“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.

Third-party administrators (TPAs) can offer a cost-effective means to assist in claims management, particularly for types that fall outside an insurer’s standard capabilities. That said, the relationship between an insurance carrier and a TPA can be complex and multifaceted, as both companies may have varying incentives and measure of success. As such, a foundation of trust is integral to a successful partnership. Open communication, transparency and regular reviews for validation are a must for effective collaboration.

At QBE, we are fortunate to work with some of the best TPAs in the business. Those relationships work well largely because we are consistently mindful of what’s working and what’s not, and we’re always looking for ways to collaborate more effectively in a spirit of continuous improvement.

Carriers and TPAs alike can benefit from increased visibility of the metrics that shed light on claims management performance. It’s never a hands-off relationship, of course, nor should it be. Effective oversight is essential, but accomplishing that efficiently and at scale can be a challenge. For both carriers and TPAs, oversight calls for new ways of systematically monitoring, evaluating and improving collaboration.

Technology offers a path toward achieving those ends, and there are now means to manage information and automate workflows at scale, but until recently, those capabilities could only be applied to highly structured information such as customer records, invoices and ledger entries.

Today, artificial intelligence (AI) and advanced analytics are making it possible to dig deep into narrative content, parse natural language, interpret its meaning and draw conclusions that can assist human actors in performing their jobs more effectively. AI is especially adept at discovering correlation and predicting outcomes based on multivariate data. This combination of natural language processing (NLP) and predictive analytics offers game-changing capabilities with respect to claims management.

When it comes to managing TPA relationships, in particular, AI provides some powerful advantages:

• Increasing the timeliness and accuracy of reserves. Case reserving is both art and science, relying on available information and a requisite amount of experience and expertise to predict an outcome. Historically, reserve accuracy has been a function of the individuals involved and their ability to truly understand what was driving case exposures. Naturally, the more complex the case, the more time is spent on the analysis and the more volatility in reserve vs. actual case outcome. It’s also more work for everyone involved, so carriers and TPAs alike would prefer to improve both reserve timeliness and accuracy without adding extra effort to the process. Using predictive analytics that incorporate a range of input variables, AI helps to identify potential mismatches in claims reserves using automation. Carriers can use this information to supplement human judgment, validating their TPAs’ reserve calculations and identifying any anomalies that may require further investigation.
• Uncovering hidden risks. Given the increase in litigation and the growth of mega-claims in recent years, insurers have been looking to sharpen their analytical capabilities and improve their capacity to identify high-risk cases. AI can discover and quantify correlations that could suggest expensive litigation, lifelong care or complications that may otherwise fly under the radar. For example, insurtech companies like CLARA Analytics are providing these kinds of insights for workers’ comp carriers, using very large data sets that span multiple geographies, providers, attorneys and categories of injury. Their AI algorithms can ingest an enormous array of information and zero in on the factors that indicate risk — even when those clues may not be apparent to the human eye. By calling attention to details that aren’t necessarily obvious, AI provides claims professionals with a powerful tool to identify hidden surprises and take action before they escalate. This informs the deployment of additional resources and aids in the ability to tailor case action plans to address the risks identified with AI assistance.
• Helping ensure optimal case assignment: “right claim, right adjuster.” Using a severity model approach, AI can assist with directing cases with higher potential exposures to where they need to go as early as possible, such as a senior level adjuster, and vice-versa. The ability to more accurately and consistently assign claims can have a significant impact on the cost of TPA services by ensuring a given program is staffed with the right mix of experience.

See also: How AI Can Help Insurers on Climate

These are a few instances of how working with TPAs, particularly through the use of AI, can be valuable to carriers. That said, it can’t be overstated how essential a strong working relationship centered on effective collaboration is to a successful partnership. At QBE, we’ve seen the benefits resulting from strategic, thoughtfully managed TPA relationships. Even if you’re not routinely working with third-party administrators, though, today’s technology offers all sorts of distinct advantages for companies that are willing to invest in the opportunity. When done right, better results can be achieved, ultimately delivering improved services to customers when they need it most.

As first published in PropertyCasualty360.

Innovation doesn’t need a pitch deck. Organizations have been talking about the need to be innovative since the end of WWII, where technological innovation was seen as the catalyst for economic growth.

Every executive has been told to be “more innovative” or to foster an “innovative environment.” We understand why, but do we know if our organization is being truly innovative?

Supreme Court Justice Potter Stewart is famous for uttering, “I know it when I see it,” when asked how he would know if something was pornographic. But executives cannot wait until they see innovation. They need tangible signs that innovation is actually happening under their roofs.

The best place to look for innovation is in the quality of decisions being made. Everything an organization does can be broken down into decisions, which are the building blocks of innovation, like Legos. 

Innovative decisions take into account factors besides growth, profit and costs. They reflect an organization that is willing to invest in a future that is unclear, planting seeds in a garden you will never see, as sung in the "Hamilton" musical.

Here are three areas within your organization where you can look for innovative decisions.

Where Are Your Ideas Coming From?

Some organizations haven’t been able to shake off their military-style leadership, where all decisions are made at the top and then trickle to the frontline workers. As you can imagine, the top-down approach doesn’t leave much room for innovation unless your CEO is Jack Welch or Steve Jobs. Even then, you would be leaving many good ideas on the table.

An innovative organization has a decision-making process that encourages ideas from anyone. Google is known for allowing employees to spend 20% of their time on side projects. Most don’t go anywhere, but a few become homeruns like Gmail.

The 3M Sticky Notes were a failure. The team was looking for strong adhesives and stumbled on an adhesive that somewhat worked. Someone recognized the value of it and made it into a product.

TV shows like "Undercover Boss" remind us that frontline workers may have some of the best ideas. After all, they are the ones who spend the majority of their time with customers. A doorman at a hotel has a better sense of what is working than an executive who might not even live nearby. 

Encouraging ideas from anywhere in the organization doesn’t mean that they have to be implemented. All ideas should be vetted through an objective framework. There are plenty of options, but I personally like the 3 O's, which stands for Outcomes, Options and Obstacles. 

An innovative organization (or even a team) can survey the company for ideas, which are then evaluated based on criteria such as strategic alignment, potential, risk and any other factors relevant to the specific decision.  

If all your ideas are coming from the same people, your decisions will always have the same outcomes. 

See also: A Look Ahead for Insurtechs in 2023

The Magic of Decision Speed

The speed at which decisions are made matters greatly. Markets are moving more rapidly today than in the past. We could debate the reasons—social media, connectivity and so forth—but what matters is that decisions need be made quicker.

Perfect decisions were never possible, but today, especially, we need "good enough" decisions. When the pandemic hit, travel restrictions hurt Disney Parks. Yet, in 2021, they had raised profit by 20% with 17% fewer visitors than in pre-pandemic years. Disney accomplished this feat by introducing an app (Genie+), by changing their ideal customer and by removing free perks -- working through all the decisions at an amazing pace..

Starbucks is another example. The “third place” concept imported by Howard Schultz from Milan is dead. Starbucks recognized the future of drive-throughs—and walk-throughs—and is retooling stores. 

Innovation often stems from a good idea implemented rapidly. Starbucks could have waited until all the research was done but likely would have lost market share to competitors. 

That’s why innovative organizations need a new metric, Decision Speed, to capture how long it takes to make critical decisions. When an organization starts tracking this metric, they are often surprised by how slow they are. Innovation cannot thrive if decisions are made slowly. By the time you decide, the market has moved on.

Forget About the HPO

The third area to look for innovative decisions is by focusing on the weight of the HPO. I’m not talking about the British brown sauce. HPO stands for Highest-Paid Opinion. 

I noticed a quirk in decision-making. The highest-paid person in a room commands attention. If they provide an opinion, it is often treated as fact. Decisions are then biased toward the HPO. HPOs often don’t realize their power, but the outcome is the same.

Innovation requires a certain meritocracy to avoid group think. Half of my work with organizations is simply providing fresh air to members' ideas. I then help them challenge their own thinking. Here are my three favorite techniques:

Technique 1: Blind Decision-Making

As you collect ideas, arguments and comments, do it blindly. You can set up an anonymous form where individuals can enter their ideas or find ways to strip their names from them. A team can then look at all the information without being biased by individuals' status within the organization.

This technique often adds a layer of administration, so I only recommend it for the most important decisions. 

Technique 2: Devil’s Advocate

The Catholic Church establishes a devil’s advocate when exploring the possible canonization of someone. The person argues against that person and can be quite effective at uncovering holes in reasoning.

You can assign individuals to take different positions in a decision to see if they can poke holes in it. Randomizing this role ensures that everyone gets a chance to voice their concerns. We are not talking about heated debates here. We simply want to hear any potential issues before making a decision.

See also: Cybersecurity & Innovation: Keeping pace in 2023

Technique 3: Thinking Partners

The best executives have informally found thinking partners to bounce ideas off. Making this a more formal process helps them have someone who can challenge them while understanding the biases they might fall into.

A good thinking partner is someone who knows your preferences and is able to challenge you respectfully. They are typically at the same level within the organization though usually in a different function.

Regardless of what technique you use, an innovative organization is debating decisions and working together to find the best solution. If none of this is happening, there’s a high chance you’re simply making the same decision over and over again.

Conclusion

One of my favorite quotes is from William Gibson: “The future is already here, it's just unevenly distributed.”

Innovation is often about trying to find the existing future. Your organization is full of innovative ideas, any of which could help the business grow. Your goal is to uncover those ideas and allow them to influence important decisions.

The future will arrive, one decision at a time. Are you making the right ones?

Customers need their insurer to provide stability, clear communication and financial support during a claim – and to do so in a timely manner. Unfortunately, claimants have had to wait days or weeks to receive funds from their insurance provider, and a check in the mail is often their only payment option. 

In today’s digital-first economy, insurers need to innovate their payout processes to offer their customers speed, convenience and flexibility. Imagine enabling payouts that provide customers with a personalized experience and doing so in near real time. With just a point and click following claim approval, customers can control when and how they are paid – reducing payout timelines to minutes or less while providing the payout options that cater to each policyholder’s preference for managing their money. 

By digitizing payouts, insurers can transform a critical customer interaction, turning a painstaking process into a truly positive experience.

Fostering loyalty via payout choice

One way to build customer loyalty is to provide claimants with multiple options for how they receive payouts. In today’s hectic world, customers want to manage finances on their terms, but those terms vary widely based on personal preferences. 

According to a recent Carat Insights study from Fiserv, which polled more than 2,200 adults in the U.S., two primary issues drive payout decisions for customers: security of the payment (40%) and speed of funds transfer (29%). This research finds that older customers prioritize security, while younger customers are more concerned with getting their money quickly. For example, receiving a payout to a bank account is the familiar and trusted experience for older generations. Meanwhile, millennials and Gen-Z are more accustomed to using digital wallets and increasingly prefer non-bank payouts. These non-bank payout types can include digital wallets that are popular among younger generations, or even payouts made to crypto wallets.

According to the same Carat Insights study, only 38% of insurance customers have ever received a payout digitally – with most consumers thinking that a payout via paper check is their only option. This gives insurers a tremendous opportunity to drive customer loyalty by enabling a differentiated experience, moving money to their customers via the payout method that best fits their personal preferences. 

Increased efficiency affects the bottom line

While developing and deploying digital payouts creates a better experience for the customer, it can also create operational efficiencies for the insurer by reducing administrative costs. According to our data, improving workflows, eliminating the administration of paper checks and digitizing the entire payout process leads to a 60% reduction in payout costs and a 25% reduction in call center volume.

At the same time, insurers that use digital payout technology provided by their merchant acquirer can secure additional efficiencies by routing consumer payments — the pay-in — for insurance premiums along the same payment rails as the payout. Similarly, these insurers can also maximize consumer choice by providing their customers an expansive list of payment options – accepting consumer payments for premiums from traditional sources like credit and debit cards, but also via ACH and popular digital wallets.

See also: Payment Processes Must Be Simplified

Paying out in a matter of minutes

Many insurers in the U.S. are beginning to focus on digitizing multiple touchpoints within their claims process. For example, we have been working closely with industry leaders to advance these initiatives via a customer-facing digital portal that gives claimants the ability to accept claims funds and the control to choose how they would like funds delivered. 

Once notified by their insurer that their claim has been approved, a claimant is prompted to access the digital portal via their personal device, at which point they can opt for a digital payout instead of a paper check. Claimants choosing to receive funds digitally then select from a list of payout options that include having a digital check delivered instantly via email, payouts sent to their bank account via a debit card or ACH transfer or even having funds delivered directly to their PayPal or Venmo account. 

Such digital payout initiatives have successfully paid out millions of insurance claims digitally across our client base – helping these insurers deliver an industry-leading experience for their customers. When paired with parallel efforts from the insurer to expedite how claims are reviewed and approved, some insurance claimants have been able to complete the entire claims process (from submission to payout) with their insurer in less than 20 minutes.

Digital transformation is changing how the insurance industry operates for the better. By digitizing payouts, insurers are creating better experiences, reducing costs and making it easier for customers of all ages to work with their insurer.

In the always escalating war between hackers and the rest of us on cybersecurity, the bad guys have a scary new tool: If they can get even a few sentences of recorded speech from someone, they can simulate that person's voice realistically and have it say anything they want.

At the moment, these voice fakes are primarily being used to fleece elderly people by generating anguished calls supposedly from children or grandchildren who are in a predicament and need several thousand dollars wired to them immediately. And it's easy to see the potential for broader abuse. 

Email scams that simulate messages from senior executives routinely con employees in finance departments into wiring hundreds of thousands or even millions of dollars to a "supplier" or "customer" that is actually a hacker bank account. That scam has happened often enough that many finance employees have learned to be careful, but now imagine if the hacker could follow up on that urgent email with a distressed voicemail message that sounds like the CFO demanding to know why the employee is being so slow.

An article in the Washington Post explains: 

"Powered by AI, a slew of cheap online tools can translate an audio file into a replica of a voice, allowing a swindler to make it 'speak' whatever they type. Experts say federal regulators, law enforcement and the courts are ill-equipped to rein in the burgeoning scam. Most victims have few leads to identify the perpetrator, and it’s difficult for the police to trace calls and funds from scammers operating across the world. And there’s little legal precedent for courts to hold the companies that make the tools accountable for their use.

“'It’s terrifying,' said Hany Farid, a professor of digital forensics at the University of California at Berkeley. 'It’s sort of the perfect storm … [with] all the ingredients you need to create chaos.'”

Even a year ago, the article says, a lot of audio was needed to clone a person's voice. Now, just recording a TikTok where you talk for 30 seconds is enough to let someone clone your voice using a tool that costs as little as $5 a month.

Based on so little audio, the tool can't replicate the mannerisms of a speaker or the language they would use, such as nicknames, if the person being impersonated was actually making the call. But the AI-generated voice sounds so much like the real person that people can be fooled, especially in a stressful situation where speed is demanded. 

Authorities say that, as usual, the best recourse for potential victims is caution: Be very suspicious of any urgent request, and always verify with the person supposedly making the request that it's really from them, whether in a personal or business setting. 

Eventually, tools will be developed that will help test whether a voice is being generated by AI, just as tools can now help test whether text or deep fake images come from AI... but the bad guys will probably be on to the next scam by then. 

Insurers have done an increasingly good job helping cyber insurance customers be more secure in the face of unrelenting attacks by hackers -- a topic we explore in detail in this month's ITL Focus, on cyber -- but they will obviously have to stay vigilant. There's no rest for the weary.

Cheers,

Paul

P.S. The latest scam comes in the context of continual improvement in what's known as generative AI. Last week, OpenAI released GPT-4, a new version of ChatGPT, just four months after it released ChatGPT and captured the world's imagination. The Atlantic does a good job of explaining what's new in this article, from March 14. 

The gist:

"Rumors and hype about this program have circulated for more than a year: Pundits have said that it would be unfathomably powerful, writing 60,000-word books from single prompts and producing videos out of whole cloth. Today’s announcement suggests that GPT-4’s abilities, while impressive, are more modest: It performs better than the previous model on standardized tests and other benchmarks, works across dozens of languages, and can take images as input—meaning that it’s able, for instance, to describe the contents of a photo or a chart....

"From what we know, relative to other programs, GPT-4 appears to have added 150 points to its SAT score, now a 1410 out of 1600, and jumped from the bottom to the top 10 percent of performers on a simulated bar exam."

We would all love to believe that everyone working in insurance is the paragon of honesty and ethical behavior and that it is the outsiders, the criminals, the general bad actors who are committing fraud against the industry. Unfortunately, this simply is not the truth. Although not the norm, insurance industry professionals have been known to participate in fraud schemes, and agent gaming is just one of the ways it happens.

Agent gaming occurs when insurance agents manipulate the details of insurance policies to alter the premium, to improve risk acceptability or to maximize their commissions and key performance metrics. It is a bad practice that can add risk to an insurer’s book of business while leaving a trail of upset policyholders. Despite the severity of the problem and the potential impact on insurers and insureds alike, agent gaming has been difficult to detect and mitigate. Fortunately, insurers have new technology that can help them.

What drives agent gaming

Insurance agents have incentives to sell a high volume of policies in the shortest time, but there are obstacles. While 65% of insurance customers still prefer to work with an agent, digital insurers create competition. This has led to financial pressure that may drive some agents to cut corners so they can write new accounts.

In addition, both home and auto insurance rates rose in 2022, which means it can be a lot harder to keep customers happy. If agents can’t provide competitive pricing, customer loyalty can erode, and policyholders may default to doing their own research and comparisons, leading to defection.

These challenges can act as a misaligned incentive for insurance agents. Because agents act as a conduit between policyholders and insurers, they have the opportunity to misrepresent policyholders when shopping for quotes. An insurance agent can increase the chances of a quick sale by manipulating a policyholder’s information or modifying essential facts to arrive at either a more affordable premium quote or to ensure the quote is considered acceptable by the insurer.

Agent gaming hurts insurers and policyholders

Premium leakage is one of the primary effects of agent gaming, and it’s part of an expensive problem for insurers. Industry insiders believe premium leakage in total may account for as much as 14% of an insurer’s direct written premiums. In other words, if an insurer has $500 million DWP, premium leakage will cost the organization $70 million per year in lost revenue.

Policyholders usually have no idea they’re part of an agent gaming scheme. Once the scheme is discovered, however, it can have bad consequences for the individual. For example, the insurer may decide to raise their rates, part ways with the policyholder or, in the event of a claim, deny or partially deny the loss. This can represent a surprising and unwelcome financial burden on the innocent policyholder.

Agent gaming can go the other way, as well. Instead of making premiums artificially lower, agents may inflate commissions by making policyholders’ premiums higher by signing them up for unnecessary coverage. Even if the scheme is detected, policyholders may never be able to recover these incremental premium expenditures.

Given the variety and scale of its impact, agent gaming can’t simply be regarded as a cost of doing business. This practice costs substantial amounts of revenue – and unwinding agent gaming schemes can cause a considerable amount of customer dissatisfaction. Therefore, insurers need ways to catch agent gaming earlier, before the scheme becomes entrenched.

See also: Characteristics of an Effective Change Agent

Manual investigation catches agent gaming after the damage is done

It can be difficult to detect agent gaming via manual review. A premium manipulation scheme, for example, will often reveal itself with a long list of policies that have abnormally low (or high) premiums. A clever, but dishonest, agent will avoid gaming every single one of their policies to evade detection. In fact, looking at their list of policyholders might reveal only a few customers with lower rates than normal. 

Even comparing a bad actor’s book of business with other agents' may not reveal them to be an obvious outlier. Manually comparing agents with one another takes time, and it’s difficult for investigators to review a large sample of agents, given other priorities. 

In many cases, agent gaming schemes aren’t revealed until a policyholder files a claim. At this point, the insurer has already lost significant revenue due to premium leakage.

Detecting agent gaming schemes sooner with AI solutions

AI solutions are capable of more nuanced analysis. They’re able to compare every agent in an insurer’s network against every other agent to find the biggest statistical outliers. This gives insurers a much better picture of the agent gaming that may already exist in their book of business.

Being an outlier doesn’t necessarily mean an insurance agent is gaming the system, however – it might just mean that an agent is a good salesperson. Therefore, AI solutions need to be capable of drilling down into individual policy details. 

Here’s an example: If an agent understates a home’s square footage or replacement cost to devalue the property and obtain a lower rate, the AI should be able to spot the problem using external data. For example, the AI solution could access county assessors to find the exact square footage of the home and then flag the discrepancy for investigation.

The crux of the matter is that AI doesn’t just perform this check for existing policies. AI solutions are also capable of flagging agent gaming in real time during the application process. In other words, insurers don’t need to wait until they start to lose revenue before they detect agent gaming schemes. Instead, they can keep their agents honest throughout the entire policy lifecycle.

The new kids on the block (#insurtech startups aiming to be disruptors) have not made a dent and for sure will not kill insurance incumbents. So, why should an insurer innovate? Because technology and data are incredible opportunities to lower the loss ratio and improve return on equity. (Do you remember my Insurtech 4Ps?)

Let's look at auto insurance. Root (the new kid on the block) once upon a time was a telematics-based unicorn, but it has never used telematics data well. Nowadays, they have given up on telematics (their latest storytelling is about embedded), and their market cap is about $50M (in March 2022, it was about $500M).

By contrast, Progressive (with a market cap of $81B) presented their 2022 results on Feb. 28 and dedicated all their earning call to telematics, providing tons of food for thought for anyone working on auto insurance in the U.S.

Below, I'll analyze nine key takeaways (with facts and figures) from this earnings call, exploring the future of auto insurance. (Spoiler: The future will be telematics-based! And you will get the reason for the headline of this newsletter edition.) 

Mobile-based telematics has made it feasible to apply this technology in any insurance market worldwide. Here, you have a recent example of the success at Cambridge Mobile Telematics in implementing telematics in Japan. There is no such thing as "a market not ready for auto insurance telematics," only telematics programs that are not good enough.

Are you working at an auto insurer that has not yet figured out how to use telematics capabilities to make more revenue, improve driver behaviors, price risks more accurately and retain more customers? You are leaving relevant opportunities on the table! You are leaving behind more than 10 percentage points on your combined ratio!

Source: Jan Myszkowski Oct '22

Let's take a quick look at Root's facts and figures.

• '22 gross written premiums $600M (vs $743M in '21)
• '22 loss ratio (including loss adj. expenses) 91% (vs 99% in '21)
• '22 combined ratio of about 120% (vs 157% in '21)
• '22 net losses $298M (vs $521M in '21)

Basically, Root has cut marketing expenses, reduced new business (more unprofitable than renewed and already pruned business) and reduced overall costs in line with the shrinking of the top line.

A year ago, I wrote: Root is not using telematics data well for pricing and risk selection. Moreover, they have even denied the usage of telematics data for claim management and for changing driver behaviors.

See also: It's Time to Get Back to Basics

They realized it and pivoted to the new buzzword: embedded insurance. Below are the mentions of "telematics" ("telematics" + "UBI") vs "embedded" in their quarterly shareholder letter:

Embedded insurance (with Carvana) represented 41% of new business in Q4 '22 (vs 3% in Q4 '21). That should represent about 9% penetration on cars sold by Carvana.

Wrapping up: They don't have their act together and haven't developed adequate telematics capabilities, and their market cap is an infinitesimal fraction of the money they raised. (Their IPO valued them at about $7B in '20.)

Does this mean telematics doesn't work? No! Absolutely not! Progressive's story, words, figures and acts demonstrate that it works well.

Progressives' earning call gave nine key takeaways for the future of auto insurance:

1. Auto insurance is your most relevant business line and is here to stay, and you have to innovate it

(Do you remember my "rumors about the death of personal auto insurance have been greatly exaggerated"?)

Progressive showed innovations (introduced over the years) and announced many further changes in their telematics-based approach. Progressive's CEO didn't talk about it on the stage of a conference; she dedicated the full Q4 earning call to this innovation journey.

They are not doing it because it is cool to be innovative. You can feel the C-level commitment to using technology and data to do the auto insurer's job better. They are innovating because it contributes to achieving their strategic goal "to grow as fast as possible while delivering a calendar year 96 combined ratio" and represents a concrete opportunity to increase their return on equity! We are talking about the second-largest U.S. auto insurer, with a personal auto loss ratio 10+ percentage points better than the market average in 2022 and a total shareholder return constantly in the top 10 insurers worldwide for the past two decades.

They took this insurtech approach (telematics) seriously and are obtaining terrific results on their most relevant business line. So why are you still ignoring/denying it?

2. The future of auto insurance is telematics-based

This 86-year-old carrier has been a pioneer in using telematics data since 1996, and has constantly invested in developing its telematics capabilities. Progressive has clearly talked about where they see economic value in using it:

• "Segmentation is a key facet of our competitive prices pillar, and nowhere is that more evident than in our investment and usage-based insurance products"
• "UBI is our most predictive rating variable, and it provides unparalleled rate accuracy"
• "It's about segmentation and matching rate to risk"
• "The program systematically helps us retain lower-risk drivers" 
• "Our claims representatives have seen that telematics data can help them settle claims more quickly and efficiently"
• "Offering a service to detect and respond to major accidents [...] customers have consistently told us that this kind of service is something that they really do value"

(Here is an old (gold) paper published with Swiss Re back in 2017: Unveiling the full potential of telematics )

Customers are ready in any market (a recent survey -- done by the IoT Insurance Observatory together with Swiss Re on 10,000 policyholders in nine different markets -- shows a high and consistent level of acceptance: Only a fifth dislike the telematics approach!) Regulation is not an absolute barrier in any market (only a constraint that would influence the execution of the program). And mobile-based telematics removed the old barrier: The telematics hardware was too expensive in some international markets where the annual insurance premium is below $200. The size of your company is not an excuse, either: You don't need to be a 20M policy carrier; there are international telematics success stories from players with less than half a million policyholders. In every market, you can find tech players and system integrators that allow smooth execution in your organization.

It is 2023, and there are no more excuses. What is your reason for still leaving on the table all these percentage points on your combined ratio?

3. Surcharge bad risks

Progressive explained well why it is necessary to surcharge bad risks to create value through more accurate pricing, and how it works:

• "Participation discount is larger now, at 15% instead of 10%"
• "We've increased the size of the maximum potential discount to 45%, and the maximum potential surcharge to 60%" 
• "75% of customers still receive a discount, and only about a fifth receive a surcharge"
• "Renewal rates for the safest drivers who are earning the biggest discounts are about 6% higher than average, while they're about 16% lower for the riskiest drivers who aren't receiving a discount"
• "We had deployed this new continuous model in 12 states, representing over a quarter of our net written premium, and plan rollouts to most of the rest of the country during 2023"
• "Early on, we weren't pricing to the full curve. So, we understood that a lot of people should be receiving surcharges and that some people should be receiving bigger discounts. [...] I think we are now very close to or almost completely pricing to that curve with the data we have today. [...] we continue to advance the size, continue to pull additional data elements [...] our segmentation game is never ending. We're always trying to continue to advance to find additional data to use that can keep us with a gap between us and the competition"

Telematics Innovation at Progressive (Feb '23)

How many times have you heard at conferences or read in articles that customers don't want to be monitored, don't accept the idea of being surcharged and would massively switch if you surcharge them? Progressive's "the share of our personal auto customers participating in Snapshot has moved steadily upward" seems a definitive answer to these doubts.

Putting together the information from this earning call with the previous ones, It seems that the telematics penetration on the new business is above 46% in the direct channel and about 13% through the agent channel.

4. All the policyholders must have a telematics app whatever the product chosen

This has been the most substantial and most surprising message in Progressive's earning call:

• "We know that despite how times have changed, there's a large segment of customers who don't want their insurance premium to be based on their driving data"
• "That means that if we limit this just to our Snapshot customers, we'd be leaving out a lot of others. So, in March we plan to start making accident response available to all of our auto customers, not just those who are in Snapshot"

(Do you remember the telematics predictions Harry Huberty and I dropped a couple of years ago?: "It will be the norm in the U.S. personal auto market for customers to download their insurer’s app on their phone to be insured. This app will continuously use the smartphone’s sensors to deliver a superior customer experience regardless of what product a customer chooses: pay-per-use, telematics-based renewal pricing or a policy with a traditional rating based only on traditional variables such as age, credit score, etc.") 

Progressive has started monitoring all policyholders to create value for both the customer and the insurer,

5. Wrap services around the insurance contract

Their talk about services: "We'll use data from the sensors on the phone to detect when a serious crash is likely to have happened. We'll reach out to the customer to confirm the accident and to see if they need help. If we don't hear from a customer at all, and it seems particularly serious, we'll request that the police conduct a well check to make sure our customer is OK. Since we know the customer's location from the telematics data, we know just where to send them. [...] This adds value to the customer's relationship with us and can become another reason to choose and to stay with Progressive. Additionally, while other insurers offer crash detection to their UBI customers, we'll be making it available to all of our personal lines auto customers, whether they're in Snapshot or not. Third, we're deliberate about dispatching EMS."

• Honestly, we have merely scratched the surface of the service opportunity ("continuous is a little bit more expensive [...] we're excited about and what Jim talked about, the excitement about it is the services that we're going to provide, especially in some of the claims examples") in the earning call
• However, Progressive has only just started with the continuous monitoring approach. ( "Our most recent addition is continuous monitoring, which began its rollout in the summer of 2022")
• I'm pretty confident they will fully understand the service opportunity in the coming years

6. The IoT paradigm gives tremendous value to claim handlers (and mobile-based telematics data is good enough)

• ”Having this telematics data available, we're able to get their claims started more quickly and able to handle it more efficiently”
• ”Within two minutes of the impact, we reached out to our customer […] That agent dispatched an ambulance and a tow truck […] it took only 10 minutes from the time of the accident to when we had a claim in our system”
• ”This customer had this accident just two days after buying their policy […] we can see from the telematics data that the crash happened where and, importantly, when our customer said it did […] very confident that this loss did occur after and not before the customer purchased the policy”

"Over the last couple of years, we've experimented with offering a service to detect and respond to major accidents," and "We'll use data from the sensors on the phone to detect.” Do you really think that Progressive would have released this feature to all their policyholders if they had not been super-confident with their mobile-based crash detection?  

I told you this in my first LinkedIn article back in 2014:

• “Emergency services with automatic claim detection or buttons for direct-dialing the assistance center”
• "Act more proactively […] make the whole process faster and more efficient, by anticipating: the actual verification of the claim (anticipating the first notice of loss); the direct contact with the client for claim description”

7. Telematics is a capability, not a product

My friend Pete Frey highlighted in an article we wrote together in 2021: "Telematics adoption should be seen “as not just launching a program but actually building a business capability within your organization. The biggest difference as you switch your perspective from program launch to capability building is that you look at building buy-in, understanding and expertise across the organization while launching the program." In an interview with Forbes in 2020, I shared: “building the capability to master the IoT insurance paradigm is an achievable target, and it doesn’t require tens of millions of dollars. However, governing this journey and transforming the way an insurance company does business will require a multi-year commitment and strong leadership.”

I had a couple of meetings in the last few months where two different insurers (both listed) gave me the clear feeling they didn't get it and will not obtain any result from telematics. At least, not with the current leadership teams.

In the meeting with one of the insurers, their main question was: "Can we hope someone will bring us a driving score already calculated? This way, we will have not to deal with app, devices and telematics data". The second insurer (bigger and with an international presence) opened the discussion by saying "for us, telematics is only about claim management. Can that app give us the same data as this device?".

See also: Embedded Insurance Is Everywhere

What have we heard in Progressive's earning call?

• "Getting into telematics is not easy. [...] It's not just as simple as adding a new rating variable. It takes broader and sustained effort, new capabilities and investments"
• "I'd like to tell you about how we've built on that long history in telematics that Tricia discussed"
• "Its efforts and investments over the past 20-some years has established a lot of these capabilities or learnings that we can leverage"
• "We're going to continue to evolve and continue to advance our competitive advantage when it comes to pricing and telematics"
•  "I'd like to share some exciting news that doesn't involve using telematics data to more accurately rate policies but instead builds upon our telematics heritage to provide a valuable service to our customers"
• "Invested in a process of continuous improvement in our UBI products"
• "In parallel to our efforts in personal lines, we were developing UBI for commercial auto products"

A competitor’s product can be replicated in a few months, but capabilities require time to be built and internalized in the organization. A capability gap is going to require years to be closed. The sooner you start your telematics journey, the better.

8. You don't need to wait for OEMs or beg for their data

When you talk to an insurer struggling with telematics, it is frequent to hear the belief/hope/illusion that connected cars will change everything, that OEM data are the inevitable end game, that OEM data will allow insurance telematics to take off and that this will happen soon.

What has Progressive said?

• "Working with data collected by automakers. [...] They've been working to show the value of these programs to their customers so that they'll sign up to share that data with them"
• "And we've been able to tap into that. When a customer comes to us to quote and their driving data is available, we'd ask the customer if they'd like us to use it to determine their price. They say yes, we bring that data in and apply the UBI discount or surcharge to their quote immediately, again, pushing that rate accuracy to where it matters most, the new business quote"
• "This isn't common yet, but we're excited about the opportunity it represents"
• "The framework we're talking about depends upon vehicles with cellular connection. [...] Some, you know, are still working on it. So, it's -- and it does take a while for the fleet to turn over. So, it's very focused on a few OEMs and the most recent model years. [...] So, we expect that this population will grow over time"

Basically, we are talking about customers asking for a quote from Progressive, and already have a driving score generated by their connected cars. This is a specific use case that doesn't happen too frequently yet (but will be more frequent in the future). Progressive is happy to use (and pay for) this additional information at the point of quotation.

Telematics Innovation at Progressive (Feb '23)

I'm pretty aligned with this vision. There are not a lot of data today (insurers insure all the cars in use today, not only the new sales), OEM data can be helpful for some specific use cases (that don't require a lot of data), and a few times you are even able to find sustainable business care considering the high cost of this data.

However, many of the telematics opportunities require continuous monitoring and can be addressed better with the insurer's mobile app.

Here a recent conference where I talked about OEMs and insurers VIDEO 

9. [MISSED] To change behaviors is an incredible opportunity for an insurer

Driver behaviors can be changed, and the most effective way is through frequent and tangible rewards. Over the years, a material part of the IoT Insurance Observatory's research has been dedicated to this use case (in the different insurance domains, not only personal auto). I've seen best practices obtaining up to three percentage points on their combined ratio changing driver behaviors. Last year, in the March edition of this newsletter, I interviewed Anton Ossip (Disovery Insure's CEO), who built a great telematics program focused on changing driver behaviors: Vitality Drive. A broader perspective on behavioral change can be found in the paper I published with the Geneva Association in 2021: From risk transfer to risk prevention

This issue is totally missing in Progressive's talk, and it is a pity because to apply it on all the portfolio (point 4 above) is a fantastic opportunity. I'm sure we will hear even this in Progressive's earning call within two or three years.

This earning call should be read again and again by everybody working on auto insurance. A lot of food for thought.

My advice in a nutshell: Be more like Progressive and less like Root.

The metaverse is coming — fast. In PwC’s 2022 US Metaverse Survey, 82% of business executives (including 87% of insurance executives) said they expect metaverse plans to be part of their business activities within three years. Insurers that move swiftly and wisely to use the metaverse can find success in two ways: 

1. Enhance operations and customer engagement by engaging employees, customers and policyholders in new ways.
2. Create new revenue streams through coverage of new, metaverse-specific risks.

Here is a brief rundown of some of the main opportunities in each area — and five guidelines to help seize them.

Engage and excite your stakeholders

The metaverse is on its way to becoming an immersive, global and decentralized digital world that blends seamlessly into the physical one. This new world can enable insurers to reach customers in new ways and deepen relationships with existing ones. It also can help insurance employees pick up new skills, collaborate more intensely and do their jobs more effectively — all while potentially cutting costs. It can, for example, help:

• Upskill the workforce in new ways. Metaverse tools available right now can train employees in hard skills (such as risk or damage assessments) and soft skills (such as leadership and resilience). Metaverse “campuses” can support remote work, collaboration, recruitment, onboarding, performance management and more. These tools can be both lower-cost and more effective than in-person equivalents.
• Transform underwriting and claims. Instead of (or in addition to) sending staff to inspect physical properties in person, your employees can inspect their digital twins in the metaverse. These detailed, interactive, continuously updated 3D models can help your people better assess risks and identify risk-mitigation measures. That can support more accurate, lower-cost underwriting. Digital twins can also enable claims assessors to virtually walk through an incident scene. They can then better determine damages, recreate and simulate incidents for training and service claims faster. 
• Captivate customers. Offices in the metaverse enable carriers and clients to share information and ideas (e.g., the results of the 3D model assessments we mention above) and assess coverage needs via a personalized and personable immersive experience while saving time and resources.

Close the protection gap — and increase revenue

Like any new set of technologies and experiences, the metaverse offers new risks. There sometimes are metaverse “accidents” (such as service outages at key moments), as well as criminal and malicious behavior including scams, financial fraud, intellectual property theft, data breaches and abusive behavior. 

Because few of these risks are covered today, insurers could create new lines of business through metaverse-specific products. Although it will likely take time to properly assess and price some of the newest risks, there are several potentially attractive entry points for insurers that understand the metaverse and its underlying technologies.

• Digital assets. Digital assets in the metaverse aren’t just cryptocurrencies. They also include NFTs, avatars and virtual real estate. Some are highly valuable. Most are blockchain-based and don’t run through traditional financial institutions, so there may be no recourse if they’re lost or stolen. Virtual real estate also carries new risks, ranging from “cybersquatting” and vandalism to missed mortgage payments, operational failures and new legal liabilities. Demand already exists for insurance against these and other digital asset risks. As the metaverse matures, this demand could grow exponentially. 
• Events and entertainment. More and more brands are hosting metaverse events, which include product launches, fashion shows, concerts and parties. Most risks related to these events are familiar, but “translated” into a digital world: surprise cancellations or delays, technical failures and threats (such as abusive behavior or cyber attacks) to attendees’ health and safety. All these could lead to financial losses or legal liabilities for companies — many of which find insurance appealing.
• Intellectual property. When companies put their IP and brands into an immersive digital world, they also may make them vulnerable to cybertheft. Deepfakes and other forms of digital fraud on the metaverse can also infringe on content, trademarks and copyrights, causing financial loss and reputational damage. Metaverse platform providers and marketplaces may also be liable for some of these IP violations. Savvy insurers may be able to build on traditional IP policy coverage to cover these and other related threats in the metaverse

See also: The Metaverse and Financial Services

Five steps to help start or accelerate metaverse initiatives

To take advantage of the metaverse’s opportunities, insurers will need to:

1. Set a strategy. Chart a course for sustainable success in the metaverse by assessing market opportunities and your own current and potential capabilities. Consider immediately available business outcomes as well as the potential for business transformation and all-new revenue streams. Your strategic assessment can include limited, low-risk tests of new technologies and products with select groups of employees and customers.
2. Choose the right tech. With your strategy in place, determine which technologies and related processes you’ll need. This requires not only choosing the right metaverse platform — based on business and customer preferences — but also buying or developing metaverse-specific technologies (such as specialized AI) and crafting appropriate procedures and controls. 
3. Build skills. For internal use cases, business growth and risk management, the metaverse requires new skills. Fortunately, you can teach basic skills through training in the metaverse itself. For more advanced ones, you may need to send your tech experts for focused training, make new hires, or work with third parties that can provide the key skills you lack.
4. Build in trust. Among the new metaverse-specific risks that every company must manage, insurers should pay special attention to (fast-evolving) regulatory developments and to security. It’s also important to go beyond the letter of the law and design trust upfront and throughout your metaverse operations — rather than having to make fixes later. No one wants to make headlines for suffering a metaverse scam, security breach, privacy violation or tax penalty, and insurers naturally prefer to avoid these risks.

Learn and grow. As you begin to launch metaverse products for business and enterprise use cases, have measures in place to keep managing your risk appetite and expand your offerings. For example, if you begin by adding metaverse-related line items to existing coverages, you may soon be able to offer whole new lines of business as you deepen your institutional understanding of the metaverse.