sales strategies, as well as a lack of clarity around the product. 

See also: Managing Absences for Disability Insurance

Legacy insurance carriers have lacked a direct-to-consumer platform (D2C) to provide disability insurance to consumers. With the majority of disability insurance sales still coming through the traditional network of brokerage general agencies (BGAs), this product has been sold the exact same way for over two decades.

Insurance is going the way of quick, online quoting that uses data and predictive analytics to not only approve or deny applicants but also determine their policy details. But disability insurance is playing catch-up. Carriers have lacked the data science capabilities required to quickly, digitally and accurately match consumers to a disability insurance policy based on occupation, health and other personal factors. 

That lack means certain occupations cannot get fairly valued disability insurance quotes in a streamlined manner and are possibly pushed away from purchasing. 

Look at the increasing number of 1099 workers. They may want disability insurance because they do not receive the same protection as full-time employees and may change jobs frequently.

However, carriers have lacked the data analytics to accurately and quickly price out a disability insurance policy for 1099 employees because there are so many variables at play. 

Selling disability insurance has long been complicated because there is widespread confusion about what the product is, the benefits it provides and how the underwriting process works. The potential to grow this market just through consumer education is massive. But agents and brokers have stayed away because of the complexities involved in underwriting.

See also: What Is Happening to Life Insurance?

At Breeze, we’re working to grow the disability insurance market through a direct-to-consumer platform that uses data analytics to accurately and quickly quote policies in an entirely digital manner across a wide variety of professions and backgrounds. The legacy carriers we work with can still sell disability insurance through the traditional agent and broker network but now have a D2C marketplace that can hit consumers in every corner of the country. Agents, brokers and BGAs can also use our platform.

The potential for disability insurance is massive -- and the more that activity can move online, the more consumers can be reached.

The managing general agent (MGA) and managing general underwriter (MGU) business has exploded in the past five years. Today, MGAs and their MGU cousins account for approximately $60 billion in premium flow, up from $25 billion in 2012. Given that agents and brokers need to partner with MGAs to deliver expert insurance solutions for their most complex and specialty clients, that growth is hardly a surprise.

However, the agent/broker channel is experiencing record consolidation that may present new challenges to MGAs’ expansion plans. At the same time, changes across the insurance ecosystem, from an increase in direct-to-consumer models to disruptions caused by insurtechs and new entrants, are poised to alter the distribution landscape. Without strategies focused on technology innovation and investment, MGAs could face business roadblocks as distribution changes.

A recent SMA research report, “Distribution Technologies for MGAs and MGUs: Current State and Future Plans,” examines how MGAs are approaching technology innovation and investments to expand their market shares today. The five different digital sales-oriented capabilities and nine servicing capabilities analyzed in the report offer enterprise-wide insights on how technology solutions can help MGAs expand their business with new and existing distribution partners. Results from a survey of MGA executives also highlight critical areas affecting distribution plans, including the biggest challenges when implementing technology for partners, the types of offerings available in the market and where MGAs are investing in digital capabilities today.

SMA’s research found that not only are MGAs anticipating distribution changes in the coming years, but they also are prioritizing investments to improve the customer experience for agents and brokers, including deploying new digital capabilities and enhancing existing ones. (The satisfaction MGAs feel about the performance of digital offerings vary, with mixed results across all the sales and servicing capabilities examined.)

In some cases, most MGAs expressed more dissatisfaction than satisfaction with the capabilities offered to distribution partners. For example, on the servicing side, 29% of MGAs reported dissatisfaction with the billing inquiry capabilities provided to distributors, whereas only 18% said the offering is satisfactory. The research also shed light on opportunities for vendors to offer capabilities not currently provided to MGAs.

See also: First Steps to Digital Payments Processes

Although investment and innovation challenges lie ahead, MGAs are in a unique position to embrace technology within both underwriting and distribution, with numerous opportunities to expand their footprints, enhance digital solutions and strengthen relationships. But MGAs interested in growing their market share with new and existing distribution partners must understand that agents’ technological needs are changing. Fewer agents believe digital capabilities from partners are “nice to have,” as more expect advanced capabilities to be the baseline for doing business.

The global insurance industry is making a slow but remarkable recovery from the COVID-19 pandemic. 2022 is expected to continue this momentum, facilitated by trends in insurance technology that will dominate in the years to come. Let's review.

Applied Artificial Intelligence

At present, very few insurance carriers are harnessing AI to its full potential. Regardless, the pockets where insurers have been successful in implementing AI have raked in significant ROI in various forms. For instance, 65% of insurance agencies believe that AI investments in customer experience (CX) have lived up to expectations. 49% believe that improvements in internal decision-making have likewise met expectations, and 45% say the same about innovation in products and services.

As technology matures, AI is bound to diversify and find deeper and more meaningful applications to augment business capabilities. As a result, it will continue to reengineer algorithms and core processes to make insurance more predictive and intuitive. So, whether businesses are looking to tailor their services or predict the risk or probability of an event, AI solutions can step right in and bridge projections with reality.

Process Automation and Virtualization

Robotic process automation (RPA), as we know it today, has evolved from a series of technologies such as industrial bots to machine language-driven assistants. Currently, these operate as low-code, rule-based bots targeted to perform routine, repetitive tasks.

These bots can integrate seamlessly with legacy systems and streamline everyday business processes. To achieve the latter, RPA can complement any individualistic element of insurance business operation, such as underwriting, policy management, claims and regulatory compliance. Alternatively, it can spearhead end-to-end process automation requiring minimal human intervention. 

By patching together isolated systems and optimizing business processes, insurance agencies can foster efficiency at the grassroots level. Plus, RPA executes change in minimal time, which generates immediate value and holds a certain amount of elasticity even in the face of disruption.

See also: Workers Comp Trends for Technology in 2021

Blockchain and Distributed Ledger

Insurance technology expands its reach to subsume cybersecurity through the introduction of blockchain and distributed ledger technology (DLT) to cater to trends in insurance technology. While the technologies are still up for debate to some extent, blockchain and DLT introduce greater security, transparency and immutability to operations.

Insurance service providers can use blockchain technology to cut down on administrative costs, verify payments made by third parties and install a layer of protection from fraud. DLT can also come in handy while implementing traceability for quick audits and inspections.

According to a survey conducted by MarketsandMarkets, insurers plan to deploy such solutions primarily for:

• Identity management and fraud detection
• Smart contracts
• Payments
• Governance, risk and compliance management
• Claim managements

Mobility Solutions

Insurance tech gives birth to a distributed infrastructure having a centralized database. And as companies are no longer chained to their on-premise equipment or technology, they can finally explore flexible, mobile, and next-generation options.

In this respect, cloud architecture will play a greater role in maintaining information accessibility with a high degree of modularity, as expected from such applications. In addition to centralizing data, insurers can launch agile products and maintain computational and operational scalability. 

Mobile apps will break new ground in improving CX by offering a bouquet of services -- from policy management to claims settlement -- at the customer’s fingertips. Nearly 74% of insurers are banking on mobile app-driven convenience to ramp up customer satisfaction levels, partly by making it easy for customers to contact insurers. Such apps will unlock multiple channels and entry points for data collection, which will enrich the database.

Connected Networks

The future points toward greater connectivity that goes beyond smartphones. Whether you view it from the smart home/smart devices angle or consider the case of telematics, the key piece to the puzzle lies in connected networks. As such, the Internet of Things (IoT) will continue to remain one of the most prominent trends in insurance technology. 

Insurance companies, across varying segments like life, auto, health and P&C, can harness first-hand data obtained from such networks to gain a holistic understanding of the insureds. Businesses would be laying the foundation for greater customer satisfaction, improved accuracy, better risk assessment and several other non-tangible benefits, in addition to the most obvious and quantifiable metric-- revenue.

See also: Technology and the Agent of the Future

Closing Thoughts

For 2022, we can expect the continued upswing of insurance technology, influencing core products, services and functions.

Insurance in the U.S. goes back to the mid-1700s and Benjamin Franklin. It has become one of the most essential parts of our lives and one of our most important economic industries. We depend on insurance companies and policies to protect us and our assets in times of loss and catastrophe. As it is such a critical piece of our social and economic fabric — it is also one of the most regulated and scrutinized industries — we fundamentally want and need to trust insurance.

For the most part over the centuries, consumers and businesses who purchase insurance have felt a relative transparency and obvious correlation between the relationships of risks and insurance; if you live in a flood zone or have a history of speeding tickets, insurance costs more. However, as carriers are touting proprietary advancements in big data and artificial intelligence (AI), insurance becomes more complex, and questions arise.

As society at large is challenging a lack of equity and fairness across races, genders and social statuses, insurance, too, is under scrutiny. Exactly what “big data” is being used, and how are those factors influencing model-based decisions about prices or coverage? There is an expectation to prove fairness and sometimes to “eliminate bias,” but delivering on this expectation is not so simple. In actuality, it is IMPOSSIBLE to eliminate bias from insurance. Insurance fundamentally needs to be biased; it needs to bias away from unreasonable risks to be financially feasible. Insurance can, however, put processes in place to mitigate disparate impact and unfair treatment.

So how does insurance move forward in a world not simply expecting proof of fairness but also an unrealistic expectation of eliminating bias? The solution has to come from and live within a corporate prioritization framework and a cross-functional lifecycle approach to model governance.

Prioritize Fairness as a Pillar of Corporate Governance

Data and model governance (AI governance) needs to be a C-level priority. Committing to fairness and transparency is a corporate responsibility. Managing AI risks like bias is a business problem, not just a technical problem.

Mitigation of unfair bias needs to be incorporated into compliance and risk concerns of the board and enabled through strategy and budget by the C-suite. The best strategies fit within a broader vision or plan, and, in this case, incorporating mitigation of bias aligns well with ESG or CSR efforts. As the SEC, regulators and investors demand more attention to these areas, executives have a unique opportunity to take advantage of the momentum and incorporate data and model fairness as central tenets of corporate governance. Leadership can ensure that AI governance is properly funded to deliver results and avoid the challenges of distributed ownership and budgets across the company.

Finally, it’s important to promote and celebrate these efforts externally. Show consumers and regulators evidence of your awareness and investments in building greater oversight and accountability of your organization’s use of data and modeling systems. Sharing these efforts is investing in brokering trust and confidence -- important and lasting competitive advantages.

Establish Stakeholder Alignment and Shared Lifecycle Transparency

When it comes to AI and other consequential decision systems, the technical nature of the work tends to silo the essential stakeholders from one another. A line of business owner greenlights the project. A team of data scientists and engineers develop on their own. Risk and compliance teams come in at the end to evaluate a system they’ve never seen before. Such a pattern is a recipe for bias to enter the equation unknowingly.

To combat this, companies need to invest time and effort in creating transparency across teams, not just in the decisions that their models are making once deployed but also in the human processes and human decisions that surround the model’s conception, development and deployment. Every person involved with a project should have access to the core documentation that helps them understand the goals, expected outcomes and reason that a model is the best way to solve the business problem at hand.

Once a model is in production, non-technical team members should have user-friendly ways to access, monitor and understand the decisions made by their AI and ML projects. Technologists can do much in designing their models for governance to provide more visibility and understandability of its decisions, and hiding behind the veil of the “black box” only creates more work for them in the end when they have to go back in time to explain odd or unexpected behavior from their models. Business owners should be able to evaluate system performance, know when problems of bias arise and understand the steps that were taken to identify and correct course.

See also: 3 Big Opportunities From AI and ML

Require Objective Oversight

Objective oversight and risk controls are not a new concept for your business, so continue this best practice when it comes to data and models. There needs to be a separation of duties and responsibilities between the teams who build the models and modeling systems from those functions who are responsible for managing risk and governance. The incentives are different, and so the objective motivations of mitigating risks that sit within governance functions need to be empowered and expected to oversee modeling systems. While there are technical tools being developed for data science teams to monitor, handle version control for and explain AI/ML systems, the tools are not oriented toward the non-technical, objective risk partners. The modeling team cannot, and should not, be expected to self-govern.

Thanks to the thorough approach to corporate and model governance covered above, second and third lines of defense will have intuitive and context-rich records and interfaces to discover, understand and interrogate models and the decisions the models have made for themselves. Because all of this evidence is mapped to a previously established model governance methodology, the objective governance teams can readily pass or fail adherence with policy.

Of course, this sort of objective governance and control will require front-end work and focus on collaboration, but it is an obvious and necessary approach to enhancing the fairness of systems. There’s a secondary benefit of that effort: Understanding the boundaries gives your R&D teams a much clearer path to develop systems that operate within them, thereby unlocking innovation rather than stifling it.

Perfection Is Not the Goal – Effort and Intent Are

Despite all best practices and efforts, we depend on humans to build and oversee these systems, and humans make mistakes. We will continue to have incidents and challenges managing fairness and bias with technology, but insurers can implement risk governance, transparency and objectivity with clear intent. These efforts will yield positive results and continue to cultivate trust and confidence from customers and regulators.

The job of an insurance underwriter is complex on many levels and requires a highly skilled workforce. Underwriters must possess a combination of actuarial, risk management and people skills to assess complicated policies or claims in a highly collaborative and customer-centric environment. Given that underwriting decisions involve huge cost implications, training on the job is not a viable option; these professionals must gain their experience somehow in a safe training environment, where mistakes don’t have real-world financial consequences.

Adding to the challenge are the significant technical skills needed to work with smart systems that aid in the underwriting process, and a tight talent pool generally in financial services for highly skilled employees. These combined pressures have put the lens on workforce training as a way to quickly onboard and close knowledge gaps for highly skilled employees, while keeping them engaged and invested in the job to minimize turnover.

These considerations were top of mind for the chief underwriting officer and HR managers at the Hiscox Group, a Bermuda-based global insurer with a workforce of nearly 4,000 staff across 14 countries. The company sought to hire and retain junior underwriters to help manage policies within a balanced portfolio of catastrophe-exposed, globally traded risks and less volatile retail insurance business. 

Hiscox’s novel approach in addressing this need was to engage strategic partner Attensi to create the first-ever “underwriter simulator” program of gamified learning and 3D interactive scenarios – a new approach to learning that is helping redefine what is possible for workforce training in the insurance industry.

Solving a Unique Training Need

Like other companies weighing training investment, Hiscox did not need to look far for data points to demonstrate the value: Research from LinkedIn shows 94% of employees say they would stay at a company longer if it invested in their learning and development. Furthermore, people working at companies that prioritize learning investments are 83% more likely to feel happier at their jobs.

Though Hiscox was sold on the why, the bigger question was exactly how to train this niche cohort of newly onboarded junior underwriters on the demanding skills needed to apply technical concepts throughout the underwriting cycle. This was needed to free experienced underwriters for more complex tasks as the company pursued aggressive growth targets. Hiscox set some specific educational goals that were ambitious enough to require a new approach to training that went beyond traditional online or classroom models.

See also: The Defining Factor in Underwriting Success

To begin with, junior employees needed to be brought up to speed quickly with an engaging curriculum that could teach a combination of skills needed to balance the judgment of a risk analyst and the interpersonal skills of a customer-facing financial representative. The training also had to be online and mobile – both due to COVID-19 work-from home restrictions and because many junior underwriters are digital natives with high expectations around mobile and online options.

These factors helped shape requirements around three core characteristics to guide development of the training. Hiscox and Attensi agreed that the training solution would have to be:

• Engaging through gamification and real-world scenarios. The curriculum should ideally leverage knowledge of what fascinates people about video games and incorporate those dynamics into a remote learning and training approach. And the solution should use real dialogue and lifelike office scenarios to simulate the work experience as closely as possible;
• Immersive through the use of 3D graphics and competitive challenges to drive staff to not only complete trainings but continue playing to quickly build expertise and close knowledge gaps; and
• Measurable, to reap advanced insights on the training process that go beyond a checkbox approach, allowing enhanced evaluation of employee performance and improved program efficacy.

With these requirements in mind, Hiscox and Attensi set about crafting a bespoke training solution that could fit the bill.

Creating the “Underwriter Simulator”

Attensi and Hiscox collaborated on a 3D insurance training simulator for underwriters that teaches key skills by challenging users to compete in a series of realistic and gamified scenarios via an app.

Learners were given simulated data sets for digital case studies to train around skills like account analysis, segmentation and profit improvement planning. Each case study allowed the learner to manipulate the data sets to solve the case and then deliver a custom analysis to demonstrate understanding. Multiple modules were designed to cover key areas, including equity capital, profit after tax, return on equity and net asset value per share.

Great care was taken to customize the curriculum with cross-disciplinary domain expertise from pros in game design, insurance, risk analysis, education and behavioral psychology.

To add to the realism, the simulator allows in-platform use of tools needed on the job, such as MS Excel. And, in a feature that proved highly engaging and effective, learners interacted with 3D avatars of office colleagues and stakeholders – such as brokers, chief underwriting officers and product leads – to replicate the real-world collaboration needed to conduct the underwriting process.

The results speak for themselves: Hiscox is on track to have more than 90% of the junior underwriters in the program fully trained by the end of the year, and some 85% of the participants say the training has helped them understand their jobs better by practicing in a virtual “safe” environment before working with real financial data, colleagues and customers.

In addition, many staff continue to be engaged in the competitive elements of the game – eager to repeat the training modules up to 10 times to achieve better scores and higher placements on the leaderboard. Hiscox has already used these positive outcomes as inspiration to expand the gamified, 3D simulation approach to new use cases such as sales development and internal onboarding.

See also: Underwriting in the Digital Age

Conclusion

It took aggressive growth targets and the challenge of training a unique demographic of junior underwriters as forcing functions to break new ground in workforce training for the insurance industry. In doing so, Hiscox and Attensi are helping redefine what’s possible in talent development around some of the most demanding work in financial services.

December 2021 FOCUS OF THE MONTH

Smart Home

FROM THE EDITOR

For nearly 30 years, I've been hearing about smart homes. Even before a commercial version of the internet browser was invented in the early 1990s, the rich, geeky types I dealt with in my travels at the Wall Street Journal were figuring out ways to wire their homes to ward off possible intruders. 

They'd turn lights on and off remotely to simulate being there. They'd install cameras that they could monitor from their computers -- smartphones were still a ways off even for this crowd. They'd install sensors that would alert them about any unusual activity.

What began as a sort of hobby is now going mainstream, as you can see from my interview with Matteo Carbone, who has pretty much established himself as the king of telematics, and from the smart home articles we've curated for this month's ITL Focus. 

It's still not possible to make a full-on economic argument for installing sensors everywhere to monitor for water leaks, gas leaks, intruders, etc. -- the cost of installing sensors and cameras everywhere would exceed the losses that they'd prevent. But smart home technology has declined in cost enough that many people will start investing in it for the peace of mind. And many insurers will start subsidizing the technology, both to reduce losses and to show customers that they're partners in protecting customers' well-being.

We haven't "crossed the chasm," to use the Silicon Valley term, to a true mass market, but we're well past the hobbyist phase.  

Cheers,

Paul Carroll, ITL’s Editor-in-Chief

INTERVIEW WITH MATTEO CARBONE

As part of this month’s ITL Focus, we spoke with Matteo Carbone, founder and director of the IoT Insurance Observatory, on the intriguing opportunities that are taking shape for smart homes.

“Today, we have the same mood among insurers about the smart home, and I’m telling people not to make the same mistake that they made on personal auto telematics.”

Matteo Carbone 

WHAT TO READ

Perspectives of a ‘Smart Home’ Owner

Insurers that focus on second homes, vacant homes or certain commercial properties should be developing strategies now.

Read More

Smart Home = Smart Insurer!

The one technology that is both the most opportunistic and the most misunderstood, is the Internet of Things (IoT) for smart homes.

Read More

Home Is Where the (Smart) Hub Is

As the smart home gains momentum, insurers need to do three things to begin to understand the implications and opportunities.

Read More

Smart Home Devices: the Security Risks

Smart devices often represent the most vulnerable point on any given network, exposing customers and insurers alike to potential risks.

Read More

‘Smart Homes’? Not Just Yet

After years of asking, I finally have an answer about the economic argument for "smart homes" -- just not the answer I wanted.

Read More

‘Smart’ Homes Can Have Stupid Features

"Connected homes" allow for, say, remote control of lights but can undercut improvements in alarms and leave openings for hacker vandals.

Read More

WHO TO KNOW