As the healthcare industry evolves, insurance payers are looking for innovative solutions to improve their claims process. One such solution gaining traction is the use of artificial intelligence (AI) technology. AI has the potential to revolutionize the claims process by reducing costs, increasing efficiency and improving accuracy.

The complexity of healthcare claims processing has been a mounting issue for provider revenue cycle management professionals for some time. Staffing shortages, training challenges and the need to keep up with payer policies and governmental regulations have created a perfect storm. This problem is only intensifying and will affect the payer side, possibly causing more unclean claims to be submitted and endless workstreams as claims are denied and resubmitted.

As insurance payers continue to face challenges from the provider side that affect claims processing, both parties can benefit from AI as a solution. Here’s how AI can help insurance payers in their healthcare claims process:

Faster Claims Processing

One of the most significant benefits is speeding up the entire process. With AI-powered automation tools in place, insurance payers can quickly sift through large amounts of data related to patient information and medical procedures provided by providers. This helps identify potential issues that could lead to rejection or denial early in the process.

By automating repetitive tasks such as data entry and pre-authorization checks with robotic process automation (RPA) and machine learning algorithms, insurers can increase productivity while freeing staff time for more complex tasks like handling appeals and resolving disputes.

Improved Accuracy

Another key benefit of using AI in healthcare claims processing is improved accuracy levels when identifying fraudulent activity or errors within submitted claims. By analyzing vast amounts of historical data on previously approved or denied claims, AI spots patterns and trends that can be used to flag suspicious activity before it becomes an issue.  

Using machine learning algorithms helps insurers detect fraud patterns much quicker than traditional methods would allow for, which translates into increased accuracy levels while reducing false positives.

Enhanced Cost Savings

Healthcare costs continue to rise each year due largely to inefficiencies within healthcare systems themselves -- this includes providers and payers alike.

AI technologies enable insurers to identify areas where cost savings can be made across their operations by automating manual processes such as claim adjudication or billing reconciliation, reducing administrative overheads while minimizing errors associated with human intervention.

Moreover, because AI-driven algorithms analyze large amounts of historical data on past claim submissions and payments made against similar services, they’re able to identify and predict future payouts based on existing trends. This leads to better decision-making and lower costs overall because unnecessary payouts aren't made.

See also: Can AI Solve Health Insurance Fraud?

Streamlined Workflow

Using advanced analytics capabilities from machine learning algorithms provides insurers with an opportunity to optimize internal workflows, resulting in greater efficiencies across all aspects of their business, especially those related to health system operations, such as underwriting policies or managing risk pools.

For example, predictive modeling techniques help identify high-risk patients, allowing insurers to develop tailored plans and reduce the likelihood of re-admission, saving money downstream and improving value-based care delivery models.

Ensuring Regulatory Compliance

Insurance payers need to comply with numerous laws and regulations set forth by local state governments and federal entities alike - including things like HIPAA laws, privacy rules and mandates from CMS. Failure to meet these requirements often results in fines, penalties and lost revenue streams, all of which hurt bottom-line performance figures and long-term viability in businesses involved in the healthcare space.

AI-powered solutions offer compliance monitoring tools that automatically flag any deviations from regulatory guidelines, enabling staff to take corrective action early and ensure continued adherence to best practices throughout the organization.

AI is certainly taking off not only in the healthcare industry but in many sectors and offers significant benefits for insurance payers and providers alike looking to streamline their processes and improve accuracy in the claims process. If you're an insurance payer looking find ways stay ahead of the curve, then adopting and promoting technologies like machine learning and AI should definitely be a top priority.

While Ralph Waldo Emerson’s words inspire travel and grace countless knick-knacks, many COOs may roll their eyes at his focus on the journey over the destination. After all, key performance indicators need to be met for continued growth, client satisfaction and even those coveted team bonuses. The destination cannot be tossed out the window like a banana peel.

As with any road trip, organizations cannot get to destinations without a solid foundation in place. And by setting realistic KPIs, organizations can still achieve change, with fewer potholes along the way.  

Is the vehicle safe for highway travel

Before jumping in a car for a long journey, a safety check is the prudent first step. In the business world, before any operation can set realistic KPIs that resonate with employees and customers, there are three safety checks that position it for success: 

• Ensure strategic alignment of the business strategy
• Have an inspirational and compelling vision
• Outline clear objectives

Making sure there is strategic alignment is critical to setting key performance indicators. The operation must have a tactical orientation to the business’ strategy; it shouldn’t be an independent plan. Everyone involved should know how they fit in and how the work they do enables the business’ larger strategy. If the team is working on anything that doesn’t align with the strategy, it needs to be reevaluated and challenged. 

Once there is a solid foundation, a critical need is an inspiring and compelling vision that is simple and resonates with the employee base. This vision serves as reason for getting in the car and driving to the destination. i.e.. the Grand Canyon. Employees will want to be on the journey when they’re interested in the destination. The destination/vision I’ve added to the GPS for my team is to be the customer’s first choice for life insurance by delivering innovative solutions.

The operation must have clear objectives that line up with and enable the overarching strategy. These should be measurable, actionable and outcome-based, serving as the “why” for the whole journey. 

Hitting the road

With those three elements laying a solid foundation for a sturdy, road-worthy vehicle, realistic and meaningful KPIs can now be formed. There are a few things to keep in mind:

• Ensure they are directly related to objectives and achieve larger business goals. If the KPIs don’t relate to the objectives set in your foundation, let alone the main goal of the business, employees will be spinning their wheels with as much luck as a Skylark stuck in Alabama mud. The workforce will get discouraged if they are working toward indicators that have no bearing on the vision at large.
• Focus on the top few. Prioritization is crucial to success; otherwise, the team will be spread too thin to have any real impact. By focusing on the top performance indicators, the team can devote the appropriate resources and time to making sure those are met. 
• Identify leading indicators. No different than a GPS re-routing you when there is an accident ahead, it is imperative for the operation to have leading indicators that will alert the team to a potential issue so the course can be corrected before a problem occurs. The leading indicators my team uses include sharp changes in volume, staffing or mix of business.   
• Know what is critical for your specific industry and function. When it comes to operations, regardless of industry, KPIs should include expenses, i.e. as a set percent of premium or unit costs such as cost per application, call, claim, policy, etc. or expense as a percentage of premium.  

See also: 3 Fatal Mistakes Risk Consultants Make

Road rules

When setting new performance indicators and driving significant change, there are three best practices that keep everyone safely moving toward the target: mindsets and behaviors, change management and rewards and recognitions.

Establishing mindsets and behaviors that are expected from the team grounds them in habits that drive a shift in culture. Things like collaboration, accountability, agility, innovation, process improvement and being data-driven are just a few examples of mindsets and behaviors to which I hold my team accountable. 

Cars don’t just swerve into a new lane without turn signals -- well, the good drivers don’t -- and a strong change management program acts as that trusty blinker. By leveraging a well-thought-out change management program, KPIs are met more easily and with less resistance. It's imperative to have a change plan mapped out that ensures the team is aware in advance of the change, understands the benefits to them, the business and customers, is given the right training and is rewarded for adopting the change. Having a clear map will also enable the team to bring stakeholders on the journey and manage any resistance.

No road trip is complete without some fountain sodas and snacks. Don’t forget to connect reward and recognition programs to the desired behaviors and outcomes. When a team member is exhibiting the actions driving expected outcomes, they should be rewarded. Rewards will reinforce the change and help the team achieve the KPIs, ultimately meeting the broader business goal. 

There’s a lot to be said for the journey, but having a clear destination in mind can help the journey fly by so that when someone asks, “Are we there yet?” the answer is "yes." With these tips in mind, don some sunglasses, get a full tank of gas and hit it.

The tornados that killed at least 26 people in Mississippi and Alabama and flattened towns in the Delta region over the weekend come amid dispiriting reports about the extent of natural catastrophes in 2022 and projections that they may even intensify in 2023. 

Swiss Re reported last week that economic damages from natural catastrophes totaled $284 billion globally in 2022 (with less than half of that insured). The total is far above the 10-year average of $220 billion. State Farm recently reported a $1 billion INCREASE in hail-related claims, to $3.5 billion in 2022. The number of claims climbed by 45,000 at State Farm last year. 

The La Nina weather phenomenon officially ended in early March, and an El Nino is expected to form throughout the summer, increasing temperatures and boosting the likelihood of the violent storms that just laid waste to parts of Mississippi. And unusually warm water in the Gulf of Mexico is expected to produce more tornados than normal this year, possibly many more than normal.

No rest for the weary....

What concerns me most, especially in the wake of the devastation in Mississippi and Alabama, is this Washington Post article about the potential for an especially bad tornado season. The article cautions that tornado forecasting is in its infancy but says meteorologists believe "there may be ties between [Gulf of Mexico] sea surface temperatures and the frequency and intensity of severe weather events in the U.S. Deep South in particular."

With water temperatures several degrees above normal, and even reaching into the high 70s, the article reports:

"The year has already been off to a historically active start, with a preliminary total of 168 tornadoes touching down across the Lower 48 states in January. That’s the second-most on record. In February, there were 55 tornadoes — double the average of 29. Oklahoma reported 17 tornadoes in January and February. The average during that window is one. On Jan. 16, two tornadoes spun up in eastern Iowa, the state’s first January tornadoes in 50 years. Alabama also logged 29 tornadoes during January, smashing state records."

As I wrote at the beginning of the year, the end of the unusually long La Nina could usher in the hottest year on record, adding energy to storms. That temperatures across the South and Southeast have been hovering 3 to 6 degrees above normal may well have contributed to the extraordinary violence of the recent tornados in Mississippi and Alabama. One tornado produced winds that reached 170mph and traveled nearly 60 miles -- less than 1% of tornados in the U.S. travel even 50 miles. After that tornado lifted off the ground, another formed, reaching winds of 155mph and traveling nearly 30 miles.

The one bit of good news is that the emergence of El Nino tends to mute the formation of hurricanes in the Atlantic, but it also usually promotes tropical storm activity in the Pacific, while increasing the risk of wildfires because of the higher temperatures. 

At a time when there is a lot of talk about how insurers can switch to a "predict and prevent" business model, I'd love to offer a way to help customers batten down the hatches and prepare for what looks like a season of unusually violent storms, but I'm not sure there's much to be done -- in the long run, sure, but not so much in the short run. All I can figure is that we as an industry need to prepare our organizations to help clients recover as quickly and painlessly as possible when the storms come.

Cheers,

Paul

The concept of embedded insurance is familiar, yet we leave value on the table when we consider it solely from a “point-of-sale” perspective. The opportunity we miss is to weave protection in at the “point-of-design,” an approach that has the potential to realize the vision of embedded as a $70 billion market for insurers by 2030 (Conning, 2023). In fact, embedded insurance may help insurers reduce the over $1 trillion insurance protection gap by making protection a part of products. 

Imagine a future in which protection solutions are woven into everyday products in ways that inspire the same surprise and delight as a car that detects who you are and adjusts mirrors and seats to your settings automatically, or a phone that fits perfectly into your palm as if it were made for it, or a music-streaming service that has learned the beats and melodies that you prefer and accurately recommends the tracks you’ll love. Protection services can be like that, too.

Form follows emotion: Weaving protection into product design

What if product, service and space designers incorporated unmet or under-addressed protection needs to weave insurance solutions into industrial design? 

Great design entails a practical understanding of how consumers are likely to interact with the product or service coupled with the broader context of the user’s experience. We can think about this as the consumer’s “comportment” toward a product or service. 

“Comportment” describes the relationship between the consumer and the product. It describes how consumers interact and engage with products and services and how they perceive it is intended to be used or consumed. Understanding this relationship is key to determining how to design products and services that will meet and exceed consumers' underlying needs. It is this insight that makes it possible for designers to create that “wow factor,” experienced when the form of a product or service intuitively “makes sense” and addresses an un/under-expressed need. 

Exceptional product designers observe and empathize, listen and read between the lines and match shape and flow with what may be un(der)expressed customer needs. They harness experience and emotion that serve as the basis for purchase decisions. What if insurance underwriters and product specialists were included in this process and, together with their design counterparts, explored means of weaving protection services into the design of everyday products and services?

What if insurance underwriters and product specialists played a role in the product/service design process and had access to the same observations and insights as the designer?

Realizing this vision begins with re-framing the concept of embedded insurance from a bolt-on model to imagining embeddedness applied at the point-of-design.

An analogy: Levi’s trucker jacket with Jacquard by Google 

In 2019, Google and Levi’s partnered to develop and launch a line of connected clothing. The Jacquard trucker jacket, interwoven with conductive fibers, is complemented by a sensor fob connected to a cellphone app that allows users to control some of their phone’s functionality through swiping the fabric. Users can manage calls and texts, query Google Assistant and control media through contact with the jacket. 

Image from Google/Jacquard.

Google and Levi’s were natural partners in this venture. Google brought the augmented fabric solution, sensor technology and the power of their technology brand while Levi’s brought clothing manufacturing capability, market access and their globally recognized clothing label. They collaborated to create a hybrid product that addressed both fashion and adjacent lifestyle needs in a way that enhanced the wearer’s experience.

Embedded insurance, developed from the point-of-design, is aligned in concept with the wearable technology solution that Google and Levi’s developed. We can amplify exceptional experiences by tailoring protection options during the design phase and making them part of the core product or service value proposition. To do so, we encourage our clients to become unfamiliar with insurance as traditionally conceived.

Working from first principles: Becoming unfamiliar with insurance

When we explore point-of-design development we are looking for a fundamental relationship between the protection service and the non-insurance product or service. We boil the concept of insurance down to its most fundamental and customer-centric element – the need for protection.

Insurance addresses the desire to avoid the pain of loss. Related to the decision bias of loss aversion, we accept a degree of loss now (i.e., premiums) when we voluntarily purchase insurance products to avoid a greater loss in the future that we perceive to be a plausible risk. Beginning with this most fundamental component, we can re-imagine ground-up what protection services may entail.

For example, when exploring designing an embedded insurance solution for a global watch manufacturer, we were faced with the task of weaving protection services into an extended warranty proposition for consumers for whom the financial impact associated with losing one or more of their collection was not meaningful. What was most important to them were the memories, events and relationships associated with the items. 

We addressed this challenge by collaborating with the manufacturer and identifying a series of protection service packages based on client segments that provided a means of capturing and recording irreplaceable memories and records of events, white-glove risk management services and advice, as well as specialized risk placement services beyond existing standard coverages. A first-principles approach and a re-framing of protection services made possible a complementary design process and an unexpectedly appealing outcome. 

We can take this further. As we begin to explore protection services embedded within everyday products, we can see the opportunity for all manner of products becoming wholly new direct distribution channels. Existing insurers may both have their branded and recognized products available directly or through intermediated distribution channels plus tailored protection solutions distributed through wholly new partners such as clothing manufacturers, OEMs and equipment manufacturers, not to mention service providers, software and experience firms and so on. 

Data and insights collected from embedded solutions may be shared with a consumer’s existing agent or broker to provide an even more personalized experience. Agents and brokers with permission to access consumer travel, fitness or related data may more effectively manage their individual relationships as well as their overall book of business. 

Plus, we anticipate the opportunity for large product manufacturers or retail platform providers to facilitate the development and launch of protection service marketplaces (in collaboration with underwriters and brokers), whereby individual risks may be collected, evaluated and bid upon by (re)insurance underwriters in exchange for commissions payable to the retail host. 

We recommend considering the following actions when exploring making point-of-design embedded insurance a key part of your growth strategy:

What to consider:

• What are your organizational objectives, and how will embedded insurance help realize these goals? 
  • Make certain that your organization has clearly defined and measurable objectives
  • Develop a sufficient understanding of the embedded insurance value proposition to determine whether this approach is right for your organization to realize these objectives
• How confident are you that you understand your target market?
  • How well does your organization understand the target market for this proposition – its segments, their needs, their purchasing behaviors and preferences that may inform which partners to engage? 
• Do you have a clear path to engage with partners and a capability to manage these relationships?
  • Once the strategic alignment and target markets are clear, is your organization positioned to identify and engage with partners who bring the other pieces of the puzzle (such as non-insurance product manufacture if yours is an Insurance firm)? How well do you feel you understand their industry and their objectives? Have you explored how you will package and pitch the opportunity to your prospective partners? 
• To what extent do you have the capability to inform industrial and protection services design?
  • Many organizations have mature design capabilities – to what extent can your organization manage the co-design of non-insurance products and protection services? How will this process be managed between the (at least) two firms? How will arrangements such as branding and marketing be managed, and how will challenges such as disputes be addressed?

What next?

Embedded insurance, if approached with an open mind and the patience to nurture business partnerships, has potential to grow existing business in wholly new ways and to reach more customers in a more intuitive manner than previously thought possible. Perhaps more importantly, it encourages us to entirely re-frame the way we think about insurance and to consider the possibility of protection services that can be woven into any product and service.

Insurance has been a pillar of society for centuries, providing individuals and businesses with the protection they need against life's uncertainties. 

Whether it was the law in King Hammurabi’s code that said a debtor didn’t have to pay back debts if a personal life tragedy made it impossible, or an insurer in today’s world helping homeowners repair their roofs after a natural disaster, insurance has enabled society to take risks and make progress.

Right now, though, insurance is at a critical juncture.

Some risks are becoming too risky for insurers to cover, leaving individuals and insurers unsure what to do in the face of ever-worsening climate catastrophes and cybersecurity threats. Claims inflation means occurrences that remain covered in insurance policies are getting pricier for insurers to cover, and the whole industry is being pushed toward a tipping point, where they must embrace digital ecosystem enablement or become obsolete.

This article explores how the insurance industry has found itself in this situation, the change in mindset and business models that insurers need to make this enablement happen and how it’ll help the sector to return to its original thesis of supporting society to progress and the potential profits that will be their reward.

The Insurance Industry at Peak Risk

Insurers today are struggling. 

One of the core drawbacks of insurance is that it’s incredibly admin- and data-intensive to orchestrate, and is burdensome to execute no matter how you look at it. 

The original use of IT to industrialize insurance businesses has certainly helped insurers scale, but those same technologies are now holding them back. While other industries have continued to evolve their platforms and passed through the “experience economy” tipping point toward the AI economy, insurers are struggling to build similar experiences for their customers.

Property and casualty insurers, in particular, have been left to compete on price, which forces their products into being treated like commodities, even though they’re so much more than that. Further, because most insurance IT setups act as digital walls between an insurer and their end customers, individuals no longer have personal relationships with insurers, so price shopping becomes the norm, perpetuating this cycle. 

When you pile that on top of the increasing costs of covering modern-day life and catastrophes, it’s no wonder insurers are having a hard time keeping themselves afloat. Inflation, claims inflation in particular, is making this problem exponentially worse. 

This has forced insurers to a tipping point, where they must rethink their business models and adopt a new mindset to remain competitive and profitable.

The Need for Ecosystem Enablement: in Mindset and Business Models

I recently went through a motor insurance claim involving a car repair, and the best way I could describe my customer experience was, “It’s all gone absolutely pear-shaped.” 

Getting the coverage I purchased felt like a struggle, and the digital administrative walls I couldn’t get past made me throw up my hands in frustration. Beyond a vanilla FNOL submission online, the process has involved zero status updates or communication. I often get random requests to engage from garages, who repeatedly ask for the same details submitted in the original claim. It’s been over a month with no resolution path in place -- but I am reliably told this is “normal.”

Insurers need to adapt to the digital age and embrace technological advancements to overcome these issues. They must move away from business-as-usual and adopt a new mindset that focuses on customer experience, digitalization and ecosystem thinking. 

What would it look like if they shifted their focus from simply selling insurance products to creating an end-to-end experience that meets the needs of their customers? 

With a modernized core system, leveraging technologies like AI, machine learning and the Internet of Things (IoT) not only becomes possible, but becomes the impetus to leapfrog the competition, changing insurance for good. 

Most new cars today are capable of self-reporting a claim and can even speak to the customer through the car speakers after an accident. They record the seconds before and after a collision, self-diagnose the repair requirements and so on. 

How Data Feeds Ecosystems to Make Insurance Operations More Efficient

AI and machine learning can automate administrative tasks, improve underwriting accuracy and streamline claims processing in ways manual, human labor just can’t. IoT can help insurers collect data on customer behavior, allowing them to personalize their offerings and provide more tailored coverage. Not to mention detecting and taking care of claims when issues first arise (like water damage) rather than unknowingly letting them evolve into something that’s 10x more costly because it takes longer for humans to notice problems than technology. 

And while early notice of loss is fantastic, an ecosystem setup helps the cost-savings of claims. When insurers can digitally integrate with other services like roadside assistance, repair shops and home contracting businesses, a seamless, done-for-you customer experience keeps things moving and saves a substantial amount of money on every single claim. 

When all the data is seamlessly shared, claims are updated in real time and no one is left wondering what’s happening or wasting precious time chasing down vendors to figure out what’s going on. There are even examples of insurance offerings with “zero claims” concepts. 

Telematics has as much value in “make me a greener driver” as it does “make me a safer driver.” Building data has as much value in “make my experience better” as it does in “remove fire risks.” It’s fair to say that this next era will need to involve a lot of thinking beyond insurance, with a lot of new value potential out there. 

Why Ecosystem Enablement Is a Must for Insurers

Embracing ecosystem enablement lets insurers return to their original thesis of supporting society to progress while also increasing their profitability. By focusing on customer experience, digitalization and ecosystem thinking, they can fully leverage the potential of partnerships and data to provide better coverage, reduce costs and increase their revenue streams.

The use of technology and data analytics is two-fold. One, it helps insurers better understand customer behavior, tailor their offerings to specific needs and improve the overall customer experience. Two, it streamlines the insurer’s operations and claims spending so everything is more efficient and affordable. 

But it can only happen once data is allowed to flow freely around the entire insurance system.

The insurance industry is at a crossroads, and ecosystem enablement is no longer a choice but an imperative. Insurers must adopt new business models that leverage technology, data analytics and ecosystems to better serve their customers, reduce risks and improve profitability. By doing so, the sector can return to its original thesis of supporting society and spur progress in how we all live our lives.

There was a time when many in insurtech believed that agents and brokers would become obsolete, that consumers would turn to buying insurance policies solely from providers. Insurance agents and brokers were often excluded from the new technologies, advancements and business models insurtechs were developing as a result. Beyond aggregator websites, innovation wasn’t widely being created with agents and brokers in mind. 

However, the industry has all too often overlooked the value and significance of independent insurance agents and brokers to consumers. While 29% of consumers prefer to interact with insurers online, 71% like human contact more, according to a study of the online consumer by Celent. Insurtechs have also neglected the important role agents and brokers play in driving business and revenue to insurance carriers. Many believed that technology could replace the independent agent and broker. But, it has not and likely never will. 

Today, savvy insurtechs recognize that agents and brokers are a dynamic part of the market ecosystem. They know that consumers have varied needs and preferences when buying insurance. Some consumers may prefer to purchase directly from providers online, while others choose the help, guidance and support that agents and brokers offer. These insurtechs also see the value of how agents and brokers can contribute to their bottom line. 

With insurance premiums increasing, a continued volatile economy and other challenges, consumers, agents and brokers are seeking options to save money and achieve their goals. They want help. Many agents and brokers also want to switch from captive to independent and increase their opportunity, success and reach. To succeed, they need to do business differently. That means today’s demand and expectation for fast, easy, tech-enabled online experiences can’t be ignored by agents and brokers. Consumers want the speed and efficiency of technology and internet access across all areas of their lives, even if they’re not tech-savvy. That includes buying insurance from agents and brokers. Customers will often turn elsewhere if innovation and ease aren’t part of the process. 

Agents and brokers can also benefit from new approaches and ideas to modernize their business and help it grow. Beyond the benefit of speed and demand for tech-enabled experiences, the use of advanced technology can help attract and retain talent by helping curb the high turnover that is often seen among agents and producers, which in turn can help maintain loyal clients, as well. 

Firefly has long used technology and the internet to help independent insurance agents and brokers connect with carriers without the burden of production requirements. It also helps agents and brokers learn, grow and succeed in their business by leveraging technology and the Internet. My company, BriteCo, recently launched a tech-driven agent and brokers program to empower these professionals to offer our jewelry insurance to their customers. The program helps agents and brokers better meet their client needs, while giving an opportunity to expand their line of business. Our strategic plan had always included opening this channel for the industry as a complement to our direct-to-consumer business. More insurtechs are likely to find ways to include agents and brokers in their models in the future. 

The proven value of independent agents and brokers doesn’t mean they don’t need to adapt and evolve. In fact, it is very much the opposite. Agents and brokers need to be on the bleeding edge of the industry and be able to meet the constant demand of speed and ease. They need to quote fast and sell fast, with quicker binding and more efficient claims management, as well as stay on top of future advancements and innovations. The era of lengthy timelines and traditional paperwork in insurance is gone. Today’s insurance agency leverages digital tools to deliver results and sell in minutes. At the same time, technology doesn’t change that consumers want a white glove experience from their insurance agents and brokers. Industry professionals need to remember that they are educators, who have to help their clients understand their offerings and make informed decisions. Services and processes should be heavily tech-enabled, but the human touch is still essential and will remain so.

For agents and brokers seeking to participate in this exciting era of insurtech, the key is to evaluate where they are currently, including the products they offer, what’s selling and what their needs are, along with where things can be improved. For example, if an agent or broker sells a lot of homeowners policies, they should look for where better products may exist and if there is anything new they should consider, and how they can make improvements. For insurtechs and carriers, it’s important to see that brokers and agents still have a powerful role to play in the industry and will continue to do so in the years to come.

As measuring and managing risk is fundamental to the insurance industry, strategic opportunities are arising for insurers because companies are increasingly working to assess and minimize their ESG risks.

For example, the proposed SEC climate rule requires public companies to disclose their ESG-related physical and transition risks. As this rule is put into effect, insurers will be relied upon to identify and alleviate risks, particularly those caused by climate, and other environmental and social factors.

In addition, as companies are committing to net-zero emissions and are encouraged to find new ways of operating, a significant amount of capital is being reallocated. Over the next several years, billions of dollars are projected to be spent on decarbonization technologies and renewable energy sources.

With the growing investment in these technologies and infrastructure, the demand for insurers to provide both standard coverage as well as adaptation and resilience support will continue to increase.

Also important for insurers to consider is the need to address their own ESG strategy. As stakeholders’ expectations continue to develop around ESG-related issues, it’s critical that insurers demonstrate they have an authentic and robust plan to drive long-term sustainability. Research has shown that taking ESG seriously strengthens relationships with business partners, helps to attract and retain employees and creates opportunities to connect with customers.

NAIC survey

Along with the proposed SEC climate rule, the National Association of Insurance Commissioners (NAIC) requires insurance companies that write more than $100 million in premiums and are located in any of the 14 participating states or the District of Columbia (representing almost 80% of the U.S. insurance market) to complete the annual Climate Risk Disclosure Survey.

Last year, the survey was revised to align with the international Task Force on Climate-Related Financial Disclosures (TCFD) framework. The TCFD standard is the international benchmark for climate risk disclosure and includes a nonconfidential disclosure of the insurers’ assessment and management of their climate-related risks. As a result of this change, the number of U.S. insurance companies preparing TCFD-compliant reports grew from 28 in 2021 to approximately 400 in 2022.

Tax credits and tax equity investing

An important part of an organization’s ESG strategy is understanding how to embed it within its other established objectives, such as its tax targets. If properly planned, tax credits can be used to fulfill sustainability initiatives while reducing tax liability.

Insurance companies have long used tax credits from investment in renewable energy, affordable housing or new markets to reduce their tax liability while allocating dollars to much-needed social and environmental initiatives. If designed properly, tax credits can also serve to satisfy ESG policy objectives.

Tax credit equity investing can also provide opportunities to corroborate ESG strategy with tax objectives. With tax credit equity investing, companies invest in specific projects, such as those noted above, in exchange for the right to claim the available tax credits. In this way, companies can quantify their ESG impact, satisfying ESG initiatives and making a measurable impact in their communities, while at the same time mitigating their tax liability.

Steps for developing a sound ESG strategy

Engage with your customers to understand their ESG expectations: Connect using surveys, roundtables and meetings to understand what’s most important to your clients and where to focus your initial efforts.

Participate in the development of industry standards: Collaborate with government leaders, regulatory agencies, insurers and other financial service firms.

Assess issues affecting your business: Assess the current state of your organization. What is your company doing well, and where could there be improvements? Identify ESG-related risks and opportunities specific to your operating structure and firm culture and examine the potential impact of various ESG strategies on the business.

“Despite the name artificial intelligence, there’s nothing artificial about it. As the industrial age amplified our arms and legs, the AI age is going to amplify our minds and our brains”. Manoj Saxena Executive Chairman of the Responsible AI Institute

This erudite quote encapsulates using AI to boost human insights and creativity. During the pandemic doctors placed expert decision making into AI datasets helping nurses deal with stress situationsⁱ. Businesses turn to AI for first mover advantage, competitive edge, future of work, mundane business process automation, advanced analytics and human augmentation. Challenges abound around ethics, privacy, liability, cybersecurity, bias, transparency and loss of jobs.  AI removes repetitive tasks so employees that learn will prosper as new jobs are created. Successful adoption centres around data integrity, verified trust and acting responsibly with exponential technology. Until recently AI had a relatively narrow focus looking at expert systems and performing singular tasks. Vendors competed for the best algorithmic approach with future promises of AI still in research labs and a perceived endgame in science fiction realms. Digitisation increased data volume, smart devices and systemic cyber risk bringing more AI technology out of the labs into production. Practitioners call this “third wave AI” and this paper looks at the implications of this paradigm shift on financial services moving from a pure algorithmic to a data driven approach as industries start to value data at the boardroom level. This innovation is recognised in global investment sectors where digitisation and AI lead the investment themes followed by renewable energy and cyber security both of which are becoming powered by AI. The stage is set. The following diagram show the AI development over a timeline.

The first AI wave took an expert system approach by gathering information from subject domain specialists and ingesting experience-based rules. The second AI wave was driven by machine learning techniques to counteract risks of climate change, cybersecurity, digitization outcomes and better modelling results. Models were trained to predict outcomes but did not address uncertainty, being based on probability theory, Monte Carlo simulationsⁱⁱ, and essentially backward looking. The relevance of AI to spreadsheets became clear, as millions of users with modeling solutions extract data from spreadsheets to run random simulations stochastically to give probability and correlation of risk across enterprises. The result is a combination of first and second wave AI into workable, but not fully dependable, statistical models that in general lack forward looking and predictive capabilities. Moving to the third wave, machine learning datasets build transparent, underlying explanatory models with perception of uncertainty, using real-world scenarios with real time data. This co-pilot approach augments human creativity with machine learning and has implications for all industries. The insurance industry sits in the cross hairs for augmentation at the underwriting stage to better identify risk to appropriately price premiums and at the claims stage, reducing fraud and improving loss ratios by enabling more competitive pricing, resulting in faster parametric claim payments for customers. AI helps stock exchanges by tracking markets with clarity and understanding in real time, without delay or distortion, bringing boards and investors closer together. Banks get a better approach to credit analysis. All financial services benefit from customer experience, efficiency and are also challenged by the same risk register.

The articulation of “Causal AI” has the means to change models to utilise causality over correlation, explaining decisions by cause and effect. Causality has evaded AI progress to date and is the advancement of contextual third wave machine learning by unlocking black boxes and explaining decisions to accountable third parties such as regulators, expert witness courts, forensics, lawyers and auditors. Causal AI needs to be embedded in models and spreadsheets to get transparency. The insurance industry has recognized causality for time immemorial but represented it mathematically by correlation, a statistical method measuring dependency of two variables. Rational counterfactual explanations require machine learning and underlying causal models taking a contextual premise beyond statistical associations.

Until recently the great academic AI debate has been about Artificial General Intelligence (AGI), where machines start thinking like humans and how this intelligence leads to training machines to create new tasks versus Artificial Super Intelligence (ASI), a consensus of AI exceeding human cognition by total automation. The crux of the debate gets back to regulation, ethics and responsible AI so likely the AGI proposition will hold true as although AI self-awareness will emerge in machines, humans will remain in the loop as expected.

Emerging popularity of Generative AI (GenAI) third wave tools, like Open AI ChatGPTⁱⁱⁱ, portend to expand creativity and eventually enhance predictive modelling. AI is an umbrella term and detractors are saying that GenAI tools are not fully AI but act like a stochastic parrot^iv as they are trained on volumes of data that are pre-generated and require continuous human input for mainstream adoption. The debates rage on, but announcements by Microsoft at WEF Davos^v to build GenAI into apps and dashboards is a significant accelerator. If these tools are embedded in power-point or spreadsheet cells, then transparency and causality are ingrained in extant everyday work tools. This opens up a new dimension in the cloud computing and AI search engine race which has nudged Google to create equivalents. GenAI impact will help finance boards make informed investment decisions. By training these tools on financial data, companies leverage machine learning capabilities, identifying trends and patterns invisible to humans. It will take time before data sharing challenges allow all this data to be ingested as many regulations surround the use of data sharing and privacy, so AI needs an established code of conduct manifesto.  

While ChatGPT may not replace the need for all human financial advice, at least in the near future, it may turbocharge service providers' ability to provide trusted advice and planning on the full range of financial services – ETBFSI.com^vi

AI changes the landscape of cybersecurity by detection speed and predictive response to emerging threats, improving security. Conversely, GenAI can create malware scripts by bad actors for malicious purposes posing a risk to cybersecurity posture.  Cyber risk needs to be mitigated at the design stage and cyber integrity maintained at all times as data used for training today will be the basis of machine learning in the future. This has to be done now, not added on, as discovery will be too late as AGI emerges and control of data could be lost.

Enterprise AI can be centralized (more risky), or decentralized with blockchain integration, revolving around data integrity (preferred). Transformation by machine learning is a boardroom issue. Complete enterprise AI solutions are emerging as low code no code, API^vii driven software to help companies leapfrog in AI whilst also addressing issues of reputational risk, solvency, brand awareness, operational resilience and data accuracy. AI regulation is a sensitive matter as algorithms shown to regulators reveal secret sauce IP. Technology itself is not regulated but instead the models, business processes and data flow that leads to customer outcomes, societal impact and bias assessment. The use of multi modal AI which holistically covers many media types (video, text and images), the reduction in social media deep-faking, embedding of AI software into hardware chips and use of synthetic data to train machines to avoid breaking privacy laws are enterprise developments that are evolving in the short term requiring corporate board governance, oversight and direction.    

The 4th Industrial Revolution^viii is realised with Web3, Metaverse and AI.  Blockchain  protects the data layer and must underpin all data accessed by AI. Internet, cloud computing, mobility and blockchain have taken years to be adopted but the embedding of AI is just taking months. This explosion of data cum intelligence will shorten the time for quantum computing, causing the post quantum^ix standards (for encryption continuity) to accelerate. The mainstream adoption of the metaverse in parallel integration of AI into avatars and NFT’s impacts evolution of decentralized finance^x (DeFi) and the future of cryptocurrency. All these trends were alluded to at the WEF 2023. Ultimately it is about ensuring each piece of data has situational awareness. This is not a short-term issue about trusting third wave machine learning systems but a long-term issue as outputs are created on which future systems will be trained, so data standards must be mandatory otherwise we risk living in an ocean of false information sources and misleading data. Now is the time to act.

Financial services adopting third wave explainable AI will achieve transparency by evidence-based data decision-making and de-risking AI adoption fears with human input at each stage of the value chain to give confidence that systems are operating correctly. This drives transformation from a business outcome perspective with governance to stop automating bias at scale. Trust is maintained with full transparent explanation, cyber integrity, compliance to privacy/AI accountability laws thus mitigating residual risk from implementation and avoiding fines or reputational risk. AI implementation will require a holistic approach by companies led from the top, avoiding compliance silos. Like cyber, AI integrity needs to be by design, to get operational assurance using AI maturity models^xi, NIST^xii standards and certifications.

What of AI trends and the future? Sensors are a large part of the industrial world with consumer devices increasing to 80 billion by 2025^xiii. The convergence of IOT and robotics change how humans interact in the real world. Autonomous transport, drones, smart agriculture equipment, surgical robots and 3-D-printed buildings will become pervasive, forcing open-source data sharing standards. Wearable device data can be ported directly to insurers, smart home and auto data made available through cloud computing so consumer device manufacturers can embed insurance at the OEM level. Public private partnerships will help create a common regulatory and cybersecurity framework around the AI paradigm.

“Artificial intelligence stirs our highest ambitions and deepest fears like few other technologies. It’s as if every gleaming and Promethean promise of machines able to perform tasks at speeds and with skills of which we can only dream carries with it a countervailing nightmare of human displacement and obsolescence.  But despite recent A.I. breakthroughs in previously human-dominated realms of language and visual art — the prose compositions of the GPT-3 language model and visual creations of the DALL-E 2 system have drawn intense interest — our gravest concerns should probably be tempered”.

This article was originally published by International Insurance Society.

REFERENCES

[i] https://analycat.com/artificial-intelligence/uncertain-accountability-in-medicine/

[ii] https://en.wikipedia.org/wiki/Monte_Carlo_method

[iii] https://openai.com/

[iv] https://the-decoder.com/stochastic-parrot-or-world-model-how-large-language-models-learn/ 

[v] https://www.weforum.org/events/world-economic-forum-annual-meeting-2023

[vi] https://bfsi.economictimes.indiatimes.com/news/financial-services/how-chatgpt-the-new-ai-wonder-may-transform-bfsi/97083280

[vii] https://www.dataversity.net/what-are-ai-apis-and-how-do-they-work/

[viii] https://en.wikipedia.org/wiki/Fourth_Industrial_Revolution

[ix] https://en.wikipedia.org/wiki/Post-quantum_cryptography

[x] https://www.investopedia.com/decentralized-finance-defi-5113835

[xi] https://www.bmc.com/blogs/ai-maturity-models/

[xii] https://www.nist.gov/

[xiii] https://www.vebuso.com/2018/02/idc-80-billion-connected-devices-2025-generating-180-trillion-gb-data-iot-opportunities/

About the Author:

David Piesse is CRO of Cymar. David has held numerous positions in a 40-year career including Global Insurance Lead for SUN Microsystems, Asia Pacific Chairman for Unirisx, United Nations Risk Management Consultant, Canadian government roles and staring career in Lloyds of London and associated market. David is an Asia Pacific specialist having lived in Asia 30 years with educational background at the British Computer Society and the Chartered Insurance Institute.

Sponsored by ITL Partner: International Insurance Society 

Data governance is relevant for every industry, but for several sectors, including financial services, deploying robust data protection and governance strategies is absolutely critical. The risks are higher, and it's not even about the data type; in finance, customer trust means everything.

When it comes to customer trust regarding data protection, the numbers are shocking. According to a recent McKinsey survey, no industry managed a trust rating of 50%.

The key lingering questions for the financial sector are:

  *   How do we protect all that data if we don't know what we need to protect?
  *   Where is the risk to business-critical data?
  *   How do we prevent data loss from inappropriate entitlements, permissions, sharing or unauthorized access?

Also critical for financial services organizations, in particular, is that they have not only their clients' data to secure, but their own intellectual property to protect. After all, their intellectual property is how they make money, and they will guard it with their lives.

For the financial industry, a company's IP is essentially its secret sauce. As such, they need an information walled garden around these critical pieces of data with the right sets of access policies and controls around it.

In this article, we'll explore a few specific use cases that underscore how crucial data protection and data governance are for the financial industry. Then, we'll explain some of the best strategies your organization can deploy for easy and effective data discovery, risk monitoring and remediation of business-critical data.

Three data governance use cases

When it comes to how data governance can turn into a nightmare for an organization, we have heard numerous stories from potential customers and current clients. Here are three that stand out, with the first two highlighting the importance of proper off-boarding procedures. The names have been changed to protect the innocent.

The CFO who still has access after leaving company

When an employee leaves an organization, far too often some of their access rights stay intact. When you think about it, this is not surprising given the sheer amount of confidential and private data financial organizations have to manage.

In one example, a CFO shared a significant amount of corporate data using her personal email address. One of the files happened to be a strategy document that was specifically created by the company's CEO.

What if that data fell into the wrong hands?

See also: Financial Well-Being: Everyone Wants It

Retired executive admin with access to the company's most confidential information

In another example, a company had an executive administrator who retired after many years with the organization. As an admin for many of the executives over the years, she had access to the most confidential data inside the entire company. She had access to many of the SharePoint sites within the corporate resources. Throughout her tenure, she shared many files with her personal account so she could work from home.

Even after retirement, she still had access to a vast number of files. Thankfully, in this case, the security team was able to vouch for her credibility, but the situation still impeded the company's security posture.

What if her personal account had been compromised?

IT staff with too much access to lending data

When you apply for a mortgage, you are providing perhaps the most personally identifiable information (PII) for any financial transaction in your lifetime. The mortgage industry in particular handles a lot of PII, including bank account numbers, statements, credit card numbers or statements, W-2s and much more.

In this example, a mortgage company had a mortgage application document full of client data that was somehow accessible by the IT staff. While loan officers and other parties with need-to-know access should be able to access customer applications, IT staff does not fall under that category.

In this case, the IT staff member noticed the overly permissive access and quickly initiated a sweeping change to their policies.

But what if the employee wasn't so honest?

Solving the data governance nightmare

How most companies approach data access

Too many organizations leave data governance to their end users. With this method, everyone in the company must ensure that the data they own has the correct entitlements, is shared appropriately and has the right sets of permissions (so they are accessible and accessed only by the right sets of personnel).

What are the chances of that working well?

Not good at all. Even if there are individuals who are motivated enough, most of the time they will not pay close attention. User errors on this front are the leading cause of data loss.

The proper approach to data governance

To help resolve the data security issues in the financial services industry, including in the above examples, you should seek out a data governance solution that can address three crucial steps.

Step 1: First, you'll need to identify exactly what type of data is accessible. Additionally, you should be able to scan all of your organization's data to provide a user-friendly type of dashboard that shows a proper risk chart.

Step 2: Second, your company should be able to ascertain the data type, who has access to it and whether it has the correct permissions and has been shared appropriately inside or outside the company. Discovery of the data type and risk profile are key here.

Step 3: And finally, more advanced solutions can even autonomously remediate the issues and programmatically remove any unauthorized access and prevent data loss.

Most data governance tools require companies to use regex parsing or pattern matching to discover sensitive data, placing a heavy burden on security teams to operationalize their data security programs. However, newer, best-of-breed solutions leverage AI models that have been built specifically to identify business critical data, classify and monitor risk, and remediate risk to sensitive data -- all without rules, regex or taxing end users. This allows enterprises to discover, monitor and protect their data without relying on large teams or burdening security teams with a lot of work to administer and derive value from their data security solutions. Even for financial organizations that still rely heavily on paper and pencil documents, these AI models use optical character recognition (OCR) to convert those scanned documents into words as if they are any other document.

In addition, modern solutions can also autonomously identify where business critical data may be at risk for financial organizations. Whether it's inappropriate sharing, wrong entitlements, unauthorized activity or wrong location, organizations are relieved of the burden of knowing what to look for or having to pre-define policies.

Modern AI-based data governance solutions allow financial firms to benefit from powerful deep learning technology that improves data access and activity governance by giving you an unparalleled contextual understanding of your structured and unstructured data, wherever it's stored. You'll be able to identify business-critical data, understand how it's used, identify any risks and mitigate them to prevent data loss and satisfy security and privacy mandates.

Ultimately, autonomous data governance is key to effective data security for financial services organizations.

Joining a group captive often results in a reduced insurance premium. This is due to the fact that, in a group captive, each member's premium is based on its own most recent five-year loss history. Group captives recruit safety-conscious companies with better-than-average loss experience.

This contrasts with commercial carriers, which base premium on a number of factors including industry-wide loss experience, statutory requirements and overall portfolio performance. This more expansive risk pool can result in higher premiums than a lower-risk company may obtain as a group captive member.

By the second and third year of membership, the increased focus on holistic risk management and post-loss claims management can drive members' premiums down even further. According to a recent study, almost three quarters of new bound policies in group captives resulted in lower premiums compared with members' previous plans. Many members enjoyed significant savings, with roughly 30% of new policies producing savings of 20% to 30% or more.

To read the full report from which this is excerpted, click here.