Exploring the Use Cases of an Enterprise Chatbot eBook

Posted on December 19, 2022

Blog: Dont dismiss messaging platforms

Eurapco also puts emphasis on communicating its partners’ achievements and errors, so that through collaboration best practices can be shared as well as mistakes, in order to avoid redundancies. And this is exactly where Large Language Models (LLM) such as ChatGPT shine and why this modern technology is having such an impact as well in the insurance industry. ChatGPT’s capabilities are so broad that experts across the industry develop and discover new use cases and applications every day. Besides, API portals may combine public data with proprietary insurance data to enable the creation of better insurance products for customers.

For the most part, this has been provided data, where customers are asked directly for information or where that information is looked up on their behalf (e.g. credit scores). However, with more powerful algorithms at their disposal, today’s insurers are incentivised to collect a wider array of data that could yield new insights about the likelihood of customers making a claim. For example, they should not be applied for high-stake tasks, as the outcome can be inaccurate, give biased responses or use repetitive phrases that can adversely affect the content’s overall quality. Care should also be taken when discussing legal and ethical issues, solving complex problems, or wanting to give personalized advice. It is therefore particularly important to phrase the questions and instructions precisely as ChatGPT is not merely a database containing information, but a “brain” that ultimately learns from the information it is given and has access to.

Automate Redundant Queries

It is optimized to execute a limited number of specialized processes that replace the need to talk to an expert or use more complicated interfaces such as mobile apps or websites. Who is responsible for subsequent liability if directors use generative AI to help with corporate decision making? EY reports that it believes most property and casualty insurers will prioritise claims management when adopting generative AI. Instabase’s blog post shows how LLMs can help actuaries analyse large volumes of data accurately and efficiently for portfolio risk analysis. Scale-up insurer Lemonade says it can now deploy “fully compliant generative AI capabilities at scale” as it looks to improve operational efficiency.

As data becomes ever present, either through point-to-point interactions or broader ecosystems, insurers must ensure they can source the right data at an efficient cost. Ideally, external data will directly feed internal product offerings by allowing insurers to produce meaningful insights and analysis to tailor product offerings and enrich the customer. Insurers are typically rich in data, and new types of data sourced from wearable technology or social media, and the permissions surrounding these are disrupting the market. Through regulated ecosystems, organisations will seek to share data openly, in line with data protection and compliance rules.

Customer Behavior Recognition

Whilst ChatGPT isn’t perfect, neither are humans, both will err, but those who ignore it will struggle for divine forgiveness. ChatGPT (Generative Pre-trained Transformer in case you were wondering) is not the only show in town. There are a myriad of other AI tools available, which can perform tasks such as creating art, videos, text speech, https://www.metadialog.com/ social media content, voice-to-text generation or sales engagement. These tools aren’t just a new way for students to dodge essay writing, they have real-world business applications which have already made significant changes to that world. Users can utilize chatbots to submit insurance claims and get information about the claims procedure.

Give your web visitors 24 hour customer service, allowing them to get quotes, process claims and get general information all within one simple platform (and with no agent assistance). Even when customers decide to purchase policies, there’s still a lot of matching to be done. Since requirements usually differ greatly from case to case, the insurance industry relies on customisation. So, before agents can recommend options bets fitted to customer needs, they take a while to study their profile and purchase history. Powered by Using Natural Language Understanding (NLU), bots can interview customers about their situation and offer insurance products that would suit them best.

At peak times, more than 300 customers have transmitted their meter readings via the voice bot per day. Therefore we present some selected chatbot use cases of our customers in this article mainly from the energy and tourism industries, but also from finance, e-commerce, and real estate. Please note that the selected chatbots are in German, but you can learn more about them in the English description. Give your web visitors 24 hour insurance chatbot use cases customer service, allowing them to request quotes, submit claims and get general information all within one simple platform (and with no agent assistance). ProNavigator also allows for bot-human handoff, meaning real brokers or insurance agents can jump into the chat if the bot is stuck or the end user requests assistance. At the same time, given data breaches and privacy concerns, customers often reluctantly share personal online.

Khan additionally emphasised that the real impact of ChatGPT has been in the way it has provided more industry-wide optimism in the ability of AI generally to help insurers. Haris Khan, manager of the insurance consulting practice at professional services firm Deloitte, has anecdotally heard of some underwriters using ChatGPT as one tool to help augment the underwriting process. “For example, the lives of people writing marketing content will get easier at one level, but we’ll need [less of these employees]. Trained on text taken from the internet, ChatGPT has been designed to provide quick and understandable answers to any question.

Snapshot Paper – AI and Personal Insurance

Customers are demanding better, faster and more effortless experiences than ever before, whether via the website, on the phone, or through social channels – and insurance is no different. Being able to optimise performance whilst enhancing customer experience is the panacea and service automation can play a big part in this journey. But there is a fine balance in reducing costs and increasing efficiency whilst driving higher levels of customer satisfaction. This is where blending insurance chatbot use cases service and process automation with live advisors can offer an instant and enhanced service whilst delivering a personalised experience. Unlike more traditional insurers, who require paper applications in some cases that can take weeks, startups using insurtech often provide rapid digital application processes. Zego for example, started in 2016, wanted to solve the problem for business insurance by providing quick and easy business insurance to save time and money.

As new sources of data come on stream – including wearables and telematic devices – insurers may find themselves collecting more information about their customers than is necessary to deliver their core services. If customer data is later sold onto third parties, it raises the question of whether the subjects have been adequately reimbursed for the value they have created for the company. The collection of more data may also increase the chance that algorithms pick up biases during the training phase (see Box 2 for more detail).