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Actuarials to drive Prediction & Personalization

Aditya Mall, the Appointed Actuary at Future Generali India Life Insurance, delves into actuarial work transformation:

Actuarials to drive Prediction & Personalization

Ravi Lalwani: What are the most notable changes in actuarial tables you have seen in your career?

Aditya Mall: Throughout our careers, we have witnessed significant changes in actuarial tables due to advancements in healthcare and improvements in overall well-being that have greatly increased life expectancy. This has led to recalibrations of actuarial tables to accurately reflect the evolving longevity of individuals, resulting in profound impacts on calculations for life insurance death and health liabilities due to shifts in mortality and morbidity curves.

Moreover, there is a growing recognition of the impact of socioeconomic variables on actuarial modelling. Factors such as income, education, and lifestyle choices now play a pivotal role in shaping mortality and morbidity risks. Actuaries are actively incorporating these broader considerations to provide a more nuanced understanding of risk and align insurance products more closely with individual circumstances.

What are the newer sources of data you are looking at for updating actuarial tables?

In the contemporary data-driven landscape, we are diversifying our data sources to enhance the granularity and accuracy of actuarial tables. While traditional sources such as mortality studies and census data remain foundational, we are exploring non-traditional datasets. Electronic health records, data from wearable devices, and insights from social media have proven valuable in capturing a more comprehensive picture of health trends.

The integration of big data analytics enables us to extract deeper insights into multifaceted factors influencing mortality and morbidity. This approach empowers insurers to craft more responsive and tailored products, adapting to the evolving needs of policyholders.

Do you find the need to convert subjective information into objective information?

The conversion of subjective information into objective data is integral to the actuarial role. When assessing health risk, subjective elements such as lifestyle choices are translated into quantifiable metrics through statistical models and algorithms. For example, in the underwriting process, qualitative information from medical reports is transformed into quantitative measures. This objective analysis ensures a fair and accurate determination of insurance premiums, maintaining the integrity of the risk assessment process.

How are you shaping the usage of IT in your organization and in your domain?

As the Appointed Actuary in our current role, we are actively leading the integration of Information Technology (IT) into our organization and related domains. Our focus includes the incorporation of advanced analytics and machine learning algorithms into decision-making at the point of sale. This enhances the precision of risk assessments and enables swift responses to market dynamics.

The utilization of IT extends beyond traditional actuarial functions, encompassing streamlined internal processes, improved customer experiences, and ensuring compliance with regulatory requirements. A robust IT infrastructure is pivotal for maintaining the agility required in the dynamic landscape of the insurance industry.

What are the top 3 areas where AI-ML will make a difference for actuarials?

The transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on actuarial work is evident across 3 crucial areas:

  1. Predictive Modelling: AI & ML empower actuaries to develop more sophisticated predictive models by analyzing extensive datasets. This heightened analytical capability enhances our ability to anticipate trends and risks, contributing to more accurate pricing, assumption settings, and underwriting decisions.
  2. Fraud Detection & Risk Management: AI-driven systems play a vital role in detecting fraudulent activities and managing risks effectively. Through continuous analysis of data patterns and anomalies, underwriters can identify potential fraud and mitigate risks in real-time, reinforcing the overall integrity of insurance operations.
  3. Personalization of Products: AI & ML enable insurers to create highly personalized insurance products. By analyzing individual customer data, preferences, and behaviors, actuaries can design offerings that align precisely with specific needs, fostering greater customer satisfaction and loyalty.

In conclusion, the evolving landscape of actuarial work is characterized by dynamic shifts in data utilization, the integration of IT, and the transformative influence of AI and ML. As we navigate these changes, our commitment remains unwavering to provide accurate, fair, and innovative solutions to meet the evolving needs of policyholders in the ever-changing insurance landscape.


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