Tag Archives: chatbots

Realistic Expectations for Insurance in 2020

Visualize a meter that ranges from No Change (1) to Total Transformation (10). I expect the actual changes to the 2020 Insurance Industry meter to register somewhere between 1 and 2.

Thinking about insurance industry trends for the next year was always a fun exercise whether I was at the META Group, Financial Insights (IDC) or Ovum (now Informa Tech, I believe). Each trend captured the opinions from our team of technology-focused insurance industry analysts concerning what we thought would occur over three to five-plus years for each specific issue. Once the trends were finalized by the team, our trend report drove a significant part of our research agenda for the following year.

Instead of trends, I decided to publish my realistic expectations for the 2020 insurance industry:

  1. The League Tables (ranking of insurance carriers) for each major insurance line of business will look the same at the end of 2020 as the tables look at the end of 2019.
  2. There will continue not to be any (statistical or otherwise meaningful) correlation between investment levels in startup insurance firms and any measurable impacts on incumbent insurance firms specifically or the insurance industry generally. (Hype does not equal reality regardless of how much PR digital ink is spewed by the startups!)
  3. Insurance firms will continue in their grand tradition of exhibiting “magic bullet” syndrome: believing that the latest technology or technology application can resolve their major business objectives and can be implemented by using minimal company resources.
  4. Insurance firms, particularly in the U.S. and Europe, will continue to struggle to rationalize the large multiplicity of each of their core administration systems (i.e. policy administration, billing, claims management systems).
  5. Independent agencies (and broker firms) will continue to sub-optimize their operations by not acting in the reality that they are joined at the hip with each of the carriers they conduct business with.
  6. Although insurance firms will continue to recognize the absolute criticality of data, the firms’ various data elements will collectively behave more like useless sludge than a clean and useful resource.
  7. The lack of clean, standardized data will continue to hinder (stop?) insurers from successfully deploying customer-facing (and other market-facing, including producer-channel-supporting) initiatives.
  8. Most insurers will continue to give lip service to providing world-class customer service.
  9. The number of independent insurance agencies and insurance broker firms will continue to decrease as M&A continues in the producer channel, but the number of agents/brokers will remain stable.
  10. 5G, immersion technologies (AR and VR) and enterprise streaming will join the never-ending parade of technologies/technology applications in 2020, already chockablock with other “supposed insurance firm immediately must haves” that include leveraging social media, offering increased functionality on mobile devices, virtual agents/chatbots, interactive video for client onboarding and customer service, IoT, big data, cognitive computing, deep learning and machine learning – all of which technology firms will use as door openers as they reach out to insurance CIOs and CTOs.
  11. Cyber risks will continue to cascade through any device connected to the Web used, owned, leased or otherwise in the possession of society (families, individuals, businesses, federal/state/local governments and the military) adding more pressure on insurers to decide whether or how to profitably offer protection or services.
  12. I’ll continue to hope, in vain, that increasingly more insurance firms will realize the importance of using geospatial solutions as critical components of decision-making, whether the geospatial data comes from terrestrial or Earth Observation sources.

See also: Are You Ready to Fail in 2020?  

Integrating Chatbots, Policy-Handling Apps

Insurers can leverage chatbots to improve a number of customer journeys, including operational processes such as claims first notification of loss, and processes related to the purchasing of insurance products, such as quote and buy. This article covers recommendations related to the latter, specifically focusing on best practices in integrating chatbots with insurance policy-handling applications.

Two initial considerations may be relevant.

First, will the chatbot journey be a sub-journey within a channel, or a net new channel?

Sub-journey within a channel: If the entrance point to the chatbot conversation is contained within the insurance platform’s front end, existing for example as a button on a web page, then the chatbot solution may not attract extra traffic and will effectively be a sub-journey contained within an existing channel.

Net new channel: If the entrance point to the chatbot conversation is outside of an existing channel, for example via Facebook, then the chatbot may generate new traffic, potentially adding significant value.

Second, will the chatbot journey replicate an existing journey, or will it be a net new journey?

Replicate an existing journey: The chatbot asks the same questions in the same order as they are asked in the journey being replicated. For example, if the journey being replicated is quote, then the chatbot asks the same questions as the quote web journey in the same order.

Net new journey: The chatbot asks questions different from those asked in other journeys, or asks the same questions in a different order. For example, a quote-and-buy chatbot may be built to ask different questions or use a different tone of voice depending on responses input by the customer, such as age or location. This provides a customer experience that is truly different from that of other channels, potentially engaging different audiences.

Beyond the initial considerations, it is important to analyze in depth the architecture of the platform into which the chatbot solution is to be integrated. Key areas to cover:

  • Insurance policy-handling application: What data does the policy system expect at the end of the chatbot journey? For example, if the chatbot replicates a quote journey, what data does the policy system require from the chatbot to convert the quote into a policy?
  • Integration layer: Is the existing integration solution fit for purpose, or should a new solution be defined addressing how to pass data between the chatbot, the policy-handling application and any relevant near and downstream applications?
  • Management information (MI) solution: Does the policy handling platform have an MI solution, and is it feasible to use that MI solution to gather data relevant to the chatbot conversation? For example, a platform may have an MI solution, but a decision may be made to use the analytics produced off the back of the chatbot to gain a more granular view of the conversation.
  • Billing application: Are there integrations between the core policy system and the core billing system that could affect the chatbot solution? For example, if the chatbot conversation collects data that needs to land in the billing system, the format in which the data is collected must comply with the billing data model.
  • Near-stream/downstream applications: Are there applications that consume data from the core policy system that could affect the chatbot implementation? For example, there may be a documentation system that the chatbot needs to integrate into.

See also: Chatbots and the Future of Insurance  

After the architectural issues, consider the overall response times of the chatbot, which have a significant impact on the user experience. Key points:

  • One question at a time – the implications: Whereas in web journeys customers can see a number of questions at the same time, and as such have something to read and engage with if the response to their inputs is slow, with a chatbot questions are presented one at a time. The outcome is that when a customer is engaging with a chatbot, and the chatbot response is delayed, the customer can do nothing but wait, which may lead to a poor customer experience.
  • Minimizing chatbot response times: An approach to minimizing response times is to build a skeleton proof of concept (POC), then test response times using the POC. A second approach is to perform performance profiling, where speculative analysis is done upfront to model how long each transaction will take. For example, knowing that a rating call made from the policy handling application takes circa one second, it can be estimated that the same called made from the chatbot will take circa one second, too. The combination of early modeling and POC should avoid the situation whereby performance issues are baked into the chatbot solution through architectural decisions that are hard to reverse.

Analysis should be done on the look and feel of the chatbot. The two main options are to have the chatbot conversation align with the look and feel of the other customer-facing journeys, or give the chatbot a look and feel distinct from all other journeys. A key disadvantage of the latter approach is that customers, not recognizing the look and feel of the chatbot, may think they landed on the wrong website or, even worse, on a phishing platform. Points that should be considered are:

  • Location in journey: If the platform’s front end indicates to users where they are in the journey, for example highlighting that a user has answered nine of the 12 questions required for a quote, does the chatbot do the same?
  • Forward and backward navigation: Is it possible to navigate forward and backward in the chatbot conversation? For example, a chatbot conversation could be built so that questions A, B, C can only be asked once and in order, or could be build so that the customer can navigate both from A to B to C and from C to B to A. If the second, then care should be taken to ensure that answering again previously answered questions does not cause issues with near-stream and downstream application.
  • Multi-device look and feel: Is the chatbot look and feel consistent across devices? For example, a chatbot that is built to replicate the look and feel of the underlying policy-handling application will need to maintain that consistent look across web, mobile and other customer devices.

Once deployed, a chatbot coupled with a feedback-gathering and -reporting framework can help uncover insights about customer journeys. For example, web forms do not analyze what the user is doing between when the web page is loaded and the click on the submit button, but chatbots do. Chatbots can help uncover:

  • Conversion rates per question, allowing local optimization.
  • Net Promoter Score (NPS) for the insurance brand delivering the product.

Each optimization derived from the learning may not be significant on its own, but the sum of all optimizations can lead to significant improvements for the overall journey. Furthermore, because, on average, chatbots can be improved once a month, the result is a learning curve with a faster acceleration than traditional web forms.

See also: Chatbots and the Future of Interaction  

Key Takeaways:

  • Defining whether the chatbot conversation will be a new channel and whether it will replicate an existing journey helps in defining the overall chatbot use case.
  • It is good practice to analyze in detail the application architecture into which the chatbot is being implemented, focusing in particular on the core insurance policy application, integration layer, MI solution, billing application and near-stream/downstream applications.
  • The time it takes for the chatbot to respond to user inputs is particularly important, as chatbot journeys display one question at a time.
  • It is helpful to define upfront whether the look and feel of the chatbot conversation will mimic the look and feel of the other customer-facing channels.
  • An implemented chatbot can be used to uncover insights about customer journeys, especially with regard to conversion rates and NPS scores.

Chatbots and the Future of Insurance

The future of the insurance industry is the customer. More and more insurance companies are stepping up their games and moving to digital and customer-centric strategies.

The push to position the customer at the forefront of the industry is driving the adoption of self-service technologies — digital offerings capable of delivering more user-friendly customer experiences. In turn, the industry is embracing another customer-centered technology: chatbots.

But important questions remain:

  • How will chatbots solve historic industry pain points around customer service?
  • How can humans and chatbots work together to improve the customer experience?
  • In what ways will chatbots change the insurance industry over the long term?

By answering these and other questions, the insurance industry can better leverage chatbots to improve the customer experience —and prepare for the changes ahead.

Chatbots: Why now?

Until recently, customer service has been frequently discussed but rarely prioritized in the insurance industry. Traditional customer-facing processes rely heavily on phone interactions and fall short of customers’ digital expectations.

Although some insurers have reduced the tedium of traditional processes, customers frequently experience long wait times and multiple touchpoints for a single action or request.

Taking a page from the e-commerce playbook, leading insurers are empowering customers with self-service capabilities, which augment the work of employees and agents, allowing them to focus on more meaningful customer interactions.

See also: Will Chatbots Take Over Contact Centers?  

Chatbots are a natural extension of the push for self-service capabilities. They improve the customer experience through a cooperative approach involving both humans and artificial intelligence (AI). The timing is right for the widespread adoption of chatbots.

Just 34% of consumers report they have definitely not interacted with a chatbot in the past year.

By making it easier for customers to answer common questions and perform routine activities like filing a claim, chatbots reposition customers at the center of insurance processes.

How chatbots improve the customer experience

Retail and other sectors have successfully used chatbot technology to significantly enhance the customer experience. However, the buying and claims processes in insurance present unique challenges. Insurance customers purchase policies because they are afraid of losing things they care about: cars, houses, even loved ones. As a result, customers are more emotionally invested when buying insurance than when they purchase consumer goods or even big-ticket items like vehicles.

Chatbots help neutralize emotions during the transactional stages of the insurance lifecycle. In some ways, chatbots are an extension of a web search. The difference is that they go deeper and present customers with information that is otherwise difficult to access. Chatbots also streamline routine tasks, eliminating the frustration policyholders commonly experience when dealing with insurers.

Consumer acceptance of chatbots is largely predicated on the availability of human interactions for certain tasks. Nearly half (49%) of consumers feel better about using chatbots if they know they can escalate the experience to a human interaction.

The ability to defuse the emotional element of buying insurance accelerates the speed of doing business. In addition, agents and customer service representatives no longer have to perform transactional processes, which improves their quality of life in the workplace and leads to a faster pace of service and internal transformation.

There are limits: At some point, humans must engage customers directly and assist them with the emotional aspects of the insurance process. Still, with today’s most advanced chatbots, there is the potential to capture new insights and further improve the policyholder experience through personalization. For example, when a customer prices flood insurance, chatbots with AI running in the background can quickly identify the best and most affordable coverage for her situation.

Insurance customers have grown to expect this level of personalization from retailers and consumer-facing brands. As AI-powered chatbots gain traction, customers will expect the same from their insurers.

A look ahead: What’s next in chatbots for insurance companies?

Chatbots will have a transformative effect on the customer experience, but benefits may vary by insurance category.

Chatbots provide an opportunity to reimagine the role of health insurance and position health insurance providers as true partners in the quest for better patient outcomes. For example, chatbot solutions can provide free/low-cost health coaching or other services.

See also: Chatbots and the Future of Interaction  

In life insurance, chatbots enable insurers to build on existing trust and serve as wellness partners to their customers. Leveraging AI, chatbots can deliver personalized recommendations that improve customers’ quality of life by promoting financial, physical and emotional wellness.

Similarly, chatbots have the potential to generate added trust with property and casualty insurance customers. With advanced analytics, chatbots can improve how customers price insurance policies or streamline the claims process. When combined with IoT devices, chatbots may even be able to provide real-time analysis, enabling customers to prevent incidents like a leaking hot water heater or a pending part failure on a vehicle.

By focusing on customer needs, chatbots not only strengthen policyholders’ relationships with their insurers but also drive sales through touchpoints that span a range of digital channels. Humans remain a vital part of customers’ experience with insurers, but, with chatbots on the team, human agents and customer service representatives have the freedom to focus on more meaningful aspects of customer relationships — a big win for customers, employees and insurance companies themselves.

Claims Technology: One Size Won’t Fit All

As I watch the insurtech space, I see a fair bit of hype around disruptive technology. Some of what I see reminds me of the dotcom boom of the late 1990s. Then, as now, lots of startups promise to deliver something ground-breaking, and all are competing in the same space, with many of the same ideas.

To put this into perspective, let’s look at the global claims loss pyramid. This pyramid is a simplified model that segments claims into four main types. The base layers of the pyramid — representing technology-driven and networked services, and low-value, high-volume claims – are where technology-enabled offerings can be effective. Technology also is beginning to flow into the level above those, where higher-value and more complex claims reside. The top of the pyramid is large complex claims, which are the most technically challenging and require highly experienced loss adjusters. Aligned with these levels are the requirements for claims talent and experience; base-level claims only need basic skills, whereas the top of the pyramid requires the most advanced skills.

At the moment, the insurance industry is aware that the claims experience is often the only contact a policyholder ever has with the insurer, so the claims journey must be completed correctly. Around the world, policyholders are demanding the same amount of transparency and oversight in the claims process that they get as consumers when they order something from Amazon or another online retailer. Many personal lines policyholders expect to be able to track and interact with their insurance carrier via an app or mobile portal.

Similarly, if they have an app to see their policy details, renew coverage, etc., then consumers expect to be able to make a claim in that app and track or manage it to completion. It is apparent, though, that many prefer a mobile portal because they rarely make a claim and don’t want to install an app they may never use again. They don’t want to have to remember login details for the portal, so they need an encrypted link that will take them straight to their claim, just like a FedEx tracking number. They expect to be able to communicate with all parties via the portal using messaging, not email, but if they need to send in photos or documentation, they may transmit those through the portal without necessarily knowing or caring who is handling the claim.

See also: Visual Technology Is Changing Claims  

For large complex/technical claims, however, policyholders and other stakeholders have different expectations as to how their claims are handled, and how the communication works. Many insurtech startups don’t seem to differentiate in this claims segment – to many of them, “a claim is a claim.” In some cases, they clearly don’t understand the distinction between simple, low-value claims with repeatable steps, and larger, more complex claims where multiple, detailed narrative reports are needed to settle the claim. Either that or they have chosen to focus in the volume claims space for commercial reasons. Large complex claims are expertise-driven. Technology can support highly experienced adjusters, but technology for the foreseeable future will not replace the skill sets or expertise needed on such claims.

A few of the new breed of software-as-a-service (SaaS) claims systems put the insured front and center in a collaborative claims process. The vendors say they can handle just about any type of claim, but they are only good for personal auto, volume and third-party administrator claims. Liability claims often entail complex sets of facts and negotiation, beyond the scope of most vendor offerings. The vendors also don’t offer client-driven SLA tracking, document management, co-insurers, multi-currency or billing capabilities. They assume that a claims manager overseeing a $10 million warehouse fire will trawl through an app on the phone to see status updates. Client-driven SLA tracking, such as through dashboards, is important for clients to make sure their claims are handled in the correct way. Vendors and their advisers need to understand their market better.

Lots of insurtech startups are focused on the easy part of the equation – signing up policyholders via chatbots, submitting claims and using artificial intelligence to settle simple claims. Many startups are creating chatbot apps to sign up domestic policyholders and allow them to submit a claim. Every insurer will have this capability soon, as it is simple to build using third-party AI services, so I don’t see this as a market differentiator. We do not see a new breed of agile SaaS claims systems coming to market yet, probably because that is a more difficult system to understand and build. There is also resistance from some suppliers to handing their data and claims process over to a third-party SaaS provider. I haven’t yet seen one of these new-breed claims systems that is built around SLA-driven workflows that drive the claim to completion, though I am aware of some in development, none could support a traditional claim-adjusting business. What some vendors are currently offering is a claims-light system, focusing on collaborating with the various stakeholders via portals and apps instead of email.

In the past, suppliers such as outsourced claims management organizations would each build their own systems to fill these gaps, often because insurers either didn’t see value in owning the claims system or couldn’t successfully bring their own solution to market. This multitude of external systems and processes doesn’t serve policyholders well, because it doesn’t always provide a consistent, positive experience when they make a claim. If I am a homeowner policyholder with ABC Insurance, then I should have an ABC Insurance app on my phone that I can use to manage my policy and any claims arising from it, including all communications with whoever is handling the claim. I should expect any outsourced provider or supplier to be plugged into that same system, either because they are directly using the same system or because their own systems are linked into it via an API.

Traditional insurers need to re-evaluate how they outsource their claims to a third party because they continue to risk getting disintermediated from their relationship with the policyholder when so much of the claims process occurs outside their organization and systems. They should guarantee a great customer experience when a policyholder makes a claim, and to do that they need to control the collaboration space — the communication and data-sharing piece. Other financial services organizations have already solved this problem for the same reason: to reduce customer churn. Traditional insurers should be able to get this right, but many will need to become more agile in the way they deliver technology to do so. In the meantime, new entrants are launching with systems that already provide their policyholders with a better, more seamless claims experience. I’m not sure how well their systems integrate with external suppliers yet, but I suspect they will get there quickly because their organizations and systems were designed from the ground up to be agile and innovative.

See also: Key Technology Trends for Insurers in 2019  

Much of the above is already being discussed within the industry. I focus on the claims systems piece because I have been involved in many of these systems over the years, and it is interesting to see the same ideas come up again and again. We were talking about the “shared electronic claim file” in the early days of the internet. Now we are talking about a “collaborative claims workspace” decades later.

I am confident that the technology available today will solve these long-standing problems quickly. I have been involved in discussions with low-code development platforms, and it is incredible what companies can now build and deploy for web, mobile and desktop in a couple of months. At this pace of innovation, advancements in claims technology will bring the industry to a crossroads. Will the industry embrace the opportunity to transform its service through the claims experience? Time will tell.

The 5 Top Trends in AI and RPA

Insurance companies are only beginning to harness the potential of artificial intelligence (AI) and robotic process automation (RPA). AI refers to computer systems that can mimic human capabilities by learning and solving problems. RPA is an emerging form of business process automation technology based on using software robots or AI “workers.”

Here is a look at the top five AI/RPA trends in the insurance industry:

1. Machine Learning for Fraud Detection and Risk Assessment

Humans learn from experience and thus can predict outcomes. Insurers are beginning to use AI algorithms with big (and small) data to accurately predict outcomes.

Machine learning, or AI, is being used to improve customer service, guide the development of products, detect risks and cross-promote products. It is helping insurance companies to improve their efficiency by facilitating damage assessment, identifying billing anomalies, boosting fraud detection and identifying lapsed policies.

2. Chatbots Offer Personalized Customer Care

Chatbots use AI to work as autonomous, internal customer-service agents that respond to customer queries. They keep a log of most frequently asked customer questions.

Chatbots can efficiently handle many routine requests, such as changing the policyholder’s address or adding a beneficiary. By handling grunt work, they can free skilled human advisers to offer the kind of guidance they do best.

See also: Next Big Thing: Robotic Process Automation

But there’s more. Using AI, chatbots can talk with customers to identify their needs and recommend the most appropriate coverages to them. They can even cross-promote products based on the customer’s needs. Then, the customer is turned over to a human adviser to answer any questions and complete enrollment.

3. AI Uses Data to Better Predict and Mitigate Risk

Insurers depend on their ability to predict and manage risk. The more information they have access to, the better their ability to assess risk.

AI enables the collection of both structured and unstructured data. Besides the insurer’s own data on insureds, structured data includes information collected through sensors in wearable devices and other IoT devices. Unstructured data is collected from public spheres such as social media pages and search engines. This data can then be used to create insights that not only help insurance companies protect their bottom lines but also give them a true competitive edge.

Employee benefits is a particularly promising area. AI is now being applied to streamline pre-approval workflow. For instance, before an insurance-company employee replies to a customer, the response can be passed through a smart compliance system that reviews it and makes any necessary adjustments before it goes out.

4. Automating Routine Processes

Other processes that are now being automated using RPA include copying and pasting data to spreadsheets, logging into applications, transferring data from one database to another and opening emails and processing them.

See also: How to Automate Your Automation  

5. Claim Processing

AI and RPA are now being used to automate claim processing, especially in property-casualty insurance and employee benefits insurance. The system assigns adjusters, integrates the disparate claim information and facilitates claims payments. For instance, ClearPay is an insurtech product that insurance companies, agents and brokers can use to integrate the settlement process and monitor claim payments in real time.

AI and RPA are only beginning to transform how business is done in the insurance industry. We can expect to see burgeoning usage in operations, customer service, risk assessment and mitigation and regulatory compliance.