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.

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.