The Future of Actuarial Science

As we enter an era where technology is evolving exponentially and the impact of climate change is felt in everyday life, it is critical for actuaries to reflect on how the profession might change in the coming years. This article explores areas that could be considered hot topics, where actuarial involvement is likely to increase significantly over the next five to 10 years.

Climate Change: The Urgency

There is growing scientific consensus that climate change contributes to extreme weather events increasing in frequency and severity. According to a report by the Intergovernmental Panel on Climate Change (IPCC), global warming is likely to exceed 1.5°C above preindustrial levels by 2100.¹ Climate-related risks to health, livelihoods, food security, water supply, human security and economic growth are projected to increase in the coming years.

Climate change-related risks could cause significant financial losses for individuals, companies and the financial institutions that invest in and insure them. See Figure 1 for some recent examples.

Potential for Greater Actuarial Involvement

Many insurers and banks are beginning to develop climate-related stress tests and other risk management tools to understand and manage their exposure to climate-related risks better. Actuaries potentially could play a role in designing an overall climate risk management framework for companies.

The process of designing a climate risk management framework would vary by company, but in general, the following steps could be implemented:

1. Identify climate risks: Identifying the specific climate risks most relevant to the company, such as extreme weather, sea level rise, a shift in consumer behavior or policy changes.
2. Assess exposure: Actuaries might be able to develop a framework to assess the company’s exposure to the identified risks by analyzing the balance sheet and the geographical and sectoral exposure to climate risks.
3. Develop risk management strategies: Actuaries can assist in implementing strategies to mitigate, manage and monitor the risks, such as diversification of investments, designing products and underwriting or incentivizing sustainable practices.

Banks and insurance companies have recognized the importance of managing climate-related risks. Swiss Re has been actively managing climate risk by focusing on specific areas, such as the protection gap for natural catastrophes.⁶ Major banks like HSBC⁷ and JPMorgan Chase⁸ have highlighted a climate strategy focused on reducing carbon emissions and solutions for long-term sustainability.

The Road Ahead: More Statistical Analysis?

Statistical analysis is used today in several areas to simulate and predict future climate-related changes and disasters. Actuaries can anticipate becoming more involved in areas where they may not have worked traditionally:

• Climate scenario analysis: There may be a need to model the impact of scenarios, such as an increase in global temperatures by 1.5°C to 4°C. Actuaries can test an organization’s resilience against extreme weather events or government policy changes.
• Quantification of a company’s carbon footprint: Actuaries can analyze progress against targets for reduced emissions and make sure they are aligned with global targets.
• Alternative investments: Actuaries can evaluate investments such as climate derivatives, which may hedge against changes in government policy or extreme weather events. Other investment options include renewable energy sources or green bonds, financing projects that are less vulnerable to climate-related risks.
• Analysis and prediction: Actuaries may use regression analysis to study relationships between variables, such as temperature and precipitation.
• Remote sensing: Satellite and aerial data may be used to study the impact of climate change.
• Paleoclimate analysis: Actuaries may use data from ice cores, tree rings, lake sediments and other sources to study past climate patterns.

Working in these areas could potentially help improve an organization’s customer loyalty and brand reputation while driving long-term shareholder value.

Artificial Intelligence and Machine Learning

Banks and insurance companies have been automating their business processes for years, but the increasing use of artificial intelligence (AI) and machine learning (ML) likely will accelerate this trend toward end-to-end automation.

There are many reasons for the significant rise of AI in insurance, including the following:

• Growing customer acceptance: An increasing number of insurance customers are accustomed to receiving computer-generated advice for insurance products.
• Improved efficiency: The application of AI to automate processes within underwriting and claims management could result in significant future cost savings for insurers.
• Fraud detection: In a 2022 study, the Coalition Against Insurance Fraud reported that insurance fraud could cost consumers $308.6 billion⁹ annually in the United States. AI-based fraud detection systems potentially could reduce these losses.

Overview of Potential AI Applications in Insurance

I believe actuaries can play a critical role in partnering with AI to provide more creative and cerebral analysis and help companies optimize their profits. There are many ways AI techniques can be applied in finance. For example, in product development, ML techniques can help banks and insurance companies identify gaps in their existing product offerings and develop products that are better tailored to customers’ needs. A specific AI technique that can be applied is clustering, which is an unsupervised ML technique that involves grouping similar data points based on their characteristics. This can help identify groups of customers, such as millennials or retirees, who may be interested in a specific product or service. It also can be used to develop targeted marketing campaigns for each group.

Other ML techniques, such as decision trees, random forests or neural networks, can be used for customer behavior analysis. For example, decision trees can be used to model the impact of different life events on a customer’s financial needs and identify the most suitable product.

An end-to-end product development process driven by AI may involve the following steps:

• Concept phase: Insurers can use natural language processing (NLP) or ML techniques to analyze customer feedback and social media posts and identify customer needs and preferences.
• Pricing phase: Reinforcement learning can be used to optimize the product price based on customer demand and market conditions.
• Launch phase: ML can personalize the product offering around customer preferences.
• Support phase: ML algorithms can help analyze customer data and provide personalized recommendations, including additional products or services, to complement the customer’s existing policies. Chatbots can be used to provide customer support.

In claims processing, multiple AI techniques could be used to automate the workflow, making it faster and more efficient. Actuaries also can develop models to predict the expected payout for different claim types and enable smoother decision-making. For example, a neural network can analyze historical data to identify patterns of fraudulent activity and flag suspicious transactions in real time. A random forest can be used to predict the likelihood of an insurance claim being fraudulent based on a combination of factors such as the claimant’s age, occupation and past claims history.

NLP techniques can be used in underwriting to quickly extract information such as dates, names, locations, diagnoses and so on to streamline the underwriting process by automating the risk assessment and pricing of insurance policies. Actuaries can help identify the relevant risk factors and develop models that accurately predict the risk associated with different policyholders.

In investment management, AI can improve decision-making by analyzing large and complex data sets to identify patterns and trends. Actuaries can help develop ML models that accurately predict the performance of different investments and identify new investment opportunities.

AI also can be applied in credit risk management. Decision trees, which are ML algorithms used to classify data based on criteria, can influence whether a loan application should be approved or denied based on the applicant’s credit history, income and other factors.

Another AI application in finance and insurance is a support vector machine, which is a ML algorithm that can be used for classification and regression analysis. Support vector machines can be used for credit scoring, fraud detection and portfolio optimization. For example, a support vector machine can predict the likelihood of a borrower defaulting on a loan based on their credit score, income and employment history.

As explained, actuaries can apply their expertise in statistical analysis, risk management and financial modeling to use these emerging AI technologies. This could potentially help drive innovation and improve profitability and customer satisfaction.

Cybersecurity: The Threat

Cybersecurity risk is the risk of an organization’s exposure to harm or loss resulting from the misuse or abuse of technology by malicious actors. Cybersecurity is likely to remain a relevant topic that requires attention given the following factors:

• Cost of security breach: A study by IBM found that the average data breach cost for financial institutions in 2022 was $4.35 million.¹⁰ This was an all-time high, with the average cost in the United States being $9.44 million. The study also found that it takes an average of 249 or 323 days—for companies with and without security AI and automation, respectively—for a financial institution to identify and contain a data breach.
• Increasing risk of cyberattacks: Apart from the average cost per incident, the number of cyberattacks on financial institutions also has increased in recent years.¹¹ In addition, the complexity of the attacks is on the rise. The underlying cryptography and technology are evolving rapidly, requiring antihacking professionals to keep up with the latest developments.
• Increase in the amount of sensitive data financial institutions are holding: The amount of personal data held by financial institutions is increasing exponentially.¹² This includes customer names, addresses, Social Security numbers and financial account information, partly due to know your customer (KYC), anti-money laundering (AML) and other regulatory obligations.

Banks and insurers are particularly vulnerable to cyberattacks given the vast amount of sensitive personal and financial information they hold. This data is a prime target for cybercriminals, who could use it for fraud, identity theft or other crimes.¹³ See Figure 2 for examples of what three organizations are doing in response to this threat.

Opportunities for Actuaries

In my view, to enhance their cybersecurity, companies will need a multifaceted approach covering the following areas:

• Systems: Firms should invest in advanced cybersecurity technologies to optimize security measures. These can help the company’s systems detect and prevent cyberattacks or improve the response time.
• Training and Recruitment: Prioritizing training and awareness or hiring skilled professionals aware of best practices to detect and respond to cyber threats can enhance a company’s cybersecurity.
• Risk management: Companies should develop comprehensive risk management frameworks that address cybersecurity risks, implement controls to mitigate risks and develop incident response plans.

I believe actuaries can play a significant role in these measures, exploring risk management and quantitative techniques such as the following:

• Threat modeling and vulnerability scanning: Actuaries can identify security requirements, pinpoint security threats and evaluate potential vulnerabilities, quantify the threat, and prioritize remediation.
• Penetration testing: Assessing an organization’s security through an authorized simulated attack can identify potential vulnerabilities that real attackers could exploit.
• Quantitative risk analysis: Actuaries can use numerical values to estimate the probability and potential impact of different threats. Combining these techniques and others can help organizations manage their cybersecurity risks more effectively.

While actuaries traditionally have been involved in managing financial risk, they are well-equipped to contribute to cybersecurity management given their expertise in risk management and financial modeling. Actuaries also could help institutions evaluate the cost-effectiveness of different cybersecurity investments, such as purchasing cyber insurance or implementing new security technologies.

In addition, actuaries could develop innovative techniques, including but not limited to the following:

• Quantum computing for encryption and decryption: Current quantum computing is still in its initial stages and has limited capabilities.¹⁷ Once it is available for wide commercial use, quantum computing has the potential to perform complex encryption or decryption at exponentially higher speeds compared to classical computers.
• Actuarial standards for sensitive data management: Actuaries can help implement best practices from other parts of the profession to establish frameworks and assess and report cybersecurity risks. This also can provide guidelines for protecting sensitive or confidential data and promote adopting robust practices across the financial industry.

Big Data Transformation: The Problem

Several factors contribute to the increasing complexity of data required for business practices, including the following:

• The rise of connected devices and the Internet of Things (IoT) has led to an explosion in the volume of data the insurance industry generates and stores.
• The insurance industry also faces pressure from competitors and consumers to use data more effectively and improve customer experience.

A good example is the increasing use of telematics (a combination of telecommunications and informatics) data in the auto and health insurance industries.¹⁸

In the auto insurance area, such connected devices could include on-board devices, or “black boxes” and “dash cams.” Depending on the type of device, the device would collect information about average speed, maximum speed, acceleration and braking habits, geolocation, distance traveled, time of travel (day or night), number of journeys, crash reports, battery and engine condition, cornering and lane changes.

To mitigate risk, auto insurance companies might consider offering premium discounts based on driving habits. They also might consider offering services that send preventive push notifications or alerts (e.g., in the case of bad weather), travel statistics reports and safe driving recommendations. Auto insurers also could consider providing roadside assistance in case of a crash or car theft and help with placing an emergency call after a vehicle crash.

The use of telematic devices in health insurance could include wearable bracelets and other fitness trackers, mobile phone applications and smartwatches. These devices would collect data such as heart rate, blood pressure, blood oxygen level, activity data (e.g., sports or step counter), hours of sleep, geolocation, food and water consumption, calorie consumption and glucose level.

To prevent and mitigate risk, health insurance companies could offer rewards for members who demonstrate healthy habits. They also could provide health activity reports and diabetes management programs to help members keep their chronic conditions under control, medical assistance services in case of an accident and a safety alarm for older adults.

Actuaries: Data Transformers

The potential for creative partnerships likely will increase for actuaries who are well-versed in programming, ML or managing big data. In my opinion, data transformation projects should bring actuaries, with their specialized knowledge in risk management, into a project early because they are more than just users of data. Aspiring actuaries who also pick up new technology-inspired skills likely will be in demand.

Conclusion

Companies likely will need to hire a combination of actuaries, climate analysts and technology experts to best use AI automation or other transformation within finance. The future is full of opportunities to increase actuaries’ influence within organizations or industries where they have worked traditionally. In my view, actuaries who can understand both the business side and the technology aspects of a problem soon will become critical for companies to reduce their costs and keep up with the competition.

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.

Copyright © 2023 by the Society of Actuaries, Schaumburg, Illinois.