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The Key to Agency Management Systems

Digital capabilities are more important than ever across all parts of the insurance ecosystem. That includes the world of principals and producers, who rely on agency management systems (AMS) to serve as a workbench for their main activities: selling insurance, managing clients and managing themselves. 

While some vendors have slowly expanded the capabilities of their core offering, others have integrated with larger, agency-focused suites of stand-alone software solutions that offer a broader range of capabilities to speed up transactions, automate processes and create a better overall experience for agents. Insurance carriers that consider agents’ ease of doing business one of their differentiators will likely have to integrate with these platforms at a minimum. 

Insurers may use AMS solutions for internal MGAs or agencies, but the primary overall users of these platforms are independent insurance agents. Modern AMS platforms are designed with these agencies in mind. Originally, these solutions began as enterprise resource planning platforms, but now they act as day-to-day workbenches. Carriers that want to improve overall agent relationships, which ultimately leads to better policyholder relationships and retention, need to consider how their products, processes and policy information will be part of these AMS ecosystems.

General Functionality of an AMS

While smaller agencies usually turn to an AMS that offers capabilities like advanced lead management and carrier connectivity, larger agencies are more likely to take a component-based approach and select a software suite with a broader range of solutions. These differences are not unlike the differences in core systems approaches taken by larger and smaller insurers. 

The difference in AMS purchasing approaches can cause some confusion about what functionality an AMS should offer, but there are three general categories of functionality that any solution, whether stand-alone or suite component, should cover: selling insurance, servicing customers and managing the agency itself.

AMS platforms help with selling insurance policies by tracking prospects, managing leads, understanding appetite, automating communications and generating quotes, among other capabilities. They can help service customers through capabilities like serving as a central record of customer activity (e.g., changes in policy, billing, claims, etc.). AMS solutions can also help improve operational efficiency by facilitating agent workflow, tracking calendars and deadlines, managing alerts or tracking individual and aggregate agent activity. 

Selling Insurance

Sales capabilities in an AMS include functions like managing leads, generating quotes, reporting underwriting appetite, automating emails, creating and storing templates for communications, managing the pipeline, marketing integration and integration capabilities (or APIs) with insurers’ portals. 

Quoting and underwriting appetite has become an area of focus for agents because omni-channel approaches are becoming the norm. Agents are also relying on AMS platforms to manage mobile messaging and social media posts, not just email and phone. In some cases, this might require insurers pre-approving templates or implementing software that can monitor compliance through a direct integration with the AMS or through workflow steps. AMS platforms are also commonly offering “next-best action” recommendations built on analysis of touchpoints and customer responses to marketing initiatives. 

Servicing Customers

When it comes to servicing insurance customers, AMS platforms typically offer download from/upload to insurers, execution and recording of endorsements, document management, ACORD forms, policy information updates, contact information maintenance, the storage of billing information, bill pay, monitoring of claims and record of payments. The platforms can also automatically alert agents when there are any service concerns that need their attention. 

Agents prefer platforms that make it easy to conduct all of their business through one interface, so allowing integration between agent portals and AMS platforms is a wise option for insurers. Agents and insurers alike are focused on the customer experience, meaning that AMS platforms should keep track of all policyholder interactions across the insurance life cycle. 

Ease of upload to insurance carrier systems can also be a differentiator; a recent Novarica study showed that 38% of young agents’ AMS platforms did not include upload ability, but they would like to have that capability. Consistent data across insurers and agents can improve customer service for inquiries as simple as updating contact information to more complex interactions like filing a claim.

See also: How Carrier Tech Drives Agency Change

Managing Agents

Agency management is a basic tenet of an AMS, and each platform should include some form of workflow management; monitoring of compliance, credentials and license; commissions tracking; general ledger and accounting; dashboards that show agent performance; data and analytics functionality; and sales and technology training. As AMS platforms have evolved to keep up with platform and industry trends, so have these capabilities. 

Regulation is top of mind for most insurers, and AMS solutions can help maintain compliance through monitoring and managing agent credentials and licensure. An AMS can produce reports and send alerts to ensure that agents are staying up to date with their licenses. AMS platforms can also help agents with their workflow management, including laying out process steps, milestones, dependencies and approvals. 

These components are becoming increasingly sophisticated; insurers looking to simplify agents’ day-to-day work should be clear about which steps require touchpoints with the carrier so the AMS can be configured properly. Some AMS platforms offer analytics capabilities to help improve sales and retention for agents and their overall agencies, routing particular opportunities to the agent who is best equipped for that specific lead. 

The marketplace for AMS platforms is broad, and agencies have plenty of options to choose from. Insurers therefore cannot routinely predict which AMS platforms the majority of their independent agents are using. Instead, insurers have to stand ready to be flexible — with data APIs, integration with connectivity platforms, easy download capabilities, readily available digital assets and, above all, a willingness to listen to their agents and understand what kinds of integrations would be most valuable and helpful to them.

Despite COVID, Tech Investment Continues

Insurers will continue to experiment with emerging technology in 2021, despite the challenges of 2020. When the COVID-19 pandemic hit, many insurers paused their 2020 innovation plans, emphasizing digital workflows and cost control at the expense of emerging technology pilots. Heading into 2021, technology priorities for many insurers, especially those in the property/casualty space, are similar to those of 2019.

The U.S. is still in the midst of the pandemic, and some insurers are anticipating lower premium revenues for the coming year. In spite of this, insurers are investing in technologies like artificial intelligence and big data, though some are narrowing the scope of their innovation efforts for the coming year.  

Understanding Emerging Technology Today

Insurers typically take a few main approaches to emerging technologies. Early adopters experiment with the technology, typically via a limited pilot. If the technology creates value, it’s moved into wider production. Insurers that have taken a “wait-and-see” approach may launch pilots of their own.

Novarica’s insights on insurers’ plans for emerging technology are drawn from our annual Research Council study, where CIOs from more than 100 insurers indicate their plans for new technologies in the coming year.

No insurer can test-drive every leading-edge technology at once, and every insurer’s priority is a result of its overall strategy and immediate pressures. Still, at a high level, several industry-wide trends are apparent:

There is big growth in RPA; chatbots continue to expand. More than half of all insurers have now deployed robotic process automation (RPA), compared with less than a quarter in 2018. Chatbots are less widely deployed but on a similar trajectory: from one in 10 in 2018 to one in four today.

AI and big data continue to receive significant investment. These technologies take time to mature, but it’s clear insurers believe in the value they can provide. More than one in five insurers have current or planned pilot programs in these areas for 2021.

Half of insurers have low-/no-code capabilities or pilots. These types of platforms are relatively new but have achieved substantial penetration in a short time. Early signs indicate they could become a durable tool for facilitating better collaboration between IT and business experts.

Despite continued tech investment, 2021 might be a more difficult year for innovation. Insurers’ technology priorities have generally reverted to the mean — more so for property/casualty than for life/annuity insurers — and technology budgets for 2021 are within historical norms. Still, some insurers are paring down pilot activity in less proven technologies, like wearables, to maintain their focus on areas like AI and big data. Technologies with substantial up-front costs, like telematics, may be harder to kick off in 2021. 

See also: Technology and the Agent of the Future

How Emerging Technology Grows

Emerging technologies have widely varying rates of experimentation, deployment and growth within the insurance sector. Their growth rates boil down to a few key related factors:

  • How easily the technology is understood.
  • How readily it can be deployed and integrated with existing processes.
  • How clearly the value it creates can be measured and communicated.

At one end of the spectrum are technologies like RPA and chatbots. These technologies create clear value, are readily added to existing processes and are relatively easy to deploy. As a result, insurers have adopted them rapidly.

Artificial intelligence and big data technologies require longer learning periods; sometimes, they require business processes to be completely reengineered. The technologies create value for insurers but have grown more slowly because they take time to understand and integrate.

Drones, the Internet of Things (IoT) and telematics can create new kinds of insurance products or collect new kinds of information. These can also create value, but their growth remains slow because developing these technologies may require orchestration across several functional areas, and they can be costly to ramp up.

On the far end of the spectrum are technologies like augmented and virtual reality, blockchain, smart assistants and wearables. Most of these technologies don’t yet have established use cases that demonstrate clear value, so it remains to be seen whether they will be adopted more widely.

Using Emerging Technology

One key insight from Novarica’s study is that technologies that integrate readily to existing processes can grow more rapidly than technologies that require new workflows to fully use. This observation comes with a few caveats for both insurers and technology vendors.

Insurers sometimes fall into the trap of “repaving the cowpath” — they adopt new technologies but integrate them into their existing (inefficient) business processes. Doing so means they can’t get maximum value from their investment. Ironically, it’s usually the shortcomings of legacy technology that have made these processes cumbersome in the first place.

It’s easy to understand the value that technology creates when it integrates with an existing process and can be measured with the same key performance indicators (KPIs). It’s much harder to create a new process enabled by new capabilities, train employees to execute it and demonstrate that the new way is better than the old way. Yet getting the most out of emerging technologies often requires rethinking how business might be done.

See also: 2021’s Key Technology Trends

For their part, vendors should focus on the value their products create and the problems they solve, aligning them to insurer needs. It’s not enough to use a new technology for its own sake, and using new tools sub-optimally may make them seem less effective. Vendors should coach their insurer clients through best practices and help them understand how their tools can ease, change or make obsolete existing processes.

At its core, insurance is a simple industry focused on connecting those exposed to risk to capital that can defray potential losses. At the center of that value chain are insurers, that continue to explore new technologies to better understand their risks, sell more and operate more efficiently. Even in uncertain times, insurers are innovating.

AI and Discrimination in Insurance

This past summer, a group of African-American YouTubers filed a putative class action against YouTube and its parent, Alphabet. The suit alleges that YouTube’s AI algorithms have been applying “Restricted Mode” to videos posted by people of color, regardless of whether those videos actually featured elements YouTube restricts, such as profanity, drug use, violence, sexual assault or details about events resulting in death. The lawsuit alleges that this labeling has occurred through targeting video keywords like “Black Lives Matter,” “BLM,” “racial profiling,” “police shooting” or “KKK.” YouTube says its algorithms do not identify the race of the poster.

Whether the allegations are true or not, the case illustrates AI’s potential for inadvertent discrimination. It is easy to see how an algorithm could learn to use variables seemingly unrelated to race, sex, religion or another protected class to predict the outcomes it was designed to target. In the YouTube example, we could imagine the algorithm noting a link between the mentioned keywords and videos depicting violence, thus adding the keywords to factors it weighs when deciding whether Restricted Mode should be applied to a given video. The algorithm is simply programmed to restrict sequences containing violence, but in such a situation it could end up illegally restricting videos posted by African-American activists that depict neither.

In response to such potential pitfalls, the NAIC this past August issued a set of principles regarding AI. The set includes principles about transparency, accountability, compliance, fairness and ethics. The only way to ensure compliance, fairness and that ethical standards are maintained is for AI actors to be accountable for the AI they use and create — and the only way for these actors to properly monitor their AI tools is by ensuring transparency.

As Novarica’s most recent joint report with the law firm Locke Lord on insurance technology and regulatory compliance notes, all states follow some version of the NAIC’s Unfair Trade Practice Act (“Model Act”), “which prohibits, generally, the unfair discrimination of ‘individuals or risks of the same class and of essentially the same hazard’ with respect to both rates and insurability.” There are many possible insurance use cases that AI and data-based technology enable, like analytics-driven targeting, pre-underwriting, rules-based offer guidance and pre-fill data. Although these capabilities can be delivered without AI, the effort required to do so has historically been prohibitive, meaning that using AI will be essential in the coming years — as will ensuring that AI does not discriminate against protected classes.

A key area for insurers to monitor is the use of third-party data in underwriting processes that may not be directly related to the risk being insured. A good example of this is credit score, the use of which several states have restricted during the pandemic. NYDFS’s Circular No. 1 lists other external consumer data and information sources for underwriting that have “the strong potential to mask the forms of [prohibited] discrimination… Many of these external data sources use geographical data (including community-level mortality, addiction or smoking data), homeownership data, credit information, educational attainment, licensures, civil judgments and court records, which all have the potential to reflect disguised and illegal race-based underwriting.” Insurers must thus have transparency into what factors an algorithm is considering and how it arrives at decisions, and they must be able to adjust the included factors easily.

What will the regulatory future hold? Benjamin Sykes of Locke Lord foresees new model regulations requiring data to be subject to regular calls on underwriting criteria and risk-scoring methods, certification by insurers that the proper analysis to avoid any material disparate impact has been performed and a penalty regime focused on restitution above and beyond the difference in premium to those hurt by an algorithm’s decisions.

CIOs will need to consider how to handle the evolution of various regulations as they arise and their implications for how third-party data is used, how machine-learning algorithms are developed and applied and how AI models “learn” to optimize outcomes. Both the regulations and the technology are moving targets, so CIOs and the insurers they represent must keep moving, too.

The Evolution of Telematics Programs

Thirteen years after Progressive launched Snapshot, its usage-based insurance (UBI) rewards program, telematics-based policies represent a modest part of personal and commercial lines insurance. Bullish estimates of double-digit adoption by 2020 haven’t materialized, but it’s clear that telematics-based products appeal to a need within the market. Adoption will likely continue to grow. Insurers should consider telematics strategically, whether they expect to enter the space or not.

Adoption: Modest but Real

Insurer telematics activity in recent years has split into rough thirds: About a third of property/casualty insurers are actively engaged, a third are monitoring the space but not yet acting and a third feel telematics don’t apply to them.

Overall, Novarica estimates the penetration of telematics programs at around 6% to 8% of insurers’ overall books, based on industry research and conversations with insurers. These numbers vary substantially from carrier to carrier. At some, telematics-backed policies can be more than 30% of their books, while, at others, it can be as little as 1%. 

Applications: Increasing in Variety

Insurers predominantly use telematics for underwriting and actuarial or product design. This approach aligns with the stereotypical UBI offering, where insurers rate drivers based on telematics data and offer retention discounts to those who prove to be safe risks. More insurers are also providing pay-per-mile offerings (such as Liberty Mutual’s ByMile and Nationwide’s SmartMiles), which charge customers based on the actual amount they drive.

Applications in other insurance functions are less common, but this is changing as both insurers and vendors innovate to offer new types of coverages and programs, like rewards programs to generate regular customer engagement or teen driving programs that can leverage telematics to create speed alerts. These offerings align with broader industry trends toward creating richer digital experiences, particularly in personal lines.

Insurers should also understand that getting the most out of these advanced features requires technological and business support beyond the telematics offering itself. For example, to support a feature like automatic first notice of loss (FNOL), insurers will need quality data, and they’ll need to be able to move it between systems across the enterprise. A comprehensive rewards program may require focused effort from marketing and customer service to stay on-message and deliver a seamless experience.

See also: Driving Into the Future of Telematics

Program Design: Essential for Success

The variety of telematics capabilities and offerings in the market means that insurers should design or expand their telematics programs with care and forethought. As with any technology initiative, the point of telematics-based insurance offerings is to better manage risk, reduce costs or create a superior customer experience.

For telematics, that means that insurers need to consider a number of factors to guide the features of their offerings. These include the target market segment, the channel through which the offering will be distributed, the services offered and how all of these elements align with existing technological capabilities and processes. There’s no one answer, and anything from a basic UBI product to an engaging rewards program could be the right fit, depending on what an insurer wants to accomplish.

Fortunately for insurers that have taken a wait-and-see approach, there are a number of products available in the marketplace, from turnkey telematics solutions to book-of-business analysis from a variety of telematics service providers and data brokers. Although early adopters like Progressive procured and managed their own telematics devices, insurers don’t have to do this anymore. Carriers that are new to the space shouldn’t spend time replicating technology that already exists.

Telematics Beyond 2020

Telematics adoption will likely continue to increase slowly but steadily over the next several years. Depending on the rate of growth, telematics-based policies could make up between $22 billion and $32 billion of the personal lines auto market by 2025.

COVID-19 will be a major factor in that growth. Anecdotally, Novarica has heard from both insurers and vendors that interest in pay-as-you-drive or pay-per-mile policies has increased in 2020 as more Americans are working from home. How long the pandemic lasts and whether widespread remote work becomes normalized could speed adoption for both insurers and policyholders.

Auto manufacturers have also been active in the space, with a number of recently announced partnerships to share driving data from connected vehicles with insurance companies. This, too, could speed telematics expansion by lowering the initial barrier to entry. 

Telematics-based insurance offerings are a small but real portion of the personal and commercial auto markets that will continue to grow. Telematics isn’t going away, but it also won’t dominate the auto insurance industry in the next five to 10 years.

At the same time, telematics doesn’t have to become dominant to affect consumer expectations around price, convenience and service. Insurers should consider potential impact now so that no matter what decision they make, it’s a strategic one.

To learn more about how insurers are using telematics, read Novarica’s full report Telematics in Insurance: Overview and Key Issues.

How Insurers Are Applying AI

AI is everywhere. Insurers are piloting various AI projects, insurance technology vendors are building it into their solutions, some insurtech startups are all AI-powered and horizontal tech vendors are creating AI platforms that sit underneath it all. Insurers that haven’t experimented with AI yet are benefiting from the technology through third-party relationships, even if they don’t realize it. 

Unfortunately, the broad scope covered by the umbrella term “AI” can cause confusion for insurers — especially because some technology providers use this label to better position their offerings in the marketplace.  

Usage of AI in the insurance world can typically be broken down into four categories:  

  • Machine Learning. The goal of machine learning, a process where an autonomous system learns from a data set to identify novel patterns, is often to refine underwriting or claims algorithms. Applications include advanced predictive modelling and analytics with unstructured data. 
  • Image Recognition. Until recently, images were a type of unstructured data better resolved by humans. Image recognition leverages AI to extract insights from digital image analyses. Applications include photo analysis and handwriting processing. 
  • Audio Recognition. AI-enhanced audio recognition captures any sound (from human speech to a car horn) and turns it into a rich, usable data source. Applications include speech recognition and non-voice audio recognition. 
  • Text Analysis. AI-powered text analysis is pulling out meaningful insights from a body of text (structured or unstructured). Applications include form reading and semantic querying. 

Justifying the Use of AI in Insurance

Novarica’s Three Levers of Value framework can help conceptualize the business value of each AI use case for insurers. Each of these levers — Sell More, Manage Risk Better and Cost Less to Operate — is applicable to a specific AI technology use case. 

Helping insurers identify upsell/cross-sell opportunities, for example, falls under sell more, while accelerating underwriting risk assessment could be categorized as managing risk better and enabling more efficient help desk support helps insurers cost less to operate. 

These are just a few examples of the value AI can bring insurers; AI use cases span categories such as product/actuarial, marketing, underwriting, customer service, billing, claims and compliance. Key use cases include: 

  • Deploying better pricing models. This machine learning use case chiefly falls in the domain of product owners and actuaries, as it applies to the area of predictive analytics. In this case, AI can help actuaries make better decisions when pricing products, thus managing risk better. 
  • Improving marketing effectiveness. This machine learning marketing use case involves using third-party or internal tools to analyze vast amounts of raw data and identify the media channels and marketing campaigns with the greatest reach and engagement levels. Here, big data analytics can help insurers sell more. 
  • Performing better property risk analysis. Using AI-powered photo analysis, underwriters can generate faster and more accurate roof damage estimates. Ultimately, this helps insurers manage risk better. 
  • Leveraging smart home assistants to deflect calls from call centers. Through a voice prompt to their smart home assistants, customers can get quotes, request policy changes and even start a home insurance claim thanks to AI-powered audio recognition. By offering another avenue to help answer customers’ FAQs, insurers free their call center employees to address more complex customer inquiries, decreasing operating costs. 
  • Increasing invoice processing speeds. Through use of text analysis and image recognition technology, AI can help billing staff eliminate error-prone human invoice handling. Using AI-powered form reading leads to greater process efficiencies, which lowers operating costs. 
  • Identifying and mitigating claims fraud. Here, machine learning can help identify potentially fraudulent claims faster. This processing speedup gives claims staff more time to focus on higher-value transactions and leads to better risk management. 
  • Enabling automatic handling of compliance requirements. Machine learning can help team members improve compliance and reporting by automatically handling complex compliance requirements. This results in lower operating costs as compliance staff can direct their attention to tasks requiring human review. 

See also: 4 Post-COVID-19 Trends for Insurers

The AI ecosystem is evolving quickly, with new technology applications emerging every day. We may soon even see further AI and ML processing speedups with the advent of quantum artificial intelligence and machine learning.  

Insurers should not invest in technology-driven projects; instead, governance should search for use-case-driven projects that most benefit the company. However, in the case of important emerging technologies — like AI and ML — it’s valuable to look for ways to deploy that technology and build up skill sets (and culture) within the organization. Additionally, many insurers have an innovation group whose sole purpose is to future-proof the organization by seeking out opportunities to deploy emerging technologies. In these cases, it’s important to refer to actual business use cases and elucidate the concrete value they provide to specific business units.

To learn more on this topic, check out Novarica’s brief, Artificial Intelligence Use Cases in Insurance.