Revolutionizing the Actuarial World

Many quotes about imagination are attributed to Albert Einstein. One favorite is, “Logic will get you from A to B. Imagination will take you everywhere.” In my thinking, that statement can be applied not only to scientific discovery but to the actuarial world as well. In this first of two “Revolutionizing the Actuarial World” articles for The Actuary, I explore innovation—with an assist from imagination—in our field.

Streamlining Migration with AI Power Tools

Generative artificial intelligence is a subset of artificial intelligence (AI) that uses generative models to produce text, images, videos and other forms of data. These models learn the underlying patterns and structures of their training data and use them, given a proper natural language prompt, to produce new outputs.

In the world of generative AI, a fierce competition is underway. In 2022, OpenAI’s public release of ChatGPT brought generative AI into our day-to-day lives. In February 2023, Meta AI was launched. In the same month, Microsoft launched Bing Chat, integrated into Microsoft’s Bing search engine, which was later rebranded Microsoft Copilot.

The next month, Baidu, a major Chinese internet firm known for its search engine, launched ERNIE Bot, its ChatGPT-like AI chatbot service. Meanwhile, Google launched Bard, its conversational AI, integrated with Google’s search capabilities. Earlier this year, Chinese startup DeepSeek affected financial markets on claims that it could develop advanced artificial intelligence models using much cheaper semiconductors than previously thought possible.

According to a survey by the SAS Institute and Coleman Parkes Research, China is leading the world in adopting generative AI, with 83% of Chinese respondents using the technology, surpassing the global average of 54% and the U.S. at 65%. A UN report revealed China filed over 38,000 generative AI patents from 2014 to 2023. As articles show, Nvidia’s quick ascent in the stock market underscores the extent to which chip quality and availability could dictate the winners in the generative AI era. But there’s another aspect to measuring early leads in the space. In China, which is angling to produce its own chips or get more from Nvidia and other sources, no dominant generative AI contender to OpenAI has emerged yet among dozens of Chinese tech titans and startups.

You might wonder, what does this mean for us actuaries? McKinsey reports estimate that the total generative AI potential for the global economy is $4.4 trillion. Given that the insurance industry is knowledge-based and involves processing unstructured types of data, it is a great place to adopt generative AI.

For this article, I had a discussion with Miguel Wong, FCIA, FSA, CERA, FRM, director at Deloitte Actuarial and Insurance Practice (and co-author of The Actuary article “Actuarial Insights on IFRS 17) on developing use cases for generative AI. According to Wong, some companies don’t feel like there is enough justification to enter this space. They are not sure if the adoption of generative AI will bring significant returns.

When asked about potential use cases of generative AI, Wong has encouraged senior management to talk to juniors in the first line since they are typically the ones who compile and analyze data. “The first line could identify the use cases of GenAI by thinking about the pain points in their current operations,” Wong said. Some examples Wong gave about generative AI include real-time refreshes and creating a dashboard for key interest rates and equity.

The McKinsey podcast “Reimagining insurance with a comprehensive approach to gen AI,” states, “Insurance companies are at an inflection point with their generative AI use cases.” Some of the use cases they provided are in the context of marketing, personalization, and underwriting, as well as client engagement and self-service.

When discussing the potential drawbacks of adopting GenAI, Wong pointed to Air Canada’s AI chatbot case. According to an article in Forbes, Air Canada’s AI-powered chatbot hallucinated a wrong answer to a question about bereavement fares that was inconsistent with airline policy. The Tribunal in Canada’s small claims court found the passenger was right and awarded them $812.02 in damages and court fees.

When considering generative AI’s use cases, I would encourage consideration of the “Shinkansen effect,” which had a transformative impact on Japan’s high-speed bullet trains, known as Shinkansen, on transportation and society. If you want a train to go 10 km/h faster, you just add more horsepower to the engine. But if you need to go from 150 km/h to 300 km/h, you have to think outside the box because you won’t get there within a few modifications.

I believe one needs to start from the drawing board with a whole new way of thinking, and that is how I approach generative AI.

Blockchain and the Internet of Things

Blockchain is a decentralized digital ledger that securely stores records across a network of computers in a way that is transparent, immutable and resistant to tampering. Each “block” contains data, and blocks are linked in a chronological “chain.”

The Internet of Things (IoT) refers to the collective network of connected devices and the technology that facilitates communication between devices and the cloud, as well as between the devices themselves. Thanks to the advent of inexpensive computer chips and high-bandwidth telecommunication, billions of devices are now connected to the Internet. This means everyday devices like toothbrushes, vacuums, cars and other machines can use sensors to collect data and respond intelligently to users.

Wong pointed out that, currently, blockchain and IoT are used for agriculture and crop insurance to track where there are natural catastrophes. They are also used for car insurance to track driving habits to better determine insurance rates. Three examples:

1. Allianz, a European multinational finance services company, uses blockchain to streamline international auto insurance claims. Allianz says this reduces time and costs spent on administration and settles claims faster for customers.
2. Chainlink is a decentralized Oracle network that can send and receive off-chain data and apply it to smart contracts. This can make insurance agreements up-to-date and tamper-proof. For example, in the case of a catastrophic weather event, Chainlink can pull relevant weather data for use on a provider’s smart contract to verify and automate damage payouts.
3. After conducting a health and life insurance study, Deloitte found that blockchain technology could be used to protect health records, complete agreements via smart contracts and help detect fraudulent claims. This integration could potentially nurture relationships with patients and customers.

Blockchain could also potentially be leveraged in other areas to do the following:

• Streamline back-office operations
• Improve capabilities to deter and detect fraud by providers, claimants or applicants
• Upgrade the reliability of provider directories for health plans
• Simplify and shorten the insurance application process and make it more customer-friendly
• Support the formation and growth of online insurance exchanges and alternative forms of insurance, such as peer-to-peer coverage groups
• Facilitate near-real-time health status monitoring and more dynamic pricing and interactive services by insurers

With the shift in demographics, tech-savvy millennials are the next generation of insurance company clients. They might prefer an online marketplace over traditional distribution channels. Blockchain might reduce application time for an online marketplace, speed up underwriting, centralize customer records and efficiently recommend products. It might also be able to improve the security of information.

Additionally, blockchain’s ability to pull together different data sources at any point in a transaction and enable data analysis could potentially increase insurers’ ability to detect, identify and mitigate fraudulent activity. In health insurance, for instance, certain providers may have billed multiple insurance companies incorrectly; however, each insurer may have had only limited instances of the activity and, therefore, not enough data to understand if the bills were honest mistakes or potential fraud.

Once all the payers’ information is combined, trends could be easier to detect to expose potential fraud. By adding analytics to blockchain, organizations can study their own data and that of the broader market to build models that could automatically detect systemic waste or fraud, and share their findings with other payers.

I believe our profession is poised for a revolutionary transformation. From the integration of GenAI to blockchain, actuaries are being called upon to adapt, innovate and lead in ways never before imagined. The revolution in the actuarial world is just beginning, and the possibilities are both exciting and boundless.

Statements of fact and opinions expressed herein are those of the individual authors and are not necessarily those of the Society of Actuaries or the respective authors’ employers.

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