Tag Archives: ai

How AI Transforms Risk Engineering

In a year marred by crisis and uncertainty, the mature property and casualty (P&C) insurance industry has seen its workload increase in both volume and complexity. According to the Insurance Information Institute, insured losses from natural catastrophes in 2019 totaled $71 billion. That number is only expected to rise in 2020 with the onslaught of hurricanes and wildfires hammering the U.S.

Insurers must contend with a rapidly changing risk landscape. Falling interest rates, climate change, man-made risks and civil unrest are causing unprecedented destruction and business interruption. This is exacerbated by the COVID-19 pandemic, cyber security threats and global terrorism, causing the number of claims to skyrocket.

Traditional methods of risk analysis are slow and expensive. Risk engineers spend considerable time performing repetitive assessment and administrative tasks that do not add value to clients.

One saving grace is the global movement toward digital transformation and automation, including the adoption of artificial intelligence (AI). Changing client expectations have propelled organizations to rethink age-old processes. 

An artificial intelligence study by PwC said, “AI could contribute to the global economy by 2030, more than the current output of China and India combined.” The same report estimated $6.6 trillion would likely come from increased productivity alone.

See also: Stop Being Scared of Artificial Intelligence

How do you know if you’re ready to embrace AI, and what are some of the areas it could improve within risk engineering? Below are three points to consider:

The easiest way to get started, is to contemplate your market in five years’ time and consider what capabilities you will need to compete – McKinsey

1) Align Business and AI Goals.

A certain appetite and readiness for change is required on the part of the C-suite and by the risk engineers within your workforce. A real pain point must be met, and the implementation of AI must align with the overarching business goals of your organization. For risk engineering, the time is ripe for AI disruption. According to McKinsey, “Efficiency improvement is an imperative. The industry’s current trajectory is inefficient and unsustainable, creating the conditions for disruption. This would involve digital technologies, automation and data and analytics to not only reduce error-prone manual processes but also enable an agile way of working.” 

If account engineers and risk engineering consultants spent more of their time on risk verification and selection rather than aggregation and analysis, this would help underwriters speed up the time to assess and quote on a new bid and ultimately increase the chances of winning business. The first response to a submission wins over 50% of the time. 

Still, the question remains, whether your organization wants to be an early adopter, fast follower or follower. Will the AI solution you create in-house or via a third-party vendor disrupt the sector and provide you with a competitive edge?

2) Examine Internal Talent. Find Your AI Champions.

Another critical factor is talent. Are there champions within your company willing to take on the added time it requires to inform the user journey and customizations, perhaps even label the initial data and ultimately execute on the AI opportunity at hand? It is vital that there is a top-down and, equally, a bottom-up culture of adoption for AI implementation to succeed.

A global digital practice survey revealed that insurance companies are attracting less digital talent than other financial services companies such as fintech and asset management. In a recent survey, 80% of millennials said they have limited knowledge of the insurance industry, and 44% said careers in insurance sound “boring.” Orbiseed’s recent interview with a veteran risk engineer also revealed that the majority of senior risk engineers are close to retirement and may resist employing new technologies. “Indeed, perception can shape reality, and the current reality is that the insurance industry isn’t viewed as relevant or exciting to up-and-coming digitally savvy workers,” the report concluded. 

3) Partner With AI Vendors You Trust to Scale Quickly.

An AI firm should know your industry inside and out, have secure networks to help protect your data and enable you to scale your AI program fast. You will also need to consider whether to select AI integrations over ground-up builds. An integration will vastly reduce the time it takes to produce a working model for your business. A good software integration will also layer into the existing system you have rather than force your employees to learn an entirely new system.

See also: 3 Tips for Increasing Customer Engagement

Next Steps Toward AI Transformation in Your Organization

AI is fundamentally changing the way business is done in 2020. For mature industries that still rely on manual, labor-intensive processes, adopting new technology can make a measurable difference in efficiency and deliver significant competitive advantages.

Risk engineering seeks to manage risk: Adopting AI practices early will ensure that your organization hedges against the risk of falling behind the competition. Firms that effectively adopt AI early report significant performance gains compared with competitors, including higher revenues and reduced expenses.

How AI Can Tackle Claims Staffing Gap

Commercial insurance faces a growing claims adjuster staffing gap. On the retirement end, there’s a rising tide of experienced adjusters leaving the profession. According to the Pew Research Center, nearly 10,000 baby boomers retire each day in the U.S., and about 25% of them leave positions in the insurance and financial services sector. Seasoned adjusters leave with a wealth of experience built up over decades, leaving newer adjusters to handle a rising volume of claims.

On the entry side, few adjusters are entering the business to make up for the wave of departures. According to the Hartford’s 2015 Millennial Leadership Survey, only 4% of millennials are interested in entering insurance, compared with 36% interested in education and 31% interested in healthcare. The gap is especially acute in commercial insurance, where few college graduates have been exposed to workers’ comp, business continuity and other types of claims outside of personal lines.

There’s little indication that COVID-19 has changed this dynamic. While job listings for “insurance adjuster” have risen, according to ZipRecruiter, insurance caseloads also continue to rise, increasing pressure on claims teams facing waves of new types of claims, from COVID-19 workers’ comp to business interruption.

One way that carriers can change the game and attract millennials is by focusing on artificial intelligence (AI), which itself is experiencing a surge of interest. AI is commonly seen as a replacement for people, a way of fully automating jobs to prevent the need to even hire in the first place. But the real value lies in machines and humans working alongside each other, where machines can enhance humans’ natural instincts.

It’s this augmentation role that makes AI an appealing solution to the industry-wide talent gap — across three broad dimensions.

Break out of the mundane

The first way AI can help is by handling aspects of the adjuster’s job that are more routine and thus less appealing. AI techniques like machine learning are optimal for handling an array of calculations, such as estimating reserves needed for a claim or tracking the cost of medical bills that have been paid. Natural language processing can identify relevant comments and insights in a sea of text, reducing the need to parse every document that emerges. Entity resolution detects when multiple providers, attorneys or companies are actually related in some fundamental way, which can significantly cut down time-consuming legwork that can go into sizing up the various players engaged in a claim.

In these examples, AI gives adjusters superpowers that free them to focus on the more nuanced and interesting aspects of claims adjusting. A job description with “acquire AI superpowers” might appeal to millennials more than “study policy footnotes and calculate claim reserves.”

See also: How to Recruit Claims Adjusters

A natural mediator

The second major appeal AI offers is in transforming a potentially adversarial relationship into a more mutually supportive one. The traditional workers’ comp claim can feel like a one-dimensional tug-of-war between adjuster and claimant — where one’s gain feels like the other’s loss. But AI can find win-win breakthroughs by considering a wider range of factors and data.

By identifying doctors with successful track records in a specific injury type, for example, AI can get workers to providers that enable them to recover faster, while also reducing both the workload and cost for the adjuster. Automatically interpreting adjuster and provider notes can detect situations where the worker is confused about the claims process, enabling adjusters to address the confusion before it becomes a deeper source of frustration.

Aligning adjuster and claimant plays to the healing side of claims adjusting — and to a new generation that is increasingly looking for meaning in their work. In the 2019 Rising Medical Solutions survey of over 1,000 claims professionals, 36% of respondents indicated that shifting to an advocacy model with workers would improve the reputation and social image of their organizations. Lifting the social image of the organization and profession will increase the appeal to millennials and Generation Z, both more mission-focused cohorts than older generations.

An expedited ramp-up

The third way AI can attract talent is by helping newcomers optimize their impact within months versus having to invest years in traditional training. AI-generated recommendations can come with explanations that show how they were arrived at. This gives the user confidence in the recommendation. It also provides the user with guidance to accelerate overall mastery of the domain.

Adjusters can generate their own insights and recommendations, looking at the various factors in the claim, then compare those against the AI-generated answers, giving the adjusters a ready-made feedback loop to train themselves over time. Newbies can play this “guessing game” until they get enough right answers to start taking action on real claims. They’ll, of course, need training from experienced colleagues, but this approach can get them up to speed faster.

Claims professionals can set themselves up well for future changes by playing up their familiarity with AI. Insurers can emphasize the AI fluency that adjusters will gain from specific roles. Adjusters can increasingly reference AI strengths on their resumes. Technically inclined claims professionals can shift all the way into business intelligence, machine learning or data engineering tracks, which are among the fastest-growing in the entire economy, according to LinkedIn. Claims operations end up with an increased flow of talent and a strengthened internal mobility program that they can showcase to new candidates.

See also: Transforming the Claims Space

The adjuster recruiting challenge didn’t appear overnight and will take years to overcome. But AI is clearly one way smart firms can accelerate progress and stand out to attract a new generation of insurance professionals.

As first published in PropertyCasualty360.

How AI Powers Customer Contacts

For insurance carriers, customer retention relies on trusted communication between the company and its customers—often by way of representatives like brokers and agents. Developing and maintaining that trust depends heavily on the quality of policyholder communications: knowing and understanding your customers and presenting your brand in such a way that customers feel they know and understand you. While this seems a simple concept, in this era of digital communications it requires—and customers expect—the intimacy of personal interaction distributed through sophisticated and varied media channels and devices. 

The customer communication management (CCM) systems that many insurers employ today are able to create communications to be delivered via the various channels that customers prefer. However, modern CCM systems are capable of even more personalized and relevant interactions. And there’s the problem: Many insurers have been in business long enough to experience evolving generations of communications systems. This has resulted in valuable content and customer communications being stored in various silos throughout the organization—one for marketing, another for billing, another for claims and so on—and often in near-obsolete formats or systems. 

For customer service representatives, claims adjusters, brokers or agents, finding the right template can be problematic and time-consuming. Then, creating an appropriate response with approved content is yet another hurdle. Ideally, every insurer today should have a CCM system that is able to draw on the accumulation of content from customer communications from all internal departments, then assist business users in using that information to create a fitting response. If that is not possible in your organization, a more robust CCM system needs to become a priority for the sake of your staff and to satisfy increasing customer expectations. 

Moving to a better system

If the necessary content and customer information required for a new CCM system are still housed in disparate silos and legacy systems, the question becomes, where do you begin? When considering migrating to a modern CCM system, we recommend starting with a communications assessment to get a realistic idea of the scope of the task you’re undertaking. Consider the volume of the materials you have, where they are located in your organization—again, they may be distributed among several departments and systems—and your priorities. What will your future omni-channel customer experience look like? This understanding will help determine what kind of CCM system best suits your requirements, the plan needed for migrating to a new system and the costs involved.

Traditionally, the only way to migrate legacy content was manually. That meant getting staff to look at the tens of thousands or hundreds of thousands of documents and content messages in your files, then sort them into various piles, labeled as “obsolete,” say, or “excellent explanation,” or “good introductory paragraph,” or “Connecticut doesn’t require this any more.” Depending on the volume of communications and associated content objects in your archives, this process would likely mean a major investment that could include hiring and training many more people and allowing them months or even years to sift through everything. Add to that the potential for human error, and you can see what a painstaking, expensive and fraught task this is. One alternative is to “lift and shift” all content from your old systems into a new one. Unfortunately, this move won’t deliver the change you are looking for as it just recreates the chaos of your old systems in the new platform.

See also: AI Ends Guesswork in Uncertain World

One answer is to apply more advanced technology to review and sort out your existing customer communication files. Modern technologies, such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are increasingly being used to significantly accelerate content migration and optimization processes. To begin with, AI can be a powerful tool to automate the ingestion and metadata tagging of legacy content from various file formats and systems. In addition, AI,  ML and NLP can analyze content and communications to identify outdated, off-brand, duplicate or similar content, as well as inconsistencies or non-compliant branding, reading levels and sentiment. This content can then be optimized prior to importing it into a content hub for use in future communications. Applications with these kinds of built-in AI and ML functions have been known to reduce the time required to modernize and optimize your existing customer files by as much as 99%.

Putting it all together

The aim of this process is to then to house the content in a centralized content management system or “content hub.” This enables your content authors to centrally control content and provide customer-facing teams with access to approved communications regardless of the channel (print or digital). This content hub should include documents like disclosures, policy statements and explanations of benefits, standard customer correspondence templates for claims responses, account servicing and billing to enable servicing teams to build relevant and personalized correspondence. The right hub will also help to automate the application of the required regulatory and compliance language for different states and jurisdictions. The AI-powered content hub would offer nearly all the pieces needed to put together a customized and relevant reply to a customer query via the preferred channel of communication. 

These systems empower your customer-facing teams, ensuring they use the latest, approved content and enabling them to add relevant, personalized messaging based on real-time information. AI embedded within these systems can provide guardrails to ensure communications stay within brand, reading level and sentiment guidelines. This not only helps to protect brand equity, but it also assists the organization overall in driving toward a more consistent, cohesive customer experience across various touchpoints and channels. 

As insurers have expanded customer touchpoints across new digital channels, delivering consistent and relevant communications presents new challenges. Increasingly, both existing and prospective customers expect prompt and appropriate answers to their queries via the channel of their choice, or they may look to your competitors. With a solid communications strategy and a CCM system that supports it, you will find new ways to create, manage and deliver an array of complex insurance communications with consistency and efficiency, which can be a significant factor in winning — and keeping — your customers.

Property Claims: It’s Time for Innovation

The personal and commercial property claims process has traditionally lagged well  behind other segments of P&C insurance in the adoption of technology and innovation. That officially ended in 2020, aided by a global pandemic that changed virtually everything about life and business as we knew it. Understanding the factors behind the historical lack of innovation in property claims provides insights into why and how this segment is suddenly undergoing such rapid transformation.

Auto vs. Property Claims Process Transformation

When compared with the recent impressive rate of change in auto claims, property claims appeared to be a more of a laggard than it really was – but a laggard nonetheless. To put this in perspective, U.S. auto insurance policies, premiums and claims in 2019 were approximately four times larger than property. Further, auto claims are generally more visible and more consequential to the public than property claims. And the auto claims process was broken until about 1990, with the emergence of direct repair programs enabled by internet and database technologies, so the transformation has been that much more obvious and impressive.

Industry Fragmentation

The property claims repair market is characterized by extreme fragmentation, which exceeds that in the auto insurance claims industry. This is due to several factors: 

  • the relatively large number of service providers specializing in distinctly different major damage types, especially managed repair networks, as well as independent contractors, in general
  • the complexity of property claims themselves, which involve the coordination of numerous general and specialty provider types for a given claim 
  • the proliferation of task-specific software solutions, which are generally not integrated with one another
  • the smaller influence of property insurers on the repair process as compared with the influence that auto insurers have (because of less consolidation of property insurers and because they collectively represent only about 33% of repair industry revenue while auto insurers represent almost 90% of collision repair revenue)

A high-level comparison of market fragmentation of third-party auto and property claims repair provider markets provides another important explanation of the emerging transformation in property claims. The collision repair industry has undergone significant consolidation both in terms of the numbers of repair shops and shop ownership – and consolidation continues. Since 1990, the number of U.S. repair locations has fallen roughly 50% to approximately 32,000. Moreover, consolidators have created large multi-location, multi-regional and national MSOs (multi-shop operators) and now control almost 30% of the repair industry revenue. Private equity investments and relatively inexpensive debt have provided the enormous pools of capital required to enable this consolidation.   

See also: Key Advantage in Property Underwriting

Property Claims Ecosystem

In studying the property claims, mitigation and restoration ecosystem, we identified 110 companies with material market share, which we grouped within nine distinct categories:

  • Software applications for:
    • Property estimating
    • Restoration management
    • Claim management platforms
    • Accounting/financial, measurement, documentation, communication and productivity
    • Payment solutions
    • Imaging/aerial inspection
  • Services:
    • Third-party administrators (TPAs)
    • Property claims adjusting and estimating
    • Managed property repair networks

Industry Consolidation

When we researched corporate ownership profiles for these 110 firms, we discovered that 45 – or 39% of them – are funded or controlled by private equity, venture capital or a few strategic investors. While there is some such investor activity in every one of the nine segments, it is most pronounced in managed property repair networks, claims management platforms and imaging/aerial inspection verticals.

These investors are fueling consolidation in these segments in much the same way as they are in the auto claims ecosystem, and will spur greater adoption of cost-effective and process innovation technologies. This is already evidenced by the emergence and adoption of artificial intelligence, computer vision, augmented, virtual and extended reality, machine learning and natural language processing across property claims.


Emerging Property Repair Market Opportunity

The property repair industry is 40% to 50% mature, while we estimate the auto claims industry is approximately 80% mature. This is partially illustrated by direct repair claims penetration of the collision repair industry, which is at or over 50% for carriers with higher market share (and more for some auto carriers) versus less than 10% on average for property repair.

Homeowners property insurance claims and ecosystem software and technologies market, viewed holistically, represent a significant and mostly unaddressed market opportunity. The situation closely parallels the auto insurance claims process and collision repair markets of 1990, which saw technology and economics drive vendor consolidation and carrier adoption of managed national repair programs, which were enabled by automated estimating software development, digital communications, imaging and end-to-end claims workflow tools.

Property Claims Solution Platforms

Property insurance carriers increasingly will be seeking technology-driven end-to-end property claims management solutions featuring;

  • connectivity between all parties from report of loss to remediation to payment and closure
  • hybrid insourced/outsourced carrier claims and repair network management capabilities, including  universal, standardized contractor onboarding, performance metrics, automated skills/needs matching, user reviews and vendor rankings.
  • integration with Guidewire’s claims platform or similar partner ecosystems

Property Claims Technologies

Artificial intelligence (AI), machine learning (ML), robotic process automation (RPA),computer vision (CV), natural language processing (NLP), aerial imagery including drones and digital payments are being aggressively adopted across the P&C insurance claims process, and specifically property.

  • Smart home technology adoption will mitigate and in some cases eliminate claims and losses; Bain Capital predicts that in just five years there will be 50 billion connected devices and a trillion by 2030. According to Statista Market Forecast, the global smart home market was valued at $55.65 billion in 2016 and is projected to reach $174.24 billion by 2025, growing at an annual rate of nearly 14%. While 32% of homes currently have a smart device, that number is expected to reach 52% by 2025.
  • The impact of these technologies to the property claims and restoration industries is already — and will become even more — significant
  • As residential policyholders become more comfortable with self-administered smartphone photo and video inspections of property damage reported directly, insurers will gain more control over the restoration assignment process, which will promote the use of national repair networks (and the claims management software that can manage the end-to-end process)
    • It is estimated that the use of photo inspection services can reduce field claims cost from an average $550 down to between $60 and $90 and the cost of technical inspections from $550 to $300
    • Technical inspections or VAIP (virtual adjusting and inspection programs) will fuse services, including the use of a licensed adjuster. Claims will offer faster cycle times and savings of 35%.
    • Providers of satellite and aerial images, including drones, are gaining in importance in the residential property damage identification, validation, damage assessment and repair estimation process.
    • Satellite and aerial imagery are increasingly being used by the property insurance industry for catastrophe planning and response, including damage evaluation and estimation.

Property insurance carriers now seek to avoid the effort and responsibility of managing restoration contractor selection or oversight but require a complete end-to-end workflow management platform to achieve their goal.

See also: How to Pursue Innovation in a Crisis

The property insurance claims and repair industries continue to move through a multi-segment structural transformation caused by prevailing market conditions, including industry fragmentation, consolidation, investments, revenue and geographic scale, end-to-end technology and software integration, emerging technology adoption and claims process improvement. Companies and investors that recognize the numerous opportunities presented by this transformation and solve for these dynamics are likely to be the future industry leaders.

Where Are All Those Benefits Promised by AI?

Having covered developments in artificial intelligence for going on 35 years, I’ve long been struck by the confusing expectations. Based on what many were saying back in the ’80s, we should all be working for our robot overlords by now. Yet people are also often too cautious: If I could tell my 1986 self that I’m now calling out to my Amazon Echo for random factoids that I just have to know that instant or that I’m dictating text messages to Siri, my earlier self would have called for the men with the butterfly nets.

Sorting through the confusion, I’ve decided that artificial intelligence is a moving target, an aspiration for capabilities that might be possible just over the horizon. Once something becomes reality — even something as mind-boggling as speech recognition — what was AI becomes garden-variety computing.

So, I haven’t been overly surprised in my seven years with ITL to see the insurance industry light up at the prospects for AI and, at the same time, have trouble realizing them.

Two big studies released last week, one led by BCG and the other by Willis Towers Watson, shed light both on how to realize the gains that AI can provide — and on how the industry remains unrealistic.

The study by BCG Gamma, the BCG Henderson Institute and the MIT Sloan Management Review found that only 10% of companies reported significant financial benefits from implementing AI — so figuring out how to realize gains would seem to be in order.

The authors were encouraged by what they see as an increase in interest in AI: Their survey of more than 3,000 executives globally found that 60% have an AI strategy in 2020, up from 40% two years ago. But the authors argue that simply having a strategy — what they call “discovering AI” — only gives a company a 2% chance of significant financial benefits. (“Significant” means a gain of $100 million in revenue or a $100 million reduction in costs for a company with annual revenue of more than $10 billion, and proportionally lesser gains for smaller companies.)

The authors say that even moving into the “building phase” — getting the right data, technology and talent and organizing them within a corporate strategy — only boosts the odds to a 21% chance of success.

Companies can help themselves a lot, the authors say, if they figure out how to iterate with targeted users on solutions that AI might offer — achieving this “scaling stage” lifts to 39% the prospects for significant financial benefits.

The study finds that the final, “organizational learning” phase — “orchestrating the macro and micro interactions between humans and machines” — makes the biggest difference. Getting to that stage gives businesses a 73% chance or reaping big benefits from AI.

The report cites two key factors for companies that hope to move into successively more mature phases:

  • Use as many feedback mechanisms as possible to improve the capability of the AI. There are three possibilities: Humans can provide feedback to the AI; humans can take feedback from the AI; and the AI can teach itself. The report finds that the AI is five times as likely to produce real benefits if all three types of feedback are used than if just one type of feedback is.
  • Be willing to change existing processes to incorporate the AI rather than treat it as a separate animal. In other words, don’t just assume that you’re training the AI; realize that the humans need to be retrained, too. The authors report that companies that changed business processes extensively were five times as likely to succeed as those that didn’t.

Fair enough. All that makes sense.

But the second study I saw last week, from Willis Towers Watson, suggests that we’re going to be overly optimistic about our ability to absorb AI — even if we know we’re going to be overly optimistic.

The study looked at seven ways that insurers intended to use AI and found a consistent pattern: Asked in 2017 about plans for 2019, insurers expected huge gains. Surveyed again in 2019, the insurers hadn’t come close to achieving their goals. Asked about plans for 2021, though, insurers were undaunted; they predicted even bigger improvements than the ones they failed to achieve by 2019.

For instance, asked about using AI to remove bottlenecks in claims, 3% of insurers said they were already there in 2017, but 30% expected to be there by 2019. Actual number? 7% said they hit the goal in 2019. So you’d think insurers would be chastened, and perhaps 10% — maybe 15% — would expect to achieve that goal by 2021, right? Nope. 43% say they’ll get there.

I was reminded of this sort of inability to escape a recursive logical fallacy earlier this month when a fascinating fellow I interviewed 30 years ago up and won the Nobel Prize for Physics because of work he’d done on the mathematics of black holes. On the side, Roger Penrose shared ideas with Dutch artist M.C. Escher (including about what are known as the Penrose steps), and following our talk I picked up what turned out to be a profound book, “Godel, Escher, Bach,” by Douglas Hofstadter. Among the many insights was what is known as Hofstadter’s law, which the author posited about any task of sufficient complexity: “It always takes longer than you expect, even when you take into account Hofstadter’s Law.”

While he was largely referring to programming, I embraced the idea about my various writing projects and posited what I called Carroll’s Corollary, which says: “Writing always takes longer than you expect, even when you take into account Carroll’s Corollary.”

That’s all a long way of saying that, while I’d love to be able to give you clear guidance on how to be more realistic about how quickly you’ll be able to adopt AI, I realize this is a hard problem. I mean, I have trouble just figuring out how long it’ll take me to write Six Things each week.

The only solution I know is calibration. If you’re one of those companies in the Willis Towers Watson study, and you’re making projections for a couple of years out, don’t just start with a blank sheet of paper. Go back and look at the projections you’ve made previously and see how they’ve panned out. If, like most, you’ve been overly optimistic, look into why. Then be specific about the assumptions that have to hold true for you to be right this time around, and see if you aren’t making the same mistakes you made last time.

You’ll likely still be too optimistic, at least according to Hofstadter’s Law, but you’ll get better at predictions over time. Eventually — who knows? — maybe AI will solve the problem for you.

Stay safe.


P.S. Here are the six articles I’d like to highlight from the past week:

AI Ends Guesswork in an Uncertain World

AI is highly sensitive to new data and tends to react immediately, creating a dynamically updated vision of the future.

How to Pursue Innovation in a Crisis

It’s common in crises to pause or cut investments, including in innovation, yet this is an incredible time to innovate. Here are five tips.

Speeding Innovation in Life Insurance

Life insurers have been flirting with a new digital paradigm in underwriting, health protection and remote claims. Perhaps now is the time.

Private Equity Drives Change at Agencies

Independent agencies haven’t fundamentally changed the way they do business in 100 years but now must greatly up their game or sell.

How Risk Management Differs From Insurance

If you call yourself a risk manager when you are really only selling insurance, are you representing yourself truthfully?

A Future-Proof Operating Model

Insurers need an operational model with adequate agility to follow market fluctuations. It’s time to outsource all non-core activities.