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Why Open Insurance Is the Future

In their quest to deliver customers the personalized experiences they crave, insurers have invested heavily in ramping up the digital capabilities needed to deliver tailored policies. But they may not be doing enough to break out of the outdated paradigms that have held back the customer experience.

To be sure, insurers have dramatically stepped up their innovation game since the arrival of the COVID-19 pandemic. In a recent survey of insurance CEOs conducted by KPMG, 85% said that the pandemic has accelerated their digitization initiatives, with 78% saying that the crisis has intensified their focus on crafting a “seamless digital customer experience.” 

The problem? With billions across the globe having spent the better part of the past year using digital technologies to work, learn and shop, the baseline expectations for what constitutes a quality digital experience have only been raised – making it challenging for many insurers to keep up.

That’s why more and more are turning to “open insurance” solutions, under which insurers leverage open APIs to share and access data and services with third parties – including insurtechs, financial institutions and organizations that possess useful data points that can help insurers more accurately gauge risk and develop personalized coverage. 

For legacy insurers facing mounting competition from digital-forward insurtechs, open insurance offers a promising pathway to shoring up their competitive posture over the long run. Here’s what insurers should know about the benefits of this model and how to go about pursuing it.

The Freedom and Flexibility to Evolve

Open APIs are hardly a novel concept. Open banking, for instance, increasingly uses the common practice to develop new apps and financial services through third-party applications, offering conventional banks the chance to work with fintechs rather than compete with them. 

What will open APIs mean for insurers? Here are just a few examples: By tapping into a rich variety of data, insurers can obtain a much more granular view of risk, paving the way to more accurate, tailored pricing for each individual policyholder. Armed with data-driven insights into their customers, insurers will be able to identify and pursue new revenue opportunities and product offerings, with open APIs facilitating a much faster time to market for new products and services. 

For instance, insurers can take individual software components and seamlessly incorporate them into their offerings, thereby shortening the customer journey, improving customer experience and even adjusting to offer to the customers they are targeting. Some components allow insurers to meet specific organizational needs and minimize the overhead associated with “inventing the wheel.” This provides insurers the freedom and flexibility they need not only to adapt, but to position themselves ahead of the curve.

See also: Insurance Leaders Use Digital for…

How to Get Started

While insurers have long been hesitant to share data, embracing the open insurance model will require them to shed that reluctance and operate with a partnership mindset. In this digitized climate, success demands forging partnerships with relevant organizations both within and beyond the traditional insurance industry.  By combining their offerings with those of business partners outside the insurance industry, insurers can generate new value while achieving stronger and more diverse coverage. 

Incorporating open APIs allows access to insurtech products and technological capabilities, as well as more granular customer information, while still adhering to relevant privacy laws and regulations. Simply put, these APIs make it possible for insurers to significantly increase their technological prowess without having to start from scratch or make massive investments in recruitment or R&D.

Cloud computing serves as a vital enabler for this model of collaboration, allowing for quick deployment, rapid scale and easy access to third-party data and resources that insurers can leverage to develop new offerings. 

Insurers don’t have to face this transition alone. Technology partners can provide insurers with the know-how and capabilities they need to extract the most value from open APIs, roll out new offerings and dynamically evolve in response to changing market conditions and customer expectations.

Preparing for the Future

As the COVID-19 pandemic proves, disruption isn’t always predictable. And in a world that often seems to be moving faster than ever, more seismic shifts may be on the way. 

But while not everything is foreseeable, this much is: Insurers’ ability to survive and thrive in the ‘next normal’ will hinge on their ability to deploy innovative models of open collaboration. As insurers plot their digital strategies, they can’t afford to ignore the promise of open insurance.

It’s Time for Next Phase of Innovation

The insurance industry has slowly embraced digital and mobile technology (strong emphasis on slowly) over the past 10 years. It’s time to break through the first phase of technology adoption and move into a new phase of tech-enabled innovation.

The early phase of technology adoption usually sees incumbent businesses apply technology to existing products and modes of thinking. So, with the first wave of digital adoption in health insurance, we saw plans launch member portals and mobile apps where users could view their explanation of benefits and chat with customer service representatives online. However, nothing about the underlying insurance plan changed.

When it comes to plan design, insurance companies too often think within existing platform limitations. They most often ask: “What can we design that will work on our existing platform?” Then they design the plan and move on to implementation. This is invariably the sequence when developing insurance products and bringing them to market.

The process makes sense at first, because the development of new platforms takes time and costs a lot of money. But these platform limitations are holding us back from designing new tech-enabled insurance products that can truly change the market and better serve customers.

Tech-Enabled Insurance Innovation Will Guide the Future

Starting from scratch is a startup’s advantage, which incumbent insurance companies don’t have.

Oscar Health Insurance is a great example of a startup making heavy investments in new technology and innovative plan designs to create a better member experience. Its telemedicine service and digital provider directories aim to improve access to free or low-cost care for members with high deductibles.

Major medical health insurance is far more regulated in its plan designs than other forms of insurance. If you could start another ancillary insurance product from scratch, however, what kind of plans would you design? If you follow the old way of thinking, you will design the plan first and then think about how to platform it next.

Granted, there are some advantages to this approach. You can create a better version of existing products, like, for example, critical illness insurance policies. Another advantage to tweaking a known insurance product is that you can often rent an existing technology platform to get to market quickly. But if you do that, you risk designing a product that’s not differentiated enough from existing solutions to entice employers and brokers to switch. You also miss an opportunity to create a better solution for your members that actually improves their health.

When we started Brella, for example, we knew we wanted to make a real difference in the health and financial wellness of our members. We saw that as the only reason to go through the trouble of developing a new insurance product — but we needed to build everything from scratch.

See also: Crisis Invigorates Insurance Innovation

The next phase of insurance product innovation is tech-enabled thinking. When you design a new insurance product, think of it in the context of its tech platform. That means you must have both insurance experts and technologists at the table. This is really powerful; it is what enabled us to come up with the concept of a supplemental health insurance plan that pays benefits on diagnosis.

An example of this is Beam Dental’s dental coverage insurance, which uses a connected toothbrush to reward groups with good brushing habits with rates better aligned to their lower-risk profile. Eden Health is also making strides with its primary care solutions that weave together healthcare navigation services with direct primary care. Its tech platform was nimble enough to quickly help employer customers with COVID-19 screening and testing to keep their teams healthy and working through the coronavirus pandemic.

At Brella, we learned from our customer research that out-of-pocket costs caused by rising health plan cost-sharing are a major pain point for families. Existing supplemental plans were not designed to cover that gap. In addition, those plans were rarely used, and, when they were, customers were far from satisfied with their claims experience and the complex rules associated with the plans.

As we thought about building a supplemental insurance plan to address this need, we asked ourselves: “What does a plan design that pays benefits on diagnosis allow technology to do?” and “What does technology allow us to do with the plan design?”

Our tech-enabled plan design lets us pay benefits sooner because the diagnosis happens early in the care journey. This approach also dramatically simplifies the claims and adjudications processes, so it’s easy for customers to file claims in minutes online and through our mobile app. What’s more, it opens opportunities for automation that didn’t exist in past supplemental insurance plans — which were stuck in the first phase of technology adoption.

These are the kinds of differentiated benefits that help customers and their families. That kind of value is worth the price of change for employers to embrace innovative insurance solutions.

Essentially, tech-enabled insurance plan design asks: “What can you do with the plan that unlocks new technical capabilities?” and “What can you do with technology that makes new plan features possible?”

In the next phase of insurtech innovation, more partnerships between insurance experts and technologists are necessary. We need more tables surrounded by actuaries and engineers to create solutions that will realize the world we all want — one where health hardships don’t become financial burdens.

Intersection of AI and Cyber Insurance

Exhibitioners at the Century of Progress International Exposition held in Chicago from 1933-1934 touted washing machines and air conditioners as capable of bringing vast changes to our everyday lives. This optimism for future generations is inherent within the human psyche. As such, we often speak of artificial intelligence (“AI”) as a lofty, almost dream-like reality that awaits us in the not-so-distant future. 

But AI proliferates today and extends beyond the entertainment-based efficiencies embedded within Netflix and TikTok that we read about; attorneys apply AI to document review projects; vehicle manufacturers use AI to control a vehicle’s acceleration, speed and steering; hospitals and doctors are using AI to triage and diagnose patients; and biotech companies increasingly rely on AI to model the potential success of newly developed therapies and vaccines. 

Insurance carriers remain optimistic about the efficiencies to be gained by implementing AI-based applications into their workflows. The same is true for cyber insurance carriers, who over the last eight to 10 years entered the market to meet the needs of customers who seek protection from potential financial and operational ruin due to the rise of ransomware and other malicious activity perpetrated by cyber criminals. And, while AI is sure to benefit society when wielded properly, cyber carriers remain conscious that AI’s proliferation is a double-edged sword. Thus, cyber insurance will have an even greater role to play in an AI-dominated world.

The reasons are twofold:

First, harm from cyber attacks will be more widespread because of the threat posed by more sophisticated AI-based attacks. By using an AI-based attack, malicious actors will be able to operate in ways that are both highly efficient and highly scalable. For example, rather than disguising malware as an email attachment in a message from “your boss,” or hawking magic pills, a sophisticated AI-based attack may be capable of personalizing, instantaneously, the malicious email (or other vehicle) received by each target victim. 

Second, increasingly intelligent cyber attacks are likely to bring greater cost and consequences. Cyber-attacks today inflict financial harm and disrupt the productivity of the victim but generally do not alter people’s livelihood or society at large. We will see that blast radius grow exponentially in the future when malicious actors deploy cyber attacks against those AI-based systems that society increasingly relies on for day-to-day operations.

Look at the recent attack on the Colonial Pipeline and what it’s done to gasoline prices in the eastern U.S. Citizens’ freedom of movement may be jeopardized when a future cyber attack against a vehicle manufacturer not only disrupts assembly line production but also paralyzes entire fleets of autonomous vehicles operating on the vehicle manufacturer’s software. Or, in a more dire situation, if there is malicious disruption of the AI-based systems at the core of a vehicle’s control system. Disrupted AI-based hiring systems could also result in significantly slower access to available low-wage jobs. And patients may suffer or die when a hospital loses its ability to intelligently triage and provide treatment. In sum, the outcomes from a cyber attack could be devastating.

See also: Surging Costs of Cyber Claims

But the future is not entirely bleak. Cybersecurity firms and professionals continue to improve on threat detection and elimination tools by harnessing AI. These types of tools and software are capable of intelligently digesting data points gathered from both past and current attacks across a massive scale. Decreasing response time via the real-time adjustment of threat detection applications is among the myriad ways AI is changing the cybersecurity landscape. 

The adoption of AI by the insurance industry is also bringing about a paradigm shift. The most prominent example is Lemonade, a property and casualty insurer that makes decisions about policy underwriting and claims processing based entirely on AI. Lemonade went public via IPO in summer 2020; it raised $319 million in a single day. Opportunities for innovation abound.

As society absorbs AI into the framework of industry and people’s lives it should expect to reap enormous benefits but also protect those benefits by preparing for and managing attendant risks.

The Broad Reality of Diversity

Our industry is diversifying, and that diversity has been a force for growth and innovation. Decades of work and progress have yielded more women in leadership roles, a more diverse workforce and a greater emphasis on recruiting talent from varied backgrounds and geographies. Though calls for diversity are not new, organizations are taking the movement more seriously now than ever before, and it is having a positive impact in the operation and growth of businesses.

According to a 2018 study from the Boston Consulting Group, companies that have higher than average diversity in their workforce had 19% higher revenue from enhanced or new products than less diverse organizations. 

As the insurance industry continues to evolve, it will be important to continue to support D&I programs and the opportunities they create for businesses and the workforce. These efforts should also include a willingness to consider recruiting candidates from a diverse array of experiences, trainings, socio-economic backgrounds and more. 

A Personal Journey

In my previous life in television production, working for shows like “One Tree Hill” and “Friday Night Lights,” I learned many of the skills that eventually led to my career in business development, and later as president and chief operating officer with AmRisc.

A degree in broadcast journalism and an early career in TV and film production are definitely rare qualifications for someone in insurance leadership. That said, for me, that background manifested the skills and mindset I needed to operate in a fast-paced environment and think creatively to support business growth. 

How did I find my way into the insurance industry? I was looking to change my career, and my resume serendipitously found its way into the hands of former AmRisc CEO Dan Peed. They were looking for someone who could come in and roll with whatever was thrown at them. That happens to be my strong suit. I was immediately energized and motivated by the work as I grasped more than insurance concepts. I began to understand the legal, accounting, managerial, compliance and operational aspects of the business, as well. 

See also: Diversity and Respect: Best Insurance Policy

My time in television production allowed me to explore my passion for problem-solving and crafting stories. That experience served me well in insurance, where I’ve had an even greater opportunity to flex those same creative and problem-solving muscles. At the same time, as someone who came to insurance from a very different space, I was able to offer a unique perspective the company didn’t have available previously.

A Creative Edge

With our company being founded by engineers – also from non-traditional backgrounds for insurance – it’s fitting that one of our core values is innovation. Imagine the innovation that could come from a team of employees with a variety of diverse backgrounds, cultures, experiences and beliefs. Then, add to that the new perspectives and fresh thinking we get from those with different socio-economic backgrounds. These may be individuals whose talents have yet to be discovered as they leave high school or college and enter the workforce. A team like that could have the versatility to not only adapt to unique problems; they would have a plethora of experiences and perceptions to offer the business and contribute uniquely to the growth of their team and organization.

At AmRisc, we’ve started to harness the knowledge and experience of this talent. We began to recruit others outside of insurance, like me, over a decade ago. For example, we have a number of former schoolteachers who have joined the team and have been remarkably successful translating the detail-oriented skillsets they honed in the classroom to our underwriting and analyst teams.  We’ve also recently launched an Innovation Hub, our own version of Shark Tank, to encourage people from all parts of the organization to be seen and heard.

This is just one step. Engaging a range of professionals with varying work experience, cultures, religions and more allows you to build a well-rounded team that can take a company to the next level. Employees who are comfortable in their own skin at work, happy, driven and inspired – who also reflect your consumer base – will thrive, fuel productivity and drive growth.  

Building for the Future

Many in the industry have developed novel programs to foster D&I within their walls. Industry leaders recognize there is tremendous value in empowering and investing in people. At AmRisc, we’ve deployed a Corporate Citizenship Program and DEI Council with our parent company, Truist. We’ve also hosted an incredibly popular Day of Understanding for employees to voice their opinions and concerns and implemented unconscious bias training in an effort to enhance our corporate culture. We even revised our logo to a display of color that demonstrates our commitment to a diversified team and one that values involvement from employees of all genders, races, religions or backgrounds.

As a company and as an industry, we also find opportunities to develop and mold our D&I initiatives through unifying organizations like the Insurance Industry Charitable Foundation. IICF’s International Inclusion in Insurance Forum presents a wonderful opportunity for the industry to get together, hear from the top minds in D&I and receive actionable, fresh approaches to incorporate into our own workplaces.

Recruiting for Tomorrow

Anyone who has worked in insurance knows the historic difficulties surrounding the recruitment of new, aspiring talent. The COVID-19 pandemic has only exacerbated this problem, but the lasting impacts of remote work may prove to be the push our industry needs to recruit geographically diverse talent from different socio-economic and experience backgrounds.

Thus, many industry leaders are taking a closer look at our hiring methodologies and prerequisites. At AmRisc, we are endeavoring to do more. We’re partnering with major universities to recruit talented, diverse candidates with specializations in risk management and beyond. We are looking to secure talent who might not have been considered for a career in insurance due to work history or educational background but who have the drive and energy to help us grow. We’re also exploring flexible work arrangements so those with family, eldercare or other personal responsibilities can better manage their work-life balance.

See also: State of Diversity, Inclusion in Insurance

Right now is the perfect time to consider recruiting with a focus on diverse experiences in addition to unique backgrounds and cultures. As people return to the workforce, a range of candidates with the potential to revolutionize our industry and become the next generation of insurance professionals may present themselves. We have to remain open to recognizing them and embracing the uniqueness they bring to our organizations.

Our industry is built on resilience and serving others when they are most in need. As employers, we must ensure we consider the whole candidate or employee and find ways to encourage them to bring their diverse skills and talents to the office for the good of the company, the industry and society as a whole.

Achieving a ‘Logical Data Fabric’

Time-consuming deals or claims-related interactions with agents are getting replaced by self-service insurance portals and sometimes even by bots. The growth of IoT, artificial intelligence (AI) and machine learning (ML) technology and the prevalence of sensors in wearables, cars, houses, agriculture, transportation and other areas are making risk profiling and precautionary measures much better and faster.

However, the sharing economy brought about by Uber, Airbnb, etc. is making insurance tricky. The pandemic is also forcing insurance companies to evolve to survive the current climate and prepare for an uncertain future. 

For many, part of this development has been adopting new technologies and digitizing services.

Insurance companies must rely heavily on their data to embrace these new trends. Unfortunately, many depend on older enterprise data architectures composed of legacy tools and methodologies. Business stakeholders need immediate information for real-time decisions, but this is just not possible when data is scattered across multiple data sources. Relying on rigid technologies such as ETL (extract, transform, load), makes it almost impossible for insurers to make real-time decisions on a claim or engage in predictive analytics with the most current data to underwrite the right insurance product for the right client.

These legacy technologies are resource-intensive, time-consuming and costly. ETL processes deliver data in scheduled batches, meaning there is always lag, which forces business users to wait for the data to be delivered. Depending on the configuration and schedule, batches can be delivered very quickly but never on an instantaneous, as-needed basis. In fact, many ETL processes are still done overnight. 

This leaves insurers with no choice but to initiate complex, expensive and time-consuming engagements with IT just to answer basic questions. On top of that, M&A and other forms of corporate restructuring are constant in the insurance industry, and legacy data architecture poses a huge threat to post-merger data architecture consolidation. As in other industries, cloud adoption and data lake implementation are becoming more prevalent, yet these cloud-first initiatives, application modernization projects and big data analytics are either fraught with downtime, implementation challenges or, in the best case scenarios, only partially successful. 

Data Fabric to Logical Data Fabric: The Modern Way to Keeping Businesses Covered

With volume, variety and velocity of data today, users need a unified view of all the data available to them in near real-time. Insurers are looking to capture the ever-changing data from streaming, data lakes and other newer data sources or data repositories and take advantage of all the data types available.

Technologists have attempted to meet the needs of their organizations in many ways. First, they used larger and larger databases. Then they set up data warehouses. Most recently, they have turned to data lakes, cloud repositories and big data implementations. Unfortunately, these latest solutions have only compounded the problem, as different sources of data are still stored in functional silos, separate from other sources of data. Even data lakes continue to contain multiple data silos, which many business users and analysts only realize when they attempt to run a single query across the entire data lake. 

To manage the complexity of today’s environments, companies are adopting newer architectural approaches such as data fabric to augment and automate data management. This modern data management approach streamlines data discovery, access and governance by automating much of the labor that would normally be performed at multiple individual junctures using older methods. Forrester analyst Noel Yuhanna defined enterprise data fabric as a set of processes that automate “integration, transformation, preparation, curation, security, governance and orchestration” of data, which are some of the most traditionally labor-intensive aspects of business intelligence, due to the highly diverse, heterogeneous nature of today’s data landscape.  

Most recently, research firms began to evolve their notion of data fabric to that of a “logical data fabric.” Analysts devised this concept based on the idea that even if technology vendors were to automate key aspects of the data pipeline – or turn these processes into services – the resulting data fabric will eventually be limited by certain physical realities; namely, the need to replicate data. To ensure the business continues to gain significant efficiencies, organizations need to change the paradigm from a physical data fabric to a logical data fabric. TDWI analyst David Stodder outlined some of the features of logical data fabric, saying that it had the capacity to “knit together disparate data sources in their broad, hybrid universe of data platforms.” 

Why Data Virtualization Is the Key to Stitching Together a Logical Data Fabric

Data virtualization (DV) is a data integration solution, but one that uses a completely different approach than most methods, making it a perfect fit for logical data fabric application. The technology is an approach to data management that allows an application to retrieve and manipulate data without requiring technical details about the data, such as how it is formatted at source, or where it is physically located. Rather than physically moving the data to a new, consolidated location via an ETL process, data virtualization provides a real-time view of the consolidated data, leaving the source data exactly where it is and containing the necessary metadata for accessing the various sources, making it straightforward to implement. 

Performing many of the same transformation and data-quality-control functions as traditional data integration solutions, DV differs because it can also provide real-time data integration at a lower cost. As a result, it can either replace traditional data integration processes and their associated data marts and data warehouses, or simply augment them, by extending their capabilities. Sophisticated data virtualization solutions go one step further by establishing an enterprise data-access layer that provides universal access to all of an organization’s critical data. When insurers need to obtain data, they query the data virtualization layer, which, in turn, gets the data from the applicable data sources. Because the data virtualization layer takes care of the data-access component, it abstracts business users from complexities such as where the data is stored or what format it is in. Depending on how a data virtualization layer is implemented, business users can ask questions and receive answers easily, because the underlying data virtualization layer handles all the complexity. 

See also: Stop Being Scared of Artificial Intelligence

Additionally, modern DV solutions offer dynamic data catalogs that not only list all of an organization’s available data sources but provide access to the data from right there in the catalog. They also leverage their unified metadata capabilities to enable stakeholders to implement data quality, data governance and security protocols across an organization’s disparate data sources, from a single point of control. That is particularly important for insurance companies, which are expected to understand and protect their customers personally identified information (PII), such as credit cards, healthcare and Social Security numbers, credit scores and banking information. That helps organizations comply with regulations such as GDPR, CCPA and U.K. Data Protection Act, to name a few. Finally, some of the best DV solutions offer premium features such as query acceleration through aggregation awareness, AI/ML driven dataset recommendation and auto-scaling architecture in the cloud. 

Supported by data virtualization, a logical data fabric can enable a wide range of benefits, including: 

  1. Real-time data integration across disparate systems. Logical data fabric enables real-time access to data across vastly different kinds of sources, including cloud and on-premises systems; streaming and historical data systems; legacy and modern systems; structured, semi-structured and completely unstructured sources; and cloud systems provided by different vendors. It can handle flat files, social media feeds, IoT data and more. 
  2. Enterprise data catalogs. Logical data fabric enables comprehensive data catalogs across the entire enterprise and provides seamless access to the data itself. Business users can use the catalog to understand what data is available and any conditions under which it can be used. They can also capture the full lineage of any dataset as well as all applicable associations.   
  3. Seamless governance and security. Because data in a logical data fabric is accessed through a unified data access layer, organizations can easily control who is allowed to view or edit which data.    
  4. Powerful AI/ML capabilities. With a unified, logical framework, a logical data fabric enables AI/ML capabilities at a variety of different points within the offered solution, including query optimization, data delivery and automated recommendations within the data catalog.   
  5. Simplified maintenance. Logical data fabric protects users and administrators from the complexities of accessing each individual source and operates with each data source “as it is.” Unlike ETL scripts — which need to be re-written, re-tested and re-deployed whenever a source is removed or changed — logical data fabric accommodates these changes, greatly simplifying the overall administrative burden. 

The data landscape is only going to get more complex for insurers, and business users will only want broader, faster access to all of the data available without unnecessary risk or liability. Implementing a logical data fabric built on data virtualization has proven to deliver information immediately to meet business demands and does so without the cost of a monolithic hardware upgrade. More importantly, it promises to reduce the total costs of the data infrastructure by leaving the source data exactly where it is. Put simply, the logical data fabric can act as an indemnity to data management and to leveraging your precious data assets.