CEO of INZMO, a Berlin-based insurtech for the rental sector & a top 10 European insurtech driving change in digital insurance in 2023.
I believe future history books will likely name November 30, 2022, as the day everything changed. That was the day OpenAI launched ChatGPT into the wild.
It took a few days for people to realize the leap forward it represented over previous large language models (known as “LLMs”). The results people were getting helped many realize they could use this new tech to automate a wide range of tasks.
Since then, there has been a frantic scramble to assess the possibilities. Just a couple of months after ChatGPT’s release (what I call “AC”), a survey of 1,000 business leaders by ResumeBuilder.com found that 49% of respondents said they were using it already. Nearly all of those (93%) were planning to expand their use of the tech. Another 30% of companies said they’re planning to start using it soon.
LLMs And The Insurance Industry
The insurance sector is a large employer of humans. Many tasks in our sector have required our incredible ability to problem solve on the fly. We have to seek out just the right information for a particular situation and then communicate it to colleagues or customers in a digestible fashion.
Insurance giant Zurich announced that it is already testing the technology “in areas such as claims and modelling,” according to the Financial Times (paywall). I think it’s reasonable to assume that most, if not all, other insurance companies are looking at the technology as well. My own company, for example, has just launched a chatbot service to improve customer service.
What’s next for chat-enabled insurance?
If that’s where just a few months can get us, what else can we expect?
LLMs can have a significant impact on the future of work, according to an OpenAI paper. This includes the insurance industry. The paper categorizes tasks based on their exposure to automation through LLMs, ranging from no exposure (E0) to high exposure (E3).
LLMs can impact the insurance industry by automating some tasks, enhancing others and creating new opportunities for learning and innovation. Some of the tasks in the insurance industry that could be exposed to LLM-driven automation are:
• Writing and editing insurance policies and contracts: LLMs can help insurance professionals write and edit policies and contracts that are clear, accurate and compliant with regulations. They can also generate templates and suggestions based on customers’ needs and preferences.
• Data entry and analysis: LLMs can help insurance professionals enter and analyze data from various sources, such as forms, documents and images. They can also extract relevant information, perform calculations and create reports and dashboards.
• Customer service and support: A chatbot can answer common questions, provide information, help resolve issues and offer personalized recommendations.
• Fraud detection and prevention: AI can help detect and prevent fraud by analyzing patterns, anomalies and behaviors in data. It’s also possible to flag suspicious transactions, alert authorities and suggest actions to mitigate risks.
• Risk assessment and management: Natural language processing and machine learning can help insurance professionals assess and manage risks by understanding complex scenarios, evaluating probabilities and estimating outcomes. LLMs can also provide guidance and advice on how to reduce or transfer risks.
• Claims processing and settlement: LLMs can help verify the validity, accuracy and completeness of claims. They can also automate tasks such as document review, damage assessment, payment calculation and communication with the claimants.
So, is it all plain sailing?
Hold on—this is not a one-way street, and there are serious issues that need careful thought.
For instance, a February 2023 Ipsos survey of 1,109 U.S. adults found that less than one-third of respondents trust AI-generated search results. Insurers will need to persuade and reassure customers about their use of LLMs.
To persuade and reassure customers about AI, it’s important for insurers to be transparent about how they are using the technology and what data they are collecting. Provide clear explanations of how AI works and how it is used to make decisions. Additionally, provide customers with the ability to opt out of certain uses of their data or AI-based decisions. Insurers must also provide customers with clear information about how their data is protected and what measures are in place to prevent unauthorized access or misuse.
For leaders in the insurance sector, it’s important to keep the wider ethical picture firmly in our sights to avoid the risk of a consumer backlash. So, when it comes to determining how and whether they’ll use AI in the future, insurers need to identify the right use cases, ensuring that the data used is accurate and unbiased. This requires being aware of the potential risks associated with AI and taking steps to mitigate these risks.
In broader ethical terms, there has been a lot of discussion about AI explainability—or rather the lack of AI explainability. As an Insider Intelligence article explained, “Generative AI is generally ill-suited to fully explaining its actions, making it inappropriate for making pricing decisions that have to be explained to internal stakeholders and regulators.”
Indeed, it is now clear that AI is far from perfect. It has limitations, such as errors, biases, inability to grasp context/nuance and ethical issues. Insider also pointed out that AI’s “rapid rise” means regulation is currently behind the curve. It will catch up, but this is likely to be piecemeal, with different approaches mandated in different national or state jurisdictions. This can create complex additional burdens on insurance companies.
Despite these limitations, and calls from some to slow down the pace of AI development, I believe it’s likely that AI chatbots will be taking bigger roles in insurance in 2023. For leaders in the insurance sector, it’s important to keep ethics in mind. That might mean going slower in the short term, but before November 30, 2022, would you really have imagined getting even this far?
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