Leveraging Artificial Intelligence in Actuarial Modeling

Estimated reading time: 5 minutes

In the ever-evolving world of insurance and finance, the role of actuaries has traditionally been one of utmost significance. These professionals have been the backbone of risk assessment and management, employing mathematical and statistical methods to predict and mitigate risks. However, with the advent of Artificial Intelligence (AI), the actuarial profession is witnessing a paradigm shift. AI, with its ability to process vast amounts of data and learn from it, is revolutionizing how actuaries model risks and make predictions. This article delves into the integration of AI in actuarial modeling, exploring its benefits, challenges, and future prospects.

Actuarial science, at its core, is about understanding and managing risk. Traditionally, actuaries have relied on historical data and established statistical methods to predict future events. However, the emergence of AI and machine learning technologies has opened new avenues. AI algorithms, especially those based on machine learning, can analyze complex and large datasets more efficiently than traditional methods. These algorithms learn from data patterns and improve over time, offering more accurate and dynamic risk assessments.

One of the most significant contributions of AI in actuarial modeling is the enhancement of predictive analytics. Machine learning models, such as neural networks and decision trees, can identify intricate patterns in data that human analysts might miss. This capability is particularly beneficial in predicting the likelihood of future claims, determining premiums, or understanding market trends. For instance, in health insurance, AI models can analyze patient records and lifestyle data to predict health risks more accurately.

While the incentive is there for insurers to apply AI to price their risks in a more optimal way and increase their net profits, they are also loath to invest in AI infrastructure or in paying for projects on AI pricing from actuarial consultants generally. Actuarial consultants on the other hand, have no incentive to apply ML pricing when the usual burning cost is all that is required under regulations and because clients pay the same fees whether its normal regulatory pricing or AI enhanced pricing. There is also the extra burden of innovating but then facing the detailed scrutiny of regulator in AI pricing projects and since these products would be new and first time, they might not pass the regulatory scrutiny on first go. A way to break this self-fulfilling loop needs to be found; for example, regulator can take a regulatory sandbox approach to encourage consultants to present their AI pricing in sandbox for regulator’s input without worrying about reputation risk of getting rejected in normal course of regulatory submissions. Insurers also need to understand the importance that AI pricing can bring by increasing net profit and aim to invest more in this area.

AI enables a more personalized approach to risk assessment and product offerings. Through advanced data analysis, actuaries can tailor insurance policies to individual customer profiles. This level of customization was not feasible with traditional actuarial methods due to the sheer computational requirements. AI's ability to process and analyze large datasets allows for more nuanced segmentation and personalized pricing.

Another advantage of AI in actuarial modeling is the ability to process and analyze data in real-time. Traditional actuarial methods often rely on historical data, which might not accurately reflect current trends or risks. AI models, however, can integrate and analyze real-time data streams, such as social media trends, weather patterns, or economic indicators, providing more up-to-date insights for risk assessment and decision-making.

Despite its benefits, the integration of AI in actuarial modeling is not without challenges. One major concern is the "black box" nature of some AI models. The decision-making process in machine learning can be opaque, making it difficult to understand how a model arrived at a particular conclusion. This lack of transparency can be problematic in an industry that relies heavily on trust and accountability.

Ethical considerations also arise, particularly concerning data privacy and bias. AI models are only as good as the data they are trained on. If the underlying data is biased, the AI’s predictions and decisions will likely be biased too. Ensuring data privacy and ethical use of AI is paramount to maintain consumer trust and comply with regulatory standards.

As AI continues to reshape actuarial modeling, it is essential for professionals in the field to adapt. This means acquiring new skills related to data science and AI technologies. Universities and professional bodies are beginning to integrate AI and machine learning into their actuarial science curriculums, preparing the next generation of actuaries for this new landscape.

Typically, our experience in the insurance industry shows that it is possible to develop numerous insurance products, hold countless meetings over several years, and still not implement any of them. This often reflects the prevailing trend among risk-averse insurers, who dominate about 90% of the market in many countries. Such situations can quickly become disheartening. Therefore, it is vital to focus on the few insurers who truly break the mould with their inspiring approaches. ZhongAn stands out in this regard, and here's why I believe they are leaps and bounds ahead in innovation compared to their Western counterparts¹

• ZhongAn's technological infrastructure and expertise enable them to identify demand for new insurance products. They adopt a scenario-based, fragmented approach for launching on-demand products. The time from conception to market launch for a new product is incredibly short, ranging from 5-10 days, with up to 500 product modifications happening weekly. Since its inauguration in November 2013, ZhongAn has accomplished in a decade (up to 2023) what would typically take traditional insurers a century. This is not an overstatement.

• ZhongAn's technology stack and research & development efforts have earned numerous patents and government accolades, alongside significant sales from exporting their technology as Software as a Service (SaaS) to major insurers in the Far East. This is a rare feat in the insurance industry, known for its outdated IT systems. In 2022, ZhongAn processed 9 billion policies with 99% automation in underwriting and 96% in claim approvals. Traditional insurers, handling the same volume, would need tens of thousands of staff for manual operations, whereas ZhongAn operates with about 3,972 employees, nearly half of whom are engineers. The level of automation they achieve using AI, Big Data, Blockchain, and Cloud Computing is virtually unseen, even in Western countries.

• ZhongAn has not just integrated their entire value chain but also focused on targeted ecosystems rather than individual products. This ecosystem approach is ambitious but offers substantial benefits when successful.

• The company operates across four business ecosystems: health, digital lifestyle, consumer finance, and auto insurance. They have an Internet hospital offering integrated healthcare, including outpatient services, and unique insurance products for critical and chronic illnesses like epilepsy, cancer, diabetes, and more, covering a wide range of moderate to mild diseases. Their e-commerce risk protection encompasses a variety of products, from shipping returns to merchant security. They also offer travel-related insurance, phone screen crack insurance, and even wedding insurance, among others. Furthermore, ZhongAn extends its reach to virtual banking and insurance in Hong Kong, asset management, and tech exports to multinational insurers across various countries.

• ZhongAn's integration with the broader system is unparalleled. They collaborate with platforms like WeChat, Alibaba, Ping An, and Grab, as well as offline and online hospitals, clinics, pet stores, fitness providers, and a wide range of group clients.

• This level of innovation and integration sets ZhongAn apart as a leader in the insurance industry, demonstrating the potential for creativity and technological advancement in this traditionally conservative field.

The integration of AI in actuarial modeling represents a significant leap forward in how we understand and manage risks. By harnessing the power of AI, actuaries can make more accurate predictions, offer personalized services, and process real-time data more efficiently. However, it's crucial to navigate this transition thoughtfully, addressing challenges such as transparency, ethical use, and the need for continuous learning. As AI technologies evolve, so too must the actuarial profession, adapting and adopting these tools to maintain its relevance and effectiveness in a rapidly changing world.

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[1] https://www.zhongan.com/channel/public/publicInfo_ndxx2018.html