Following this year’s InsureTech Conference in Las Vegas, it's clear we are operating in a new environment. Here are some thoughts that may be helpful in guiding stakeholders.

We have entered a hardening market amid rising interest rates, soaring inflation and continuing CAT challenges. That means we can expect price increases for insurance across the board. 

Venture capital investment is slowing, and insurtech valuations are moderating. Investors will no longer finance companies that spend a dollar to make 90 cents. Rather, they are looking for insurtech organizations that can achieve strategic efficiencies in both the types of products offered and the distribution methods for those products. The best companies will find investors,  but they may very well receive lower valuations compared with the past few years.

Besides funding constraints, insurtech startups will see a limited appetite for capacity, as major carriers deal with losses from major weather events and rising interest rates.

While several high-profile insurtechs have had a rough year in public market valuations, there are still many bright spots for startups in this marketplace.

See also: Insurtech Success Stories: Still Waiting for Godot

Investors are taking a closer look at startups and emerging growth insurtechs that can clearly demonstrate efficiencies in underwriting, products and distribution—and a realistic path to profitability. By specializing in a niche, MGAs have become more attractive investment vehicles, especially as they are more agile and can leverage advances in technology and data management that larger entities often struggle with. For example, my team and I have created an emerging growth MGA whose online product offering is transforming the experience of buying jewelry insurance throughout the U.S. I brought years of experience in the retail jewelry business, while my co-founder complemented that with in-depth expertise in insurance fundamentals -- the kind of combination that created considerable buzz at ITC this year.  

Our industry has learned key lessons from its past and is finding the best path forward. The market can’t rely on endless venture funding alone to grow. Insurance business doesn’t work that way. But, as seen at ITC, our industry is adapting and improving and is here to stay. 

We hear a lot of talk these days about the virtues of data-driven organizations. That’s certainly reasonable up to a point — but what does it really mean? When it comes to routine operational decisions, in particular, the current bias seems to favor increased automation over human judgment. The data doesn’t lie — or so the story goes — so we’re better off deferring to programmatic decision models.

That notion may be reasonable for some situations, but when you’re operating in a complex and nuanced domain like casualty insurance claims, that highly automated decision paradigm can begin to fall apart very quickly. Thousands of different variables come into play. Medical records and accident reports contain subtle details that provide vital clues about potential risks. To complicate matters, important minutiae are often buried deep inside the narrative content.

An experienced claims manager can pick up on that nuance, provided they have adequate time and attention to devote to reviewing the documentation. Can an algorithm accomplish the same thing?

The short answer is yes, but that comes with a vitally important caveat. In complex domains, advanced data analysis should not drive automated decisions; it should inform and empower human beings to make more effective decisions. The most effective artificial intelligence (AI) initiatives in place today are doing exactly that.

Data-Driven vs. Data-Informed

The distinction here is critically important. The data-driven paradigm is about automation. It’s about shifting decision-making responsibility away from human actors and trusting the algorithms to take their place.

A data-informed approach, in contrast, empowers and assists people to make better decisions by flagging potential risks, highlighting anomalies, and monitoring for changes that may indicate a need for attention. It’s a helper, not a replacement.

For claims managers, this approach has powerful implications. Imagine, for example, that an injured worker has missed three consecutive appointments for physical therapy. What does that mean? If the employee no longer feels a need for treatment, then it may be a sign that they’re ready to return to work, but it could also be an indication that the case has taken a turn for the worse. In either case, an adjuster should be made aware of the situation so they can make a proper assessment.

In a complex domain like claims management, this data-informed approach holds tremendous potential for transforming organizational culture and processes.

Consider how claims are handled today at most organizations; adjusters typically follow up with cases on an “as needed” basis. Depending on the individual adjuster, that might involve a diary notation, a running to-do list, or a collection of sticky notes. Inevitably, though, it means manually reviewing medical bills and records as they come in or when the adjuster’s schedule permits.

In a data-informed organization, claims adjusters focus on meaningful decisions. Because they no longer need to spend their time scanning records in search of salient information, they have sufficient bandwidth available in which to apply their professional judgment on high-priority cases. AI does that legwork for them.

Data-informed organizations can apply their valuable resources toward predictive severity-based workloads. They can focus on claims that need attention today — based on real-time data. Incoming documents are reviewed and scanned by AI, and claims adjusters are notified when a case requires their attention.

See also: The Data Journey Into the New Normal

The Business Opportunity for Insurers

The data-informed approach is already operational in a number of leading companies around the world. It’s transforming processes and driving cultural change — but not in the way that many AI skeptics have predicted. Data-informed organizations aren’t dehumanizing their processes. On the contrary, they’re empowering and elevating their claims professionals by enabling them to focus on meaningful work.

The data-informed paradigm is about focusing on the right claims at the right time. It’s about spotting correlations and anomalies, identifying potential risks and bringing those to the attention of an experienced claims manager.

The result? A data-informed organization has a shorter claim duration and lower-than-average total claim costs. Not surprisingly, workers at data-informed organizations also enjoy substantially higher job satisfaction. These companies are generating high ROI — not by reducing their workforces but by elevating them to higher-value activities.

The Build vs. Buy Debate

How does an organization achieve that kind of transformation? It starts with a predisposition toward innovation and a recognition that advanced data analytics has the potential to transform claims management from an operational perspective.

Conventional wisdom tells us that proprietary data is a differentiated asset. In other words, companies place a high value on their internal data because it’s theirs, and nobody else has it. In the world of AI and machine learning, though, more data is generally better. When ML models have access to higher volumes of information, from a relatively wide array of sources, they can “learn” faster and more effectively.

Building and maintaining those kinds of high-volume data sets can be extraordinarily costly and time-consuming. The implication for insurers is that, in the build-versus-buy debate, there is an increasingly powerful case for moving beyond proprietary data and embracing best-in-class platforms to drive the data-informed model.

This provides for a flexible co-innovation process, enabling insurers to leverage solutions and platforms that have already been proven in the real world, without reinventing the wheel. It’s the fast-track alternative for companies seeking to become data-informed organizations.

As first published in Claims Journal.

Giving the customer what they want — or need, depending on whom you're quoting — is a basic tenet of business. And perhaps the one thing all customers want — and need — is a good experience with the company they are working with.

It's a truth that the insurance industry needs to understand. One of the biggest complaints consumers have against their insurers is the often lengthy time it takes to settle claims. And dissatisfied customers often turn into former customers; studies show that over 60% of customers are likely to switch to a competitor after just one bad experience — while more than three-quarters would jump ship after just two such experiences.

If customers equate slow claims processing with poor service, insurers need to find a way to speed up claims — and fortunately, modern technology, in the form of advanced computer vision, can provide insurers with the means to do just that. These advanced computer vision systems use cameras and visual detection systems in vehicles, drones equipped with high-resolution cameras and images taken by devices such as phones and tablets. The images are then automatically uploaded to machine learning-based AI systems that analyze thousands of data points — type of damage, estimated repair costs, structural integrity (of buildings or vehicles) and much more.

These AI-based systems — relying on advanced data analysis and using machine learning — are able to develop a clear basis for claims far more quickly and accurately than with the traditional method, where human adjusters must visit the scene of the claim and human actuaries must determine the amount to be paid. Companies can thus cut down the investigation phase of a claim from weeks to minutes — rapidly approving settlements and ensuring high customer satisfaction and retention.

Investigations of structural damage from natural disasters, fires, hurricanes and similar events — where conditions make it difficult, if not impossible, to send out adjusters — become much more efficient with computer vision technologies. Drones can much more easily navigate these disaster scenes, flying into corners and crevices and recording images over, under or inside damaged structures. AI-based systems receive and examine the images, determining what damage was caused by the event and what damage may have already been in place. The analysis is based on damage models — patterns of damage based on specific events associated with disasters, such as how a roof would appear if it were blown off by a gust of wind measured at 75 mph — as well as with data on other similar incidents. With all the data taken into account, the system can quickly provide a full assessment of the damage and determine how much the insurer needs to pay to satisfy claims.

Computer vision can also streamline the claims process for incidents involving vehicles — often a sore point for customers, and the source of the largest number of complaints by policyholders. Here, too, the traditional process of claim evaluation — with adjusters sent out to evaluate the condition of a vehicle, the circumstances of an incident (traffic, weather, road conditions, etc.), along with statements by those involved in the incident, as well as police — becomes a time-consuming project that taxes the resources of insurers and leads to undue delays in claim settlement.

See also: Customer Segmentation Is Key

Images taken right away with a customer's mobile phone camera, along with data collected from sensors, road cameras and images recorded by in-vehicle cameras, are collected and analyzed by AI models, which provide an accurate picture of the circumstances of an incident, along with the liability of drivers and the amount of money the company needs to pay. Neural networks running on the customer's mobile device can provide real-time guidance to ensure suitable images are captured that can be used as evidence as part of the claim process.

Computer vision systems can also make sure that insurers get a clear picture of the condition of a vehicle before they even issue a policy — with previous damage noted and excluded from the policy, ensuring that they, too, are treated fairly in their relationship with their customers, eliminating potential fraud on the part of customers.

In addition to increased efficiency and speedier claim settlements, computer vision-based systems ensure that insurance processes are more likely to be perceived as fair by customers — increasing the level of customer satisfaction. Unlike human adjusters, computer vision systems are unlikely to miss small details that could end up being crucial to ensure an accurate evaluation. Customers will appreciate that insurers are doing everything possible to make sure that they get all the money they are entitled to as quickly as possible — and will reward their insurers with continued loyalty.

The technology to implement these systems is available right now, and as the technology proliferates, the price of systems continues to fall. That insurance companies have not yet widely adopted these systems yet is understandable  insurance is a very conservative business, and companies need to ensure that stockholders, stakeholders, and regulators are on board with major changes to the claims process. But implementing computer vision systems for damage assessment is worth the effort; companies will avoid wasting time and money, and ensure that they use their resources as efficiently as possible — while customers will get their money faster, increasing their satisfaction with their insurers and ensuring that they remain customers for many years to come.

Cyberattacks and breaches are happening more frequently and carry a hefty price tag, yet most organizations are still unprepared to deal. According to the IBM 2022 Cost of Data Breach report, the share of breaches caused by ransomware grew 41% in the last year and took 49 days longer than average to identify and contain. The report says a data breach in the U.S. costs over twice the global average, coming in at $9.4 million. Organizations recognize that risk continues to grow more dynamic and enduring.

2023 poses an opportunity for organizations to invest in cybersecurity; how they choose to invest will determine the strength of their cybersecurity posture. Incident response solutions drive resilience by improving action and analysis of threats, enabling security teams to increase efficiency and success rates.

Below are six cybersecurity and incident response trends and priorities that can help organizations in 2023.   

Investments in cybersecurity are core to investments in the entire business. 

Organizations tend to limit themselves by focusing on just cyber trends, rather than cybersecurity as a whole. Yes, businesses should invest in strong security protection for computer systems and networks to help eliminate the chances of a cyberattack or breach. However, incident response is also an integral component of improving an organization’s cybersecurity posture, providing organizations with the foundation to leverage cyber data and apply it to their responses. Businesses should think about putting an extra emphasis on building and reinforcing their incident management response going into the new year.  

See also: Cyber Risk and Insurance in 2022

Preventative controls need to be balanced with resilience.

The threat landscape evolves faster than most organizations can anticipate, and most of the time there isn’t just one “cookie cutter” incident response that can cover every cyber incident. Having a solution in place that allows organizations to target their response to a specific incident improves their agility and reduces remediation time. 

Organizations need to defend against multiple attack vectors and address the complexity. 

Changes brought by digital transformation and remote work are increasing ecosystem complexity. As organizations invest in tools that monitor, detect and provide information on their IT environment, they should invest in the processes that leverage this information. Incident response solutions orchestrate and automate responses for all threat-types. Organizations accelerate processes, streamline collaboration and use system data and documents by responding through a "single pane of glass." 

Organizations will be evaluated against performance requirements related to risk. 

Cybersecurity is being recognized as more than an IT issue, as CEOs tie risk management to business value. Incident response tools serve this aim by aligning the activity, information and people involved in each leg of the response. With the entire response memorialized in one place, management conducts reporting and process improvement with greater insight. 

Cybersecurity is becoming the determinant factor in third-party transactions and engagements. 

Existing collaboration tools are not fit for secure collaboration between internal and external stakeholders. However, there are a few incident response solutions that can provide secure access to system data and documents, enabling organizations to pursue transactions and engagements with third parties. Challo by CafeX is one of these platforms. The main benefit of using a third-party-friendly solution is the speed in which you are able to share information and address the cyber incident instead of having barriers block communication or access to documents. 

Automation is the future of cybersecurity and incident response management. 

In any threat situation, organizations face common challenges, including locating incident response plans, communicating roles and tasks to response teams and monitoring actions during and after the threat. Often, information and actions are siloed, which can slow response times and hamper recovery. And in the case of ransomware, plans may be inaccessible and communication systems knocked offline, prohibiting an effective response. Organizations can rely on solutions that can automate an incident response protocol to help eliminate barriers.

Speed is everything. More specifically, as digital technology and software continue to transform traditional business sectors, the speed of innovation is everything. Those companies that can innovate quickly will thrive. Innovation comes through iterative experiments, large data sets and insights. 

Artificial intelligence and machine learning (AI/ML) promise to automate much of the process to find those insights. However, programming AI and getting it right is a tricky business. If it were easy, everyone would be doing it.

Projections show a future market of $500 billion for AI applications, yet there are only 300,000 developers available. There is a big talent gap across all the required skillsets.

There are two core challenges here: 1) how to get to market with innovation quickly? 2) how to drive powerful AI/ML into those innovative applications and at scale?

Low-Code

Our answer to solve these challenges is to combine the speed of low-code application development with the scalability and power of the leading AI/ML engines and large data sets. The low-code approach to development breaks applications into distinct "blocks" within a visual interface, enabling a Lego-like assembly of drag-and-drop components to form a flow, which can then be deployed as a complete application. Almost all low-code development platforms will tread the common ground of basic RPA (robotic process automation), where documents or customer information flows from an input form into a portal and then a customer data platform (e.g. Salesforce). Low-code is common among marketing team applications that manage tracking segments and advertising campaigns. 

Our evolution to the low-code platform brings the full power and potential of the leading AI engines and IoT platforms. With these smart engines incorporated directly into the low-code platform, complex AI-enhanced and IoT-connected applications can now be built much more quickly. Basically, the speed of low-code + the power of AI = enhanced speed of innovation.

The Smart Applications

As these AI applications become easier to develop and deploy, we are witnessing an explosion of use cases across industries. License plate readers are deployed to provide an almost touchless experience at the gas pumps. Drivers can now “just drive out” of parking garages because their license plates have been read at entry and exit, and fees are automatically calculated. License plate readers can automate any drive-through experience, where each coffee/food/pharmacy order is made on the app ahead of time and fulfilled orders are lined up to be handed out according to the queued cars.

Image recognition is becoming smarter at an astonishing rate: What was only primitive car counting and  people counting two years ago, is now full 3D capture of streets, all the objects and moving vehicles while the car and camera are in motion. Each object is being recognized and rendered as a standalone 3D object in near real time. Our team has taken this technology to recognize weapons (rifles, guns, knives) with a high-90s percentage of accuracy and is offering it to schools. We can do this because the image recognition engine is now advanced and compact enough to run on a small edge computer on-site.

See also: SAAS 3.0: Smarter, Faster, Better

Low-Code Brings the Speed and the Smart Together

These AI capabilities are complex, involving terabytes of data to train and deploy. As the pace of innovation accelerates, the application development becomes the actual bottleneck, not the core intelligence of the AI or IoT engine within the app. Low-code gives a promising solution to this bottleneck: as new data sets become available, or new engines evolve, and as new deployment scenarios emerge that rely on legacy or complex data environments (e.g., the gas pump scenario has to go all the way to legacy solenoid switches to physically unlock the pump), Low-code will likely be the only development method that will be able to keep pace.

Talking with people in the wake of the International Insurance Society's Global Insurance Forum three weeks ago, I've heard a note I haven't heard much lately: optimism.

In the face of the formidable challenges we see in the news daily, there is a sense among speakers and attendees that the industry has risen to the occasion in handling massive claims and now has an opportunity to play a leadership role on even seemingly intractable problems such as climate change.

Josh Landau, president of the IIS, told me that, "the climate side of ESG [environmental, social and governance issues] is an area where the industry can have an outsized impact in terms of what the industry is underwriting or not underwriting and where they're investing and not investing. The industry also has an opportunity to help shape behaviors that promote social equality and inclusion and equity."

Ken Mungan, chairman of Milliman, who spoke at the GIF, said bluntly: "Humanity will solve the challenge of climate change. It's just a question of how long it will take and how much pain we will have in the process." He then offered what I thought was a remarkably clever idea for how the insurance industry can do even more than it is already. 

The problem he addressed is that insurers are only allowed to invest in securities with an investment-grade rating, and many of the efforts to reduce climate change and mitigate its effects don't carry such a rating. They can't. They're based on such new technology, in many cases, that many are bound to fail. But what if investments in climate-related initiatives could be pooled and then split into tranches? Equity investors and, in many cases, government funding could take the riskiest tranches, while insurers could invest in debt that, based on the pooling, could easily carry an investment grade.  

Climate initiatives would still require an awful lot of funding from sources outside of insurance companies, of course, but the industry would be able to put a lot of weight behind environmental efforts while earning good, safe returns that will protect policyholders.

Thierry Leger, group chief underwriting officer at Swiss Re, said the industry can already hold its head high because of how it's handled massive claims in recent years because of the global pandemic, disruptions in supply chains, the Russian invasion of Ukraine and other geopolitical crises, etc. 

"The insurance and reinsurance industry has kept its promises to help societies be more resilient," he told me as part of the video interviews I conducted following the GIF, which we'll share as soon as the edited series is available. "We have paid billions to help companies, communities, people and families to get back on track. And we're all very committed to this course."

I encourage you to check out the recordings of the sessions from the Global Insurance Forum -- they all offer meaty thinking from some of the most senior executives in the industry, and you'll surely find two or three that will grab your attention. And I certainly learned a lot hosting the interview series based on the thinking at the conference. I suspect you will, too. 

Now, the industry obviously still faces many challenges. Josh said that, in particular, he expects insurers will continue to have to wrestle with inflation over the next year, along with the related labor shortages. He added that "the geopolitical issues we were talking about at the beginning of the year, as we picked the Great Reset theme for the GIF, have not only not abated but have become more volatile."

But let's stick with the theme of optimism at least for a bit. It's nice to have a break from the doom and gloom, and there will be plenty of time to focus on inflation, geopolitics and whatever else this crazy world decides to throw at us.

Cheers,

Paul 

To provide a superior customer experience, insurance companies must thoroughly understand their customers. Where are they in their lives? What are their concerns and expectations? What motivates them, and what are their immediate needs? Answering these and similar questions enables insurers to better connect with their customers.

The challenge is that no two customers are the same. For example, the insurance needs of a 25-year-old state worker living in an apartment outside Albany are vastly different than those of a 62-year-old retired CEO in Tampa who owns two homes and a new boat. Insurance products best-suited for the state worker in New York likely wouldn’t be appropriate for the affluent retiree in Florida.

It would be impossible for insurers with thousands or tens of thousands of customers to fully understand each of them individually. However, by categorizing customers based on personal and financial characteristics, current and future needs and long-term goals, insurers can develop personalized products and services that will grow revenue. Accurately segmenting customers requires that insurers use data from multiple sources.

One of the big values of segmentation is that it helps insurers identify their most profitable customers. Contrary to traditional thinking, this is not often the customer generating the greatest revenue; it can be some smaller-revenue customers that have low costs in servicing them. Segmentation allows insurers to optimize spending and more efficiently invest resources such as support time, product development time and special offers.

Further, effective customer segmentation positions insurers for up-sell and cross-sell opportunities, if analytics uncover buying trends or ownership patterns across different segments. “Analytics tools spotlight the highest-value clients and high-potential leads,” McKinsey writes in a new report.

Though understanding their customers pays off for insurers in any environment, this knowledge is particularly critical today as inflation drives up prices and puts pressure on margins. Data is the key to gaining actionable insights into your customers.

Below are three specific data sources insurers can use to effectively gain insights into, and segment, their customers:

Internal customer data

The low-hanging fruit for starting customer segmentation is using internal customer data. Insurers have a wealth of internal information for customer segmentation on hand in the form of sales data. How customers have spent their money with you provides many clues about what motivates an individual or customer segment to make a purchase. This information provides the basis for insurers to anticipate customer needs and tailor products and services to meet those needs.

Applying analytics to sales data allows insurers to segment customers by the types of products and services they’ve purchased, what time of year they’re most likely to be looking for insurance, how much they typically spend and other transaction-based information. 

Third parties

Insurers can purchase consumer data from aggregators such as Acxiom and Experian that contains vast amounts of information for segmentation. They can further append that data to existing customer records and learn more about that customer. This is important because an insurer would want to append the relevant data to the correct client. Typically, the aggregator could do that by one or several data tributes the insurer would have for their clients. For example, one aggregator does that by matching the customer’s street address to the address that they have. Other aggregators use other methodologies of appending.

Through aggregators and direct sources, insurers can access demographic data, identity data, personal background information, lifestyle data, individual health records and prescription histories, personal financial data, credit history, shopping history, preferred mode of payment, property and vehicle information, driver records, data on property risks (flooding, wildfires, etc.) and weather data (historical, current and forecasted).

Third-party data is useful to insurers both for customer segmentation efforts and to improve efficiency by enabling data pre-fill, which streamlines the application process and enhances the customer experience.

Customer-interaction data

Insurers can learn much about their customers – from the customers themselves – simply by capturing and analyzing data from support interactions. When customers call or exchange digital messages with a support agent, they are communicating volumes (through their words and tone) about their current needs, expectations and state of mind.

Data from customer interactions can be used by insurers to segment customers by interests and areas of concern. For example, insurers can segment people who are asking about disaster coverage. Should certain questions come up repeatedly, it tells insurers what is top-of-mind for many customers. This information can lead to revenue opportunities or identify potential problems that must be addressed, such as customers in specific locations not receiving renewal notifications until their policies expired.

A cloud-based platform that offers customers multiple channels for communicating with support personnel makes it even easier for insurers to gather valuable data that can be used for segmentation. And customer support technology with artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) can supply even more data from customer interactions by conducting sentiment analysis. Not only do these intelligent tools assist support workers in real time by feeding them appropriate responses to customers who may be distraught or angry, it also gives insurers insights into a customer’s current emotional state. This can provide an opportunity for the insurer to reach out to the customer.

Conclusion

“The exploding volume of data available to insurance carriers is giving rise to new business models, revenue streams and enormous opportunities to increase value,” McKinsey writes in its report. “As first movers among insurers create new business models and seek to harness the potential of their data, those that wait will be at a significant competitive disadvantage.”

Segmentation increases the value of customer data by giving it context, making it easier for insurers to understand and meet the needs of existing and potential customers. Combining third-party, internal sales and customer interaction data from your support center will help insurers better serve their customers and provide highly targeted products and services that will sell.

Today's managing general agencies (MGAs) and managing general underwriters (MGUs) look vastly different than they did 10 years ago. The insurtech boom brought new tech capabilities to the space, including digital and analytics platforms, that have evolved the services MGAs/MGUs are able to offer. As a result, new business models have also emerged. Suffice it to say that there is no cookie-cutter way to establish an MGA these days.

SMA has spent the past decade tracking over 100 startup MGA/MGU entities, in addition to observing the developments at incumbent MGAs that are taking their offerings to the next level amid increased competition. New SMA research uncovers the six insurtech MGA business models that new entrants are pursuing today. Each model's focus ranges from MGAs that deploy line- or segment-focused businesses to serve niche markets, such as cyber or flood specialists, to MGAs designed to enable carriers to offer innovative products through digital channels.

In addition to the insurtech models, SMA's market analysis also sheds light on incumbent and traditional MGAs that have served the market for many years. Faced with new challenges, including changing customer expectations and increased competition, incumbent MGAs realize that relationships are no longer enough to stand out in a field of rivals.

See also: Best of Both: Bundling Parametric, Indemnity

These models illustrate the evolution of MGAs in recent years, and SMA expects further developments to unfold as transformation continues in the sector, including:

• Growth in MGA premiums,
• Continuation of new MGA launches,
• Increased M&A activity among MGAs, brokers and carriers
• Expansion into new product lines/segments.

What's clear is that MGAs bring tremendous value to insurance transactions, and new entrants are elevating services by driving innovation, conceptualizing new products and creating more and more specialization. The variety in MGA business models emerging across the segment will help foster a healthy and vibrant market that will continue for years to come.

One of the big themes I saw at InsureTech Connect in Las Vegas in early September was: Agents and brokers rule. 

While many of the scores of exhibitors and sessions still focused on making carriers smarter and more efficient, seemingly as many dwelt on how to make the lives of agents and brokers easier, more fulfilling and more profitable. That trend will play out over months and years, but it already shows up in two pieces I'd like to highlight this month.

One is the interview with Stephen Crewdson, senior director of global business intelligence, insurance, at J.D. Power. Its latest survey of agents and brokers finds improvement in satisfaction with how carriers are treating them, especially in commercial lines. The survey also offers suggestions (hint, hint to you carriers out there) about how to continue to make agents and brokers happier and more effective, especially for those who aren't in what J.D. Power finds to be the sweet spot for effectiveness -- those with two to 10 years of tenure with their current employer. Making technology easier to use ranks high on the list. Training on business development does, too.

The second is the article, "Sellers Need More Than CRM." You've heard from the author, Yamini Bhat, before, in an interview I did with her for the May newsletter on how agents can use an AI-based assistant to organize their calendars and their tasks to be radically more productive. In the recent piece, she expands on that interview to explain how to move beyond the limitations of today's CRM systems. The piece is worth a look.

Cheers,

Paul

P.S. Here are the six articles I'd like to highlight this month for agents and brokers:

SELLERS NEED MORE THAN CRM

Complementing a customer relationship management system with AI/ML-based solutions can harness the power of data within an organization.

IS YOUR AGENCY READY FOR AUTOMATION?

With the 21st-century shopping experience becoming a "buy now with one click" affair, consumers expect this across the board, no matter the industry.

A ROAD MAP TO INSURTECH DISTRIBUTION

When it comes to insurance, customers trust people more than tech. Customers want a credible human partner throughout the buying process.

HERE'S HOW TO CLOSE THE PROTECTION GAP

Shifting distribution tactics, focusing on more personalized coverage, designing new products and partnering across industry lines can all narrow the gap. 

In the increasingly complex world of risk and insurance, most risk managers are well aware of the number of potential risks that lie behind their brokers’ doors that can have major client-side impact. 

A big concern is the efficiencies (or lack thereof) their agencies have achieved on three mission-critical fronts: policy management and maintenance; risk placement; and risk advisory services. 

Why should the broker’s operational inefficiencies be a concern to client-side risk management teams? Well, a top priority is to keep broker partners ahead of the risk curve and ensure their policies at renewal adequately cover the business today – and tomorrow. Operational risk measurement and management has a major impact on insurers and their brokers’ financial risk. Reducing information asymmetry due to enhanced data and allowing better predictability is key to monitoring and managing operational risk. 

It’s increasingly challenging for brokers to deliver on this promise of risk mitigation. One solution is adopting smart technologies that streamline processes and make them more efficient. By digitizing repetitive work that is prone to errors and omissions, technologies enable broker organizations to become more humane, resilient to change and profitable. 

By 2025, according to the World Economic Forum, the amount of work done by machines will jump to more than 50%, most of which would be replacing repetitive, boring and low-quality manual work. AI technologies including chatbots, cognitive automation and robotics provide a streamlined, automated and quick insurance experience for its customers, and a highly cost-efficient process to the insurers. 

Insurance agencies have an opportunity to get ahead of this trend with technologies – artificial intelligence, machine learning and process automation – that will vastly improve how well they service client accounts. When this occurs, ultimately, everyone wins.

In Search of a Fix

Policy checks and risk placement at renewal remain big stumbling blocks for brokers. While largely manual solutions combining AI and machine learning are starting to make a dent in improving this process, there’s substantial room for improvement.

The numbers speak to the complexities.

Take a typical mid-market policy of 300 pages, with 3,000 or more line items on various schedules. It all needs to be checked for accuracy and variances with supporting documentation such as binding documents, endorsements and exclusions. The initial check – often outsourced – takes about 45 minutes, but then it comes back to the manager who spends another 15 minutes or more looking for variances and validating the policy in a side-by-side comparison. 

Each renewal (and new business) policy needs to go out to different carriers for quotes that are competitively structured and priced, comprehensive and right for the client’s needs. Comparing those multiple quotes to determine carriers that will be most responsive to each client’s risk success stories is painstaking, often requiring numerous hours for each client account. 

Multiply those policy check and quote comparison activities by the hundreds of policies handled by a mid-sized broker over the course of a year, and the high cost, frequent error drawbacks of the manual system become obvious. 

COVID-19 protocols disrupted these and other processes. That disruption, along with escalating turnover among experienced insurance personnel and outsourcing resources that became more restricted, has hamstrung agency operations. Those challenges have led to significant backlogs that delayed policy deliveries by months and created rising E&O risks – and claims – for brokers.

The risks of continuing with inefficient solutions are mounting. Insurers like Lloyd’s of London and some regulators in the U.S. are clamping down on the delays in final delivery of policies. Financial penalties make good incentives for improving the process. Even better than simply avoiding punitive measures, the cost savings for a broker are substantial if an automated, digitized process is adopted.

See also: Why Are We Still Talking About Digital Transformation?

Toward a High-Tech/High-Touch Experience

Solutions are available that address brokers’ administrative risks from within – in a way that focuses on the customer/risk manager experience and leads to vastly improved alignment between them. The stakes are such that investing in these advanced solutions is no longer optional. 

By harnessing state-of-the-art technology – artificial intelligence, machine learning, natural language processing (NLP) and cloud – the broker is better-equipped to extract information from unstructured data. Among the benefits:

• A faster and more accurate policy-checking process. Enhanced analytics of policy limits, deductibles, exclusions and demographic information means potential errors and omissions are identified and corrected with speed and accuracy.
• Improved review of carrier quotes. Multiple quotes and important features (like premium pricing and limits) can be efficiently compared, and the analytics of their variations used to guide recommendations to best meet the client’s needs. No longer will brokers be constrained by the labor-intensive manual process of collecting and analyzing the quote data.
• Claims automation. AI-driven touchless claims make claims management more efficient, accurate and, ultimately, faster.

In the end, as more agencies move to adopt advanced capabilities, the trend will work to the risk manager’s benefit. Smarter and more efficient broker operations will mitigate the hidden risk to clients of high-volume, rushed, rote tasks that can lead to oversights and a tenuous work product. 

Better yet, when the power of technology is leveraged by brokers, it frees them to provide an improved, high-touch, consultative customer experience. Better use of data improves the broker’s insights into the client’s seen and unseen risk challenges. Those advances foster the kind of relationship that keeps risk management teams ahead of the risk curve, whatever circumstances their businesses face. 

It’s the kind of risk mitigation everyone can understand.