The classical insurance business model has been successful for a long time but does not stack up to modern standards. Nowadays, just a policy no longer provides the best solution for managing risks. Why wait until something happens, when the technology and data are available to actually reduce the chance or impact of an unforeseen event? 

There is only one sensible way forward: to rebalance insurance and prevention. Integrate data, services and insurance policies and help customers manage risks in a way they feel comfortable with. 

I’ve named this the transformation from managing policies to managing risks. Connected Insurance, Smart Home/Smart Mobility or IOT-based insurance are relevant terms in this context. You would expect that insurers are well underway in preparing for this future.

The pressure to rebalance insurance with prevention is building

There are several drivers contributing toward the momentum for integrated, prevention-based safety and continuity solutions. Increased volatility and changing risks (related to climate, health, cyber, demography) are making safety and prevention particularly relevant and a traditional policy even less effective. The spread of connected devices and the rise of smart home offers significant potential to develop new value propositions. It’s becoming increasingly hard to explain to customers why "just a policy" is the best solution. 

Large-scale adoption is still something for the future 

I do see pockets of innovation in risk management and prevention initiatives. But the scale remains limited. It seems the devil is in the scale up. I have seen a lot of initiatives driven by motivated innovators that haven’t scaled up to serve a substantial portion of the customer base. Why? Let’s take a closer look at some barriers:

Barrier #1 – Not easy to find the right model

It’s one thing to set up a small operation or minimal viable product (MVP) safety and prevention concept that serves some customers. That already may be challenging for an average insurer, and scaling up requires something else: a sustainable business model, strong and stable partnerships and, not unimportant, insight to choose a scalable model that is attractive to customers. Too often, insurers take a plunge and create a safety and prevention solution with the best intentions, only to find it will never fly on a larger scale. That doesn’t mean safety and prevention concepts don’t work – it only proves that making the right design choices is not easy. If it didn’t work, you evaluate, regroup and try again with better design and implementation choices.

See also: A Self-Destructive Cycle in Insurance  

Barrier #2 - Chicken-and-Egg Business Case 

In decision making driven by business cases, a concept will get a fair chance when it makes financial sense within the current business model. That requires robust proof from actual data. Without data, no initiatives. Without initiatives, no data. That explains why so many risk and prevention activities remain largely driven from marketing and innovation budgets – that’s a different business case altogether. Large-scale adoption of prevention concepts is therefore limited to customer segments with high risks (e.g. young drivers), markets with mandatory safety requirements and those insurers that have a particularly effective internal prevention champion. Important, admirable but hardly fertile soil for building large-scale prevention concepts.

Barrier #3: Stove pipes blur the 20-20 view

What will be the total damages of a two-sided car accident? The actual impact may be much higher than is covered in the two associated car insurance policies. Indemnity, medical costs, inability to work, impact on employability, economic activity and personal life: The actual amount of the damage affects auto, health, income and liability business lines across multiple insurance companies. Different insurers handling parts of damages and liabilities not only increases overall costs but blurs the total costs to society of such an event. Prevention concepts should be judged with total costs to society in mind, not only by individual insurers from the perspective of an individual business line. 

Barrier #4: Important but not urgent

In many insurance organisations, a lot of change capacity is required to tackle legacy and regulatory requirements. A shift in business model is not something you can do on the side. Organizations should find the balance between innovation horizons and separate existing and new businesses, but that’s easier said than done. And let’s not forget: there might be an initial fear of cannibalization if premiums go down because of higher safety. 

Jumpstarting the shift toward building better solutions to manage risk 

Let’s create effective partnerships with players that jointly invest in safety and prevention. There must be a winning combination among insurers, re-insurers, Big Tech/IoT firms, players in adjacent industries, insurtechs, academic communities and non-profit organizations. 

Such a rich eco-system should be able to integrate data across traditional product lines, companies and other stakeholders to balance investment decisions with more weight attributed to the total costs to society. 

The recently launched OPIN initiative may provide the much-needed lubricant by developing open standards and API definitions for data exchange, enabling a coordinated approach across regions, markets and traditional business lines.

Transforming a business is always challenging, but even more so when you’re in it. No single player in today’s value chain is going to succeed on its own. The key is to create a collaborative solution combining the strength of several players. The future belongs to those who can forge such an alliance.

 

Although we hear a lot about major cybersecurity breaches in non-insurance organizations – Target, Experian, the IRS, etc. – there have been breaches in the insurance industry, too, albeit less publicized. Nationwide faces a $5 million fine from a breach back in 2012. Horizon Blue Cross Blue Shield is still the defendant in a class action suit over a 2013 breach that affected 800,000 of its insured. 

As hard as organizations try to secure their data and systems, hackers continue to become more sophisticated in their methods of breaching. This is why innovation in risk management and insurance is so important.

Can Machine Learning Improve Cybersecurity (and Vice Versa)?

The short answer is yes. Because machine learning can collect and process huge amounts of data, the technology can analyze historical cyber-attacks, predict types that may occur and set up defenses against them. 

Here is a very simple example:

An on-site employee has decided to use his computer to access some shopping sites during his lunch break. One of those sites has elements that alert the machine of a potential security threat. The security team is notified immediately. It is then possible to block access permission from that computer to any data that could be useful to hackers until a full investigation can be completed. 

See also: How Machine Learning and AI Reduce Risk  

This may be a rather far-fetched example because most organizations limit private use of their computers in advance. But consider the Horizon breach – two laptops were stolen from a facility, and access was obtained. Or the case of Target, where a third-party contractor did not have appropriate security in place, and hackers were able to access the company’s systems through this third-party. Machine learning can help to reduce these threats through a proper alert system, and remote shutdowns can then occur.

Common Types of Data Breaches that ML Can Help Thwart

1. Spear Phishing

Company employees receive emails every day, in their company inboxes. Some of these, from sources that may not be known, can include malicious links. 

There are now ML algorithms that can identify and classify language patterns – email subject lines, links, body content/communication patterns, phrases and even punctuation patterns. Anomalies can be flagged, and security analysts can investigate, even catching the emails before they are opened, if the system is set up correctly. Some of these emails, for example, may be very poor translations from foreign languages, certainly not professional translations from services like The Word Point. Poor translations will alert machines that spear phishing is a possibility.

2. Ransomware

Most everyone is familiar with this security threat. Users’ files are “kidnapped” and locked. Users must then pay up to get an encryption key that will unlock those files. Often, these files house critical client data, other proprietary information or system files that are necessary for business operations. The other type of ransomware attack will simply lock a user’s computer and not allow access until the demanded amount is paid.

To train a machine to identify potential ransomware requires some pretty deep learning. Data sets of historical ransomware files must be loaded, along with even larger sets of clean files, so the machine can learn to distinguish between the two. Again, so-called micro-behaviors (e.g., language patterns) are then classified as “dirty” or “clean,” and models are developed. A ransom file can then be checked against these models, and necessary action taken before files are encrypted or computer access locked.

3. Watering Hole

Employees, especially insurance agents who are out in the field, may have their favorite spots for coffee or lunch breaks. Or, suppose, a group of employees have favorite food joints from which they frequently order food for delivery or takeout. Whether they are using the Wi-Fi in that watering hole or accessing that business’s website to place an order, there is far less security and an ideal place for hackers to enter a user’s access/credentials through that backdoor. 

Sometimes this is called “remote exploitation” and can include a situation like what occurred with Target – a third party is used as the “door” to get in.

ML algorithms can be developed that will track and analyze the path traversals of an external website that employees may be accessing on devices they are using either on- or off-site. Users can be directed to malicious sites while they are “traveling” to a destination site, and this is what ML can detect.

See also: How Machine Learning Transforms Insurance

4. Webshell

A Webshell is nothing more than a small piece of code. It is loaded into a website so that a hacker can get in and make changes to the server directory. The hacker then gains access to that system’s database. Most often, hackers look to take banking and credit card information of customers/clients, and this type of attack occurs most often with e-commerce websites. However, medical practices and insurance companies are certainly at risk, too, because they house lots of personal data. When the insured set up automatic payments from their bank accounts, the activity is even more attractive to these hackers. Payments are simply routed somewhere else.

Machines can be trained to recognize normal patterns of behavior and to flag those that are not normal. Machines can also be used to identify webshells preemptively and then prevent them from exploiting a system.

The Requirement? Machines and Humans Must Work Together

Will machines ultimately eliminate the need for in-house or contracted cybersecurity experts? Highly unlikely. At this point, machines cannot engage in the deeper investigations that analysts perform once they are aware of potential breaches or once aberrant behaviors have been detected. But innovation in risk management and insurance should certainly include machine learning. Humans simply cannot gather and analyze data as fast as machine algorithms can. Incorporating ML as a solid part of cybersecurity just makes sense.

 

"Transformation" is fast becoming the next overused word in insurance, right behind "digital" and "innovation." But the fact that so many commercial lines insurers are talking about transformation indicates a reality – there is definitely fundamental change underway. It is true that the basics of the business remain the same, and many of the headline-grabbing initiatives are not yet driving big financial gains. But in those headlines and the behind-the-scenes strategies and pilots underway, there is a palpable sense of real transformation. And it is changing the industry for the better.

SMA’s recent research report, 2020 Strategic Initiatives: P&C Commercial Lines, provides more insight into this transformation. It addresses both what insurers are doing and why they are doing it. The why discusses the factors compelling insurers to embark on transformation, while the what covers insurer strategies and plans and their stage of development. Great progress is being made in the overall digital transformation as well as a dozen other initiatives ranging from improving customer engagement to building world-class data/analytics capabilities.

SMA’s observation from working on strategy with insurers is that there are actually two levels of transformation underway. We divide this bi-level transformation of commercial lines into approaches we call incremental and transformational.

Incremental transformation, Level One, is beyond business as usual. It is not just developing next year’s plans to improve the business on a continuing basis, with gradual, minor improvements to the metrics. It is more about harnessing innovation to generate ideas and approaches, take more risks, establish new roles such as customer experience or chief data officers and begin to change the culture. The objective is to accelerate the optimization of the business and achieve top-line and bottom-line results faster and at a higher level. However, at the incremental level, all activity is done in the context of the current business model and builds off of today’s growth and profitability.

See also: Commercial Lines Embracing Change  

At Level Two, the transformational level, revolutionary change takes place. The objective is nothing less than relevance and future survival. In this mode, insurers are looking at how to create value in new ecosystems, engage in new types of partnerships and achieve the next level of optimization. Considering new business models; rethinking the future roles of underwriters, adjusters and other industry professionals; and designing insurance products to address emerging risks are all part of this level of transformation. By definition, this more “earth-shaking” transformation is a greater challenge because it requires a broader understanding of the rapid changes taking place in the world at large and then translating them into likely scenarios for insurance. Bolder bets are required as part of the risk/reward equation.

What is clear is that many insurers that thought they were undergoing major transformation are now realizing that they are at Level One (incremental transformation). Leaders are trying to kick it into high gear as they launch initiatives to drive Level Two transformation. This is not to imply that the incremental transformation ends. On the contrary, it is still vitally important that insurers push forward with the incremental improvements to the business while working in parallel on more transformative activities. It is a difficult balancing act, but those that successfully move down these paths in parallel will be the winners in the next decade.

I have proudly worked in the North American insurance claims and information technology industry for over 30 years and have witnessed significant, albeit gradual, improvements in process and service. More recently, almost overnight, the switch of information gathering and distribution from analog to digital has transformed claims.

Mobile consumer technologies have been adopted rapidly, disrupting sales and distribution models and their supply chains. Instant gratification has become a natural expectation.  

Insurance Claims Process Evolution

In insurance, claims remains the “moment of truth.” Claims represents the best single opportunity for an insurer to engage personally with its customer, satisfy the coverage promise and foster loyalty. But claims has begun to evolve. It has been a highly complex, labor-intensive, stubbornly long, expensive and customer-unfriendly process. But it is benefiting in a big way from new and exciting technologies, and investors are eager to place bets on a new generation of entrepreneurs who shun working for large corporations (at least until those companies acquire them for mind-boggling amounts).  

The “Amazon effect” (immediate delivery of virtually anything from entertainment to information to merchandise to food through a simple, digital interface) drives consumer expectations even for insurance claims service, no matter the behind-the-scenes complexities involved. In the fiercely competitive personal lines market, traditional carriers are responding by transforming the claims process to make it more “Amazon-like.” In the process, carriers are lowering claim costs, producing savings that can then be reinvested in lower, more competitive, insurance premiums. 

See also: Breakthroughs Finally Appearing in Claims   

The claims process is still complex. It involves many disparate technologies and services, and firms that range from very large corporations to smaller, more local providers. Integrating and streamlining all of the related interactions is not trivial. But new, low-cost computing capabilities and information management technologies are enabling the interoperability of this ecosystem, leading to an array of profound changes, including these 12.

Claims in 2020 and Beyond

• Control over first notice of loss (FNOL) will be contested as technology enables real-time accident notification. The change will allow for new influences in claim process response, resolution and the customer relationship 
• Average cost of auto repair will rise further, exceeding $3,600, as more late-model-year cars enter the car park loaded with costly accident avoidance and self-driving technology that requires post-accident scanning and recalibration. A $5,000 average repair cost is already in sight. This trend will be reinforced by strong consumer preference for more expensive light trucks and SUV/CUVs, now representing 70% of new sales, and, soon, more electric vehicles (EVs)
• As cost of repair climbs, so, too, will total loss frequency. It is now at 24% for some carriers, increasing claim costs and placing added stress on valuable claims-adjusting resources. Tech-enabled processing solutions will emerge to compress cycle times and ensure compliance, but deployment and integration will take time 
• The collision repair industry and its traditional direct repair program (DRP) relationships with insurers will change in more fundamental ways as OEM repair network certification programs gain traction, with support and encouragement from trade groups, consumer safety, legal and regulatory advocates. This, in turn, will add upward pressure on average repair costs  
• Growth in claims self-service, including auto photo estimating, will outpace other methods of inspection. The change will upend staffing models and disrupt appraisal work forces as well as the traditional collision repair referral process
• A culture of speed and transparency will develop and attract new talent, filling an important gap
• AI, including computer vision, machine learning, data analytics and automation, will begin to streamline and compress the insurance claims process, identify and deter fraud and remake related work forces and skill sets 
• Digital imagery and measurement from aerial to drone to ground-based will permanently alter the property claims estimation, settlement and repair process. The change will create new strategic partnerships between carriers and third-party providers and transform the property claims field and desk appraisal
• On the journey toward touchless claims, carriers are realizing that they need to deliver empathy at scale. They will leverage intelligent platforms while recognizing and deploying emotional data – and achieving a proper balance between digital and human touch 
• CEO-led diversity and inclusion initiatives will become critical to attract talent in claims and boost organizational performance across the enterprise as well as increase competitiveness in the market
• Emerging technologies getting greater attention and testing in claims use cases will include virtual and augmented reality (VR/AR) for staff training, field service and support
• Success in the art of developing and managing effective strategic alliance and partnerships and collaborating with others, including competitors, will become table stakes for innovation 

Collaboration in Action

Now, more than ever, cross-industry collaboration across the vast claim ecosystem is critical to delivering an efficient, high-quality, low-cost claims experience to policyholders. One excellent forum for such collaboration is Connected Claims USA Summit, taking place this year in Chicago on June 24-25. As chairman of this exciting event, I invite you to join us this summer to discuss and learn how to move from strategy to action and implement the future of claims today.

See also: Future of Claims Intake for Insurance?  

The chart below compares the government's Bureau of Labor Statistics' inflation calculations for Medical Care versus the Kaiser Family Foundation's research into how much insurance premiums have been increasing. The differences between the two calculations are huge. From 1998 - 2018, the government estimates that health costs have increased by only 107%, but for some reason insurance premiums have increased 288%. In fact, 288% is a material understatement, because that figure does not include the huge increases in deductibles.

One might say, "Well, this means the insurance companies are overcharging!" That is a possibility, but if the insurance commissioners of America are that bad at reviewing rate filings, which I doubt, then all the insurance commissioners and their staffs should be replaced ASAP. Another reason I don't think the difference can be accounted for by declaring insurance companies are grossly overcharging is that the ACA to some degree limits their profit margin, and a review of their financials finds profit margins that do not suggest massive overcharging.

Another perspective is that government-sponsored healthcare expenses do not increase nearly as much as the costs covered by insurance companies. Medical care is medical care, unless if under government programs patients get materially less care or the insurance companies subsidize government programs by overcharging everyone that buys their own insurance.

A third alternative is the Bureau of Labor Statistics' numbers are just plain wrong. I trust the Kaiser numbers because they are associated with Kaiser Permanente Insurance, so they know what premiums are being charged. Premiums are easier to verify, too.

See also: The Science That Is Reinventing Healthcare  

In your day-to-day world, what difference does all this make? Maybe none except by adding to your humor or frustrations. Or, perhaps the difference adds to your conspiracy theories. In selling benefits, though, I think seeing the discrepancy helps the intelligent and educated producer sell and advise the educated and intelligent buyer. Understanding the true inflation of medical care will help people make better decisions.

Having spent seven years helping major insurers with digital transformation, I’ve seen the good, the bad and sometimes the downright ugly in practice.

The bad and the ugly include:

• Digital "strategies" disconnected from the priorities of the business;
• Inconsistent approaches across different parts of the insurer, confusing customers;
• Gaps and overlaps in delivery, wasting both opportunities and resources;
• Digital approached as a project rather than as a root-and-branch transformation; and
• Lots of activity but little practical achievement.

All of these issues, and more, can be avoided by implementing a model for sustainable digital transformation.

I offer you my version of such a model, below.

1. The Customer

Ultimately, all premiums and other revenues flow from the customer, hence placing the customer at the center of the model. Without strong focus on the customer, the digital transformation won’t be sustainable.

For any digital initiative, the insurer therefore needs to ask what the impact will be on the customer and design or re-design accordingly.

That doesn’t mean that the customer should be the only focus. There’s still scope for using digital to reduce costs, increase efficiency and generate new revenue streams - just not at the expense of the customer.

2. Business Strategy

How the insurer seeks to serve the customer will be set out in the business strategy. This should therefore be the starting point for the insurer’s digital transformation.

To be sustainable, any digital strategy has to be rooted in the business strategy. To give but one example, digital claims would look very different in an insurer competing primarily on price than in an insurer competing primarily on personal service.

3. Digital Vision

The digital vision expresses how digital will help deliver the business strategy.

The vision can take many forms, such as narrative statements, depictions of the future state and key re-imagined customer journeys. But much more important than the format is that all key stakeholders must understand, buy into and be able to expound the digital vision.

The digital vision provides the glue for everything that follows.

See also: 3 Phases to Digital Transformation  

4. Digital Design

Once all key stakeholders are aligned behind the vision, the next step is to drill it down to a level of detail that is deliverable. Again, the precise methods and tools for doing so can vary according to the particular needs of the insurer. But in my experience a focus on customer journeys or user stories is almost always a powerful component.

What is critical is to ensure that all key stakeholders, across the insurer, are aligned behind this more detailed digital design as well as the higher-level vision - not least because trade-offs between different functions and business units are likely to be required.

If key stakeholder alignment isn’t achieved at this relatively early stage, the digital transformation is unlikely to be sustainable.

5. Digital Capabilities

Only now should the insurer ask what it needs to put in place to deliver its digital transformation - the digital capabilities required.

These can, and should, be wide-ranging, encompassing culture, people and processes as well as the requirements for new and improved technology.

The digital capabilities provide the bedrock for the digital transformation, and are likely to be fairly stable over time.

6. Road Map

Once the insurer knows What capabilities are required, the next step is to establish the How and the When. How will any required culture changes be made sustainable? Should new people be brought in, or can existing staff be re-trained? For technology capabilities, should the insurer buy on the open market, build unique capabilities in-house or rent from others in the increasingly abundant insurtech ecosystem?

More controversial is likely to be the When. By now, everyone should be excited about the digital transformation and be keen to get on with it. Unfortunately, not everyone can be first - so compromises and trade-offs will again be required. Otherwise, the digital transformation is liable to collapse in acrimony in its early stages - and never deliver in the first place, let alone be sustainable.

This is also the point at which the digital transformation’s relative priority against other strategic initiatives comes into play. Reaching alignment on the trade-offs between digital transformation and other critical programs will be essential to the sustainability of the digital transformation.

7. Delivery

And now the insurer merely(!) needs to deliver to the road map. As with any transformation program, this won’t be simple - but the same sorts of approaches, tools and techniques apply, so I won’t go into that further here.

8. Review

Many digital transformations end with delivery. But that’s a mistake. Because the world moves on, and the digital transformation of an insurer is rarely complete.

To ensure sustainability, it is also critical to implement a process of continuing review - seeking feedback from customers, assessing financial and other outcomes, considering potential improvements and translating what is learned into updated visions, designs, capabilities and road maps as appropriate.

Without this step, both the digital transformation and the insurer itself will stagnate - losing the benefit of all the hard work done to that point.

See also: Culture Side of Digital Transformation  

9. Change Management

Surrounding steps two to eight in the model, you’ll see a ring titled "change management."

Having now read through those steps above, you’ll see why.

Multiple times I’ve used terms such as alignment, understanding and buy-in. And no digital transformation program will be sustainable without its key stakeholders acting in harmony to achieve common goals.

Key to sustainability, therefore, is the establishment and use of a high quality change management capability within the digital transformation program.

I won’t go into that more in this article, but you can see some of my thoughts on change management, within the context of digital transformation, here: Digital Change Management and Adapting OCM for an Increasingly Digital World.

10. Governance

Finally, no insurer’s digital transformation program is likely to be sustainable without the underpinnings of good governance.

This includes multiple elements, but experience shows that the most important to get right are:

• The RACI Matrix for Digital, showing exactly who is responsible and accountable for what, across the insurer’s functions, lines of business and transformation capabilities;
• An accompanying target operating model; and
• The processes and cadences for managing the digital transformation, at both the strategic (vision, capabilities, design, road map and review) and tactical (continuing program delivery) levels - including financial management.

* * *

None of the above is rocket science, yet to this day few insurers, globally, have implemented a truly sustainable digital transformation.

I hope that, by publishing this simple model, I am providing some help to those who find themselves struggling.

Most insurers won’t, of course, be starting from a blank sheet of paper, so the model will need refining to meet the particular needs of each insurer.

Although I often remind people that ITL isn't a news site, I'm going to make an exception here, because I want to be sure you saw the news about ITL that opens up major opportunities both for the website and for this Six Things newsletter. 

And, yes, there is a connection to blockchain. Even if I'm indulging in some news, I still don't believe in clickbait. 

The news is that The Institutes has bought the Insurance Thought Leadership brand and publishing assets, including this newsletter. (Press release here.) As a result, I will have access to a slew of thought-leading resources, including a number of publishing properties and events that The Institutes has acquired in recent months and years. Among them: the International Insurance Society, run by my old friend and colleague Mike Morrissey; Risk & Insurance; CLM, which helped launch TI's The Future of Risk conference last year; Claims Pages; and the Pacific Insurance Conference. 

I'll also be able to draw on the various repositories and generators of knowledge at The Institutes. That begins with the group that prepares all the educational materials that TI provides as part of the designations it grants but also includes the Insurance Research Council, the Griffith Foundation and the RiskStream Collaborative.

I'm the newest of the newbies, and the integration of ITL with the mother ship is in its early stages, but expect to see thoughts and thinkers from these other TI assets start to show up in what we do at ITL, and perhaps to even see some migration from ITL into these other venues as we all work to improve the state of play in insurance.

Which brings me to blockchain.

While I've seen great potential in the technology for years, I've been waiting to see it move beyond the theoretical and into the market, at least in significant tests. I haven't exactly been surprised as concerns have surfaced, in particular about the speed with which transactions can be processed on a blockchain, a means of keeping encrypted public records that can be updated and accessed by relevant actors and, at least in theory, can't be altered. Such concerns always seem to surface about technologies that promise such fundamental change—one of my favorite lines from Silicon Valley is, "Never confuse a clear view with a short distance." 

So, I was intrigued at a planning meeting two weeks ago when the leaders of RiskStream said that, after a long gestation, they are about to take to market a real, live test of blockchain. (Here is a white paper they prepared.) Of the four use cases, the two that struck me as having the most immediate promise relate to first notice of loss and proof of insurance for drivers. 

With blockchain, insurers will be able to populate an encrypted public record with up-to-the-moment information on whose auto insurance is current. A decryption key to the record can be provided via an app to anyone with the right to see that information, in particular a police officer or motorist/insurer involved in an accident with the person providing the proof of insurance. Blockchain thus provides a way to tie together insurers' disparate systems in real time, without potentially outdated paper cards, and should reduce the number of motorists who drive without insurance.

For first notice of loss, blockchain will provide a sort of spine to which the various aspects of the claims process can attach. To start, those elements will be the initial information gathering, including the conversations with those involved in an accident to gather their version of what happened. By creating a shared record for the insurers to access via decryption keys and then process via their own systems, RiskStream expects to reduce the number of calls between the companies, which can be numerous and time-consuming. Over time, RiskStream hopes to add elements to the blockchain, including dealings with collision-repair shops. 

Don't expect miracles. I certainly don't. But at least blockchain and the market will start to meet each other. I'll be fascinated to see what happens—and I'll let you know what I learn.  

Cheers,

Paul Carroll
Editor-in-Chief

P.S. I'm sorry to no longer be working on a daily basis with the great cast of characters (and I do mean characters) who did so much to help with the publishing side of ITL before the acquisition by The Institutes. Looking at you, Dave Dias, Wayne Allen, Paul Winston, Guy Fraker and Joe Estes. Many will continue on with the other part of ITL, now known as IE Advisory, which will continue to counsel on innovation best practices, drawing on its database of insurtechs and other companies whose technologies could affect the course of our industry. I wish you guys all the best, and I'm sure we'll all see each other around the campfire.

Today’s era of total mobility has seen insurance consumers increasingly demanding to receive customer service across a number of different channels – each driving an exponential increase in data volumes. Bombarding systems with queries and operational tasks like never before, this surge in traffic is taking its toll on insurers’ back-end systems. A new architecture, the digital integration hub, is rapidly coming into focus as the ideal solution to ensure that legacy back-end systems are not compromised while enabling U.S. insurers to digitalize their customer and intermediary interactions.

Rewind to just 10 years ago, and insurance providers in the U.S. comfortably relied on the telephone and e-mail. Since then, the pace of technological development has spelled out an ever-increasing number of touchpoints, creating pressure from consumers and intermediaries to deliver a better, faster, more intuitive user experience. In 2019, 23% of insurance executives said enhanced customer experience was their primary indicator of marketing success to support their acquisition and retention efforts (66%) and to increase personalization (60%). Customers expect that information should forever be at their fingertips, through an app or an instant messaging (IM) service. Modern consumers expect a seamless customer experience in which one conversation can be carried out, successively and simultaneously, across various platforms.

Intermediaries, brokers and direct insurers as well as a growing number of third parties are increasingly responding by striving to provide their consumers with information, modeling and fast processing of claims across different devices and channels. As a result, frequency and volume of system interactions across multiple channels are surging. More than ever, systems are being interrogated through a huge range of operations.

Risk arises because the data required to fulfill these queries is stored in back-end systems that also contain business-critical information such as customer data. These back-ends must be kept safe from third-party access – and third-party activity must not interfere with line-of-business systems by provoking unpredictable peaks in queries. Although new front ends have been designed to be remain independent from back-end legacy systems, the front ends often put pressure on the legacy systems, occupying machine time on low-value activity rather than core operations.

See also: 6 Implications of Big Data for Insurance  

New technologies and automation also pose great risk to existing infrastructures. For instance, new front-end development technologies such as single-page-web-application, html5, css3, angular, react and progressive eb apps (PWA) struggle to operate at the required speed and highlight the limitations of legacy systems. Additional data sources, from distributed ledger technology, big data, IoT, cloud computing, AI or biometric technology heighten the issue of handling growing data volumes.

So how can insurers leverage the powerful potential of application programming interfaces (APIs) without placing their back-end systems in jeopardy?

A new architecture centered on digital integration hubs has distinguished itself as a more efficient system and a means to overcome this challenge. In this new architecture, APIs read data extracted from a "data lake," which is perpetually updated in near-real time by the legacy systems rather calling data up from legacy systems directly. The opportunities to feed data into the data lake from other sources such as IoT is endless and presents a game changer for the industry.

When gathering data from legacy systems, traditional data management platforms tended to be based on a batch approach, which updated data in the data warehouse (DWH) on a daily basis rather than in the near-real time way offered by digital integration hubs and require hundreds of extraction and ETL procedures. While traditional DWHs can be useful for analytics and reporting, their more infrequent pulling of legacy data makes them unsuitable for customer-facing front ends. Reducing the complexity of the API service layers, digital integration hubs allow for historical and new real-time data to be fused into a single repository that APIs, rather than the back end, can interact with. During peak activity, the digital integration hub can handle the load and leave back-end systems unaffected.

Other advantages that come with digital integration hubs include their provision of 24/7 real-time data and intuitive Google-like search functions. Brokers can seamlessly access data through dashboards or "cockpits" that allow them to manage customer profiles based on a 360° view. Through system integration, flexible end-to-end solutions can help insurers to embark on their journey toward digitalization and meet customer expectations without putting their systems at risk. Insurers should embrace the opportunity to introduce a digital integration hub and connect legacy back-end system integration with new technology and data-sources.

See also: 4 Trends in Insurance in the New Year  

Today, there are some vendors on the market that can provide one or two of the elements composing the solution, but end-to-end solutions offering all of the above benefits are much less common. Paying attention to the specific needs of the client, experienced consultants can be helpful in selecting and combining different options to fully realize the benefits offered by digitalization.

To find out more, download the latest whitepaper from Fincons Group here.

Insurance systems need to talk to each other. They must be able to store, share, retrieve and use the same data. Data should flow unimpeded from the first collection of information, from a prospect or census, through underwriting to policy administration to claims. Failure to integrate data adds cost and complexity and introduces errors. These errors can slow everything down, potentially leading to loss of business in an increasingly competitive environment for employee and voluntary benefits.

Integration with many other systems is a must. Insurers often have a variety of best-of-breed systems: sales/underwriting, CRM, policy administration, claims, enrollment systems, risk and lead scores and self-built software. No one wants to re-enter data. Everyone requires an automated streamlined solution.

Systems today often still can’t use the same data for a variety of reasons.

Legacy systems for employee benefits may still be great workhorses, but they are less flexible. It takes extra work to get them to communicate with other systems. Insurers that have gone through a merger may have two sets of systems and often find their systems are incompatible. This means data must be re-entered multiple times.

Even if carriers decide to implement their own integration, the dynamic nature of the group insurance market can quickly make a recent system integration obsolete. For example, carriers may be forced to consider a new insurance product or to retrofit old ones to meet the market demands. Usually, such changes will trigger a cascade of updates for many, or sometimes all, integrated endpoints. Micro-services can alleviate this kind of problem. Breaking down software into smaller components can lead to better modularity, which in turn may reduce the implementation effort because smaller portions of the system have to be changed.

Sometimes even micro-services are not enough – many carriers have implemented complicated data pipelines with complex business logic. Changing or updating a single stage in this pipeline can thus have dramatic consequences on any downstream endpoint.

This is where an exchanger platform can be really helpful: Instead of software updates and changes in micro-service application programming interface (API), exchanger software lets carriers easily change or update the data structure that flows through the pipeline.

See also: 3 Phases to Digital Transformation  

Exchanger software must be designed with compatibility in mind: both backward compatibility (compatible with data structures produced by any older version) as well as external compatibility.

Managing data flow is a growing priority for both IT and business users. And each of those groups of users has specific requirements and constraints. IT users are focused on data formats, data security and system performance, while business users are more focused on business rules and data validation.

Each of these aspects must be configurable in the exchanger platform. One particular characteristic of integration systems for group insurance systems is the size of data that often flows between endpoints. For very large groups with a complex insurance product structure, the amount of exchanged data is very large. For this reason, the exchanger software can operate in both synchronous and asynchronous mode with built-in protection against system overload. Data flowing through the transformation pipe can be formatted in either XML or JSON and can be restricted to certain users, based on their authorization level.

The exchanger platform offers a powerful tool to build more specialized applications that fit more specific needs. Many carriers are now embracing cloud solutions like Salesforce or Amazon Web Services (AWS). Although in the long run this reduces IT operating costs, it still requires integrations with existing systems that are not yet deployed in cloud-like policy administration, claims, payroll and archive.

For all these endpoints, insurance carriers should now be able to use one of the many connectors built on top of the exchanger platform. Connectors are specialized applications ready to be deployed and integrated with a specific endpoint. For example, the Salesforce connector allows bi-directional communications with Salesforce cloud applications. Salesforce users can leverage a Salesforce connector to initiate "ratable quotes" and receive final rates whenever these are made available by the carrier rating system.

Data-exchange standards should encompass data aggregation, format and translation and frequency of delivery.

Without standards, chaos can develop, and costs can ratchet up. Unfortunately, data-exchange standards have not become universal. Industry groups such as LIMRA, CLIEDIS and ACORD are trying.

See also: Beyond the Digital Transformation Hype

One encouraging sign of progress: In 2019, LIMRA launched the prototype of the LIMRA Workplace Benefits Electronic Data Exchange Standards. This is something we look forward to seeing develop as we enter the next decade.

Reprinted with permission from the Jan. 16, 2020, issue of www.propertycasualty360.com ©2020 ALM Media Properties, LLC.  All rights reserved. 

Recent developments in robotics and artificial intelligence have changed the playing field for automated technologies. (Here is an earlier blog on the topic.) Historically, automation was beyond the reach of small and medium-sized companies. Robotics were costly, required highly sophisticated programming expertise, took months to integrate and could only perform single, discrete tasks.

In 2012, the advent of artificial intelligence (AI) was a game changer. AI brought collaborative robots to the market -- robots that see and feel like humans, learn (including integrating new data sets and information) and perform multiple tasks. These collaborative robots are also more cost-effective and easier to integrate, making them available and attractive to small and medium-sized businesses.

AI and robotics are now transforming many traditional labor-intensive industries, such as farming, construction, factories and fast food. While Amazon continues to be a global leader in leveraging AI and smart robots, there are plenty of examples of smaller businesses across the country embracing these new automated technologies.

Agricultural farms are using automated tractors and drones to help with growing their crops. Construction firms are purchasing automated brick-laying machines (to lay 3,500 bricks per day). Restaurant owners are investing in new automated machines that can store, prep and cook fast food in a highly controlled environment without any human intervention.

If the adoption of these new automated machines continues, there will be fewer jobs and payrolls in these industries. Over time, the job and payroll loss will affect insurance carriers that specialize in writing workers compensation insurance for these industries.

Historically, technology’s disruption was limited to blue-collar workers; however, AI technology now has its sights set on white-collar workers, including insurance underwriters, claims executives and legal professionals. The insurance industry, which has not been easy to disrupt, is primed for transformations due to developments in AI and automation.

Two years ago, Cambridge University predicted that insurance underwriters were vulnerable to automation. Since that time, we have seen a greater demand among U.S. carriers to invest in new AI technologies that allow them to automate the underwriting and settlement of claims for small commercial insureds. Given the shortage of new talent available to fill expected insurance and claims executives retirements, coupled with new AI technologies, we expect this trend to accelerate.

See also: Measuring Success in Workers’ Comp  

Developments in AI and automation are already changing the U.S. legal profession, one of the most regulated and specialized professions in the U.S. -- McKinsey estimates that 22% of lawyers' and 35% of paralegal tasks can be automated today. A recent HBO documentary, “The Future of Work,” supports this prediction. It highlighted how LawGeex, a new AI-driven computer software, performed against skilled corporate lawyers on a common task -- analyzing complex legal documents. LawGeex proved its ability to review and interpret the documents, identify potential legal issues and provide substantive advice to a client in half the time -- and with much greater accuracy -- than the corporate lawyer.

While LawGeex and other AI technologies will not displace lawyers in the short term, it will exert pressure on lawyers to shift their time to more highly skilled work - such as negotiating and deal structuring - and away from research, writing and reviewing documents. The result could significantly change law firm practices and economics.

Have you considered how robots, AI and automation will change the workplaces of your insureds - and your own company? Stay tuned for my next blog, “Navigating the Fourth Industrial Revolution,” for ideas on how to navigate AI and developing technologies.