Two Insurance Chatbot Case Studies that Define Optimization

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The basic principles of how the insurance industry works seem easy enough to understand. That is until something actually happens beyond transaction processing. In fact, the mere act of filing a claim can become a massive confusion for both the company and the claimant, depending on the extended (articulated) circumstances.

But even in this case-by-case business landscape, chatbots are still very capable of streamlining potential automated customer interactions. Specifically for the insurance industry, conversation AI in the form of insurance chatbots has fully adapted to become what legal brokers, agents, and counselors have long sought.

Insurance Chatbot (Specific) Use Cases Recap

Insurance industry-oriented automated assistants are similar to another customer service AI in terms of dealing with first-level queries. However, it is dealing with user-specific questions that make insurance companies truly favor insurance chatbots. Indeed, streamlining claiming processes directly correlates to huge time savings, resulting in overall financial gains when pitted against traditional methods.

The following specific chatbot use cases are highly beneficial for this particular industry:

  • Provide customized quotes – a very valuable feature of chatbots in highly price-variable services. Being able to ask for an accurate quote without the need to fill out a form or interact with a human representative does provide inherent benefits of availability.
  • Process policyholder FAQs – the undying first-line advantage of chatbots for any customer service department. For insurance companies specifically, this allows easy filtering of customers into process categories, most notably when the AI generates optimal suggestions on what policies to look out for.
  • File, process, and manage claims – this isn’t just meant to ask what service is needed to then pass over to a human agent at the next step. The chatbot itself would guide the user through the entire process of submitting a claim, step-by-step, providing there are no requirement issues, of course.
  • Instant update and new policy option notifications – quite self-explanatory. This does not involve active interaction, but is instead given as part of a suggestion list. For example, if the user’s information requests happen to be also technically related to something recently updated.
  • Settlement confirmation and transaction – once all other processes and transactions have been officially approved, the chatbot could then automatically deal with the agreed settlement. Again, step-by-step, and instruction per instruction. Operation-wise, this is not much different than your average banking chatbot processing service.
  • Fraud detection – in the case of unapproved claims due to potential fraud, the chatbot should also be capable of knowing and handling them with immediate standard measures. Needless to say, this is the single most important high-level feature that needs to be implemented with “integral” (multi-system level) accuracy.
  • Broker management – this one is a kind of a hold-over of the same responsibilities for other types of dealers. Chatbots can, albeit with limited repertoire, directly sell insurance policies, especially when done on behalf of multiple other companies within the same industry.

Modern Case Study 01: Kela Combines the Power of Many Chatbots Into One

fundamentals for insurance chatbot
  • Chatbot Provider: Boost.ai (case study)
  • Client Country of Origin: Finland
  • Specific Result Achieved: Multi-role virtual agent built in just four months
  • Main advantage(s) utilized: Template integration, scalability, starter-friendly environment

Kansaneläkelaitos, more colloquially known as Kela, is a mainly government-funded social insurance institution. Due to its foundation, it operates under Finland’s national social security programs, tasked with settling benefits regarding all the technicalities of using such a government service.

Kela’s online presence in the Finnish insurance industry is very significant. Even in 2020, its online services were accessed more than 64 million times, with most of the information submitted through application or processing done exclusively within the same digital space. The company knew its limits and had already deployed chatbots under each of its primary benefit programs. However, having separate chatbots for each benefit service caused optimization issues. The chatbots were simply a constant challenge to maintain, diminishing the otherwise huge benefits they offer in automation.

Yes, as spoiled by the sub-topic title, the solution was to unite all chatbots into a single automation entity. According to Boost.ai, around 1,500 topics from the entire suite of eight chatbots were focused into a single “super chatbot.” It was then fitted with a system that could accurately contextualize the flow of the digital conversation into whichever of the eight main categories were required. This is without the need for the user to specifically mention the category itself.

The result? Services began to integrate themselves into a sort of multi-tiered benefit program eligibility assessment. From basic income support and child benefits to educational benefits when the child reaches a certain age. In fact, the updated insurance chatbots system launched just a few months ago, in March 2021. This is a testament to the speed of positive results brought by chatbots once the correct configuration is established.

Modern Case Study 02: AA Ireland Forever “Sharpens” Chatbot-made Quotes

  • Chatbot Provider: ServisBOT (case study)
  • Client Country of Origin: Ireland
  • Specific Result Achieved: Customer quote conversion rate increased by 11%
  • Main advantage(s) utilized: Intent analysis, navigation, suggested actions

In 2019, long-time automotive service company AA Ireland finally caught up to the chatbot meta. The company has been operating since the early 1900s, and has developed a good number of services related to travel, thus its focus on various automotive-related features (technical, information, rescue, etc.).

Most notably, part of AA Ireland’s service portfolio is insurance broker management, collating insurance services from around the country to offer within their insurance-related service suite. Needless to say, the company specializes in car insurance, although it also offers a number of different insurance services depending on its nature and relation to the company’s standard service suite.

Roughly eight years ago, AA Ireland launched its mobile app, intended as a central hub for updated information, submissions, queries, and most notably, reports of roadside breakdowns. Because each query is almost always linked to availing some sort of immediate service, improving lead generation had eventually become the company’s biggest goal. Unfortunately, it had never been truly capable of optimizing quote estimates and actual sales prior to the deployment of a chatbot due to the sheer number of online queries hammering down on the limited number of human agents.

But in 2019, with the help of the ServisBOT platform, the company finally achieved the successful deployment of a conversational AI capable of filtering out online customer interaction, never to miss a single query ever again.

Well, almost. AA Ireland’s quote bot plus customer service bot was able to increase conversation rates by 11%, and slammed down missed webchats to just 19% (81% reduction). One particularly notable achievement was that the chatbot was able to automatically tweak the insurance applications bit by bit (during the application process), so that satisfied customers are able to leave with a very competitive quote that reflected more or less their intended estimates.

Is It All Optimization? Always Has Been.

Simplifying a process to automate it more reliably is a feature that has been the staple of software development since its humble beginnings in the late 20th century. It holds true before, and still holds true today.

In the case of insurance chatbots though, streamlining still poses a somewhat higher requirement for complexity. All variables that need to be addressed for each particular case and user need deeper integration, as shown by the themes of effective combination and strategic reduction by Kela and AA Ireland, respectively.

All sources are provided as links within the article.

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