‘India led the rise in demand for actuaries’

Andy Rallis

The outbreak of Covid pandemic two years ago and it’s ongoing effect brought several significant changes around the world and across industries. While it promoted digital usage to ensure continuity, it also pushed the demand for actuaries globally. To understand the need for business professionals who deals with the measurement and management of risk and uncertainty, especially during the uncertain times, Rashi Aditi Ghosh of Elets Network ENN) spoke to Andy Rallis, global chief actuary of MetLife and the immediate past president of the Society of Actuaries.

1. How has the pandemic transformed the actuary space? Is there a radical shift in demand for actuaries that you observe in India?

The pandemic has clearly revealed risks that were unheeded till present. While it has affected sectors across industries, it has impacted all parts of a company’s financial statements and daily operations, making it crucially important to use risk management techniques.

Actuaries are experts in this space- effectively strategizing to manage risks more efficiently. The emergence of Covid-19 has led to a drastic increase in public health issues, making it more imperative for companies to evaluate risks, especially those related to finance, insurance and associated fields as the frequency and size of claims can spike dramatically. Actuaries have been assessing and responding with new ways to mitigate the health, mortality and financial risks posed by this new form of exposure, particularly with respect to the financial security systems and programs that many actuaries are responsible for managing. India has been one of those countries most affected by the pandemic, and it has certainly led to a boom in the demand for actuaries.

2. Do you think the current situation has fundamentally changed insurance and risk analysis? Have businesses realized the need for having more comprehensive models on their side, with risks of climate change and cyber threats now increasing?

The current situation has made insurance and risk analysis easier as well as more challenging. While there have been complexities in risks and their interdependencies, the tools and models that actuaries use have also become more sophisticated. ‘Big data’ and artificial intelligence (AI) have been revolutionizing practices across industries, with their advantages becoming accessible at mere fingertips.

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Today, significant developments have taken the insurance world by storm, including both changes to risks and to risk management. There lies the need of establishing the need of improved business models that will tackle these developments most effectively. The presence of big data and AI can be felt very significantly, especially for their ability to tackle newer, more global issues, like climate change. However, the greater use of technology also poses greater security challenges. Businesses have started initiating these types of change, but change is likely to continue into the indefinite future.

3. What are the key skills and tools that have emerged to be fundamental to being an actuary in the last two years?

Due to the growing need to respond rapidly, actuaries must be much quicker in risk analysis and planning than before. Actuaries already use advanced software to develop new methods of calculating and analyzing data. However, in addition to being required to have strong technical skills and knowledge (IQ), actuaries also need a strong adaptability quotient (AQ) and emotional intelligence quotient (AQ), especially for leadership roles. With accelerated automation, applying a level of emotional intelligence a machine is incapable of while providing insights from machine learning will gain utmost importance. Actuaries must also be adept with tools that have the ability to build, manage and appropriately utilize multiple and complex models that properly assess key components of the data and recognize interdependencies.

4. Is there an improvement in methods that are helping actuaries in forecasting and modelling? Could you identify a few gaps that challenge them?

Today, there is an increasing number of actuaries who are using cross-disciplinary team approaches for modelling and forecasting. Health care professionals, social scientists, data analysts and others work together with actuaries for advancing their techniques through understanding the drivers of behavior and the causes of outcomes. These cross-disciplinary approaches supplement the improvement of algorithms and technology advancements. Involving new people and new tools results in improvements to the process of understanding the nature of risk and ways to model and mitigate, and both will still play a significant role in times to come.

However, there are a few gaps that challenge actuaries, many of which are created by faulty assumptions and understandings. For example, actuaries must avoid the common misunderstanding that correlation between two factors implies the existence of a causation. The causation may have simply been created by a third or fourth factor, with there being no actual relationship between the original two correlated factors. Actuaries also realize the importance of the adage that “all models are wrong; some models are useful”, which specifies that we must remember that models are a tool for decision-making yet are not a substitute for judgement. Actuaries bring to the table the ability to analyze large volumes of data that are instrumental in influencing decisions along with an aptitude to put the decisions in the right business context.

5. Do you feel that decentralization and hyper-personalization could play a positive role in the insurance world? Can there be any roadblocks in the way?

In the insurance sector, hyper-personalization and decentralization enable higher personalization and client experience enhancement, making the entire process more tailored and custom-made. They have proved to be encouraging forces, in terms of their approach to drive positive behaviours, such as improved driving habits for monitored drivers or healthier exercise habits through fitness trackers for health plan participants.

However, these might not be fully implemented within the insurance industry for primarily two reasons. Firstly, insurers (and others) must be cautious so that they do not end up discriminating in their business practices, as high levels of personalization can inadvertently result in discrimination against members of certain demographic groups.

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Secondly, the mathematical reason that insurance can be provided by insurance companies is due to the “Law of Large Numbers”, which states that while outcomes for any single insured are unpredictable, the outcomes for a large group are on average highly predictable. Hyper-personalization can break down the sizes of the insured groups to small enough segments that the outcomes do not remain highly predictable anymore, and the insurer is faced with either increasing prices to account for the uncertainty risk or withdrawing coverage for groups for which the numbers are too small to be credible.

6. Can data transformation initiatives like AI and analytics strengthen underwriting and risk analysis in insurance companies?

Underwriting and risk analysis (in addition to marketing) have been existent since long ago, and more recently have been one of the strongest users and beneficiaries of artificial intelligence and analytics in the insurance industry. The post-COVID world will witness higher investments in building the foundation of data and its transformation uses, along with increased opportunities in the implementation of AI and machine learning. The evolved data ecosystem will result in shorter turnarounds in policy approvals and issuance, leading to a significant improvement of the insurers’ claims outcomes. These techniques have also played an influential role in fraud detection in the underwriting and claims adjudication processes in recent years, and are likely to strengthen these functions in times to come.

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