AI Case Studies: Reimagining Underwriting for the Digital Age

The traditional life insurance underwriting process has remained largely unchanged for decades. But as consumers expectations shift towards instant digital experiences, how can actuaries leverage AI to create a streamlined, data-driven experience that delivers faster decisions without compromising risk assessment quality?

The scenario

XYZ underwrites individual life insurance products. As part of the application process, risk assessment includes financial underwriting and medical underwriting, which have traditionally relied heavily on human involvement, often leading to considerable delays in customer onboarding.

XYZ is investigating an underwriting automation project as one of the key initiatives aiming to improve customer experience. XYZ recognises the opportunity to leverage technological advancements to enhance its market agility and gain competitive advantages by:

• accelerating the onboarding process
• enhancing engagements and refining the customer journey
• improving risk assessment and differentiation; and
• reducing operational costs.

What’s new?

XYZ intends to adopt machine learning algorithms throughout the underwriting process, with major use cases including:

• Guiding applicants through the online application process with virtual assistants to ensure completeness.
• Screening application documents to validate completeness, identify inconsistencies and flag potential fraudulent information with a combination of supervised, semi-supervised and unsupervised learning algorithms.
• Assessing suitability of requested coverage with predictive algorithms based on the applicant’s personal information, financial circumstances and credit history.
• Determining if medical examinations are required and the risk classification of the insured with predictive algorithms based on the insured’s personal information, individual and family medical history, lifestyle and outcomes of medical examinations – if required.
• Providing additional data-driven insights (e.g., profitability score and lapse forecast) to the underwriting team for further references in case of a pricing review or referral.

Why actuaries are relevant

In this project, actuaries would be part of the team leading the strategic initiative through:

• Communicating complex strategic planning to ensure the success of operational change: Working closely with the underwriting team, actuaries have a deep understanding of the challenges of traditional underwriting and how these processes disrupt the onboarding journey. The underwriting and actuarial teams would work together to drive the initial feasibility assessment and propose the initiative to the Board. Actuaries would prepare documentation to demonstrate how AI may be used to streamline underwriting processes in clearly understood examples. Having a deep grasp of the processes would allow actuaries to communicate the business value to the Board more effectively, including synergies, operational efficiencies and positive consumer outcomes. Working across functional teams, actuaries would bring stakeholders together to deliver internal alignment and success for the proposal.
• Actuarial approach to problem solving: While technical skills are crucial for the project, understanding the business context and applying management skills are equally essential so that the scope of the project could be clearly defined and the potential impacts could be fully identified. Actuaries would help underline the importance of a holistic approach to ensure that all stakeholders are involved from the beginning of the planning phase. 
• Providing results-oriented solutions: As a profession responsible for safeguarding the public interest, actuaries would proactively emphasise the critical importance of considering the ultimate customer impacts when identifying automation areas, designing automation strategies and evaluating automation outcomes.
• Leading risk assessments: As risk management professionals, actuaries would fully recognise the risks associated with AI in underwriting, including data privacy, data and algorithm biases, lack of transparency, risk misclassification, lack of human oversight and regulatory compliance. The actuarial team would highlight the risks to the project’s steering committee, leading a more cautious design of the new underwriting approach.

How AI can deliver beneficial consumer outcomes

Consumer outcomes are expected to be improved at the completion of this AI initiative, including:

• Improving the overall customer experience: The turnaround time and time taken for underwriting decisions are expected to be reduced significantly, which could improve customer satisfaction for XYZ with the new underwriting approach.
• Rewarding customers for healthy lifestyle choices: The risk classification algorithms could incorporate lifestyle considerations which are difficult to capture in the traditional underwriting approach. AI could help identify for XYZ those lifestyle factors that may achieve lower premiums for customers. XYZ may consider offering additional discounts on certain products to customers who achieve monthly fitness goals.
• Reducing overall premiums: The new approach using AI could help reduce fraudulent applications and operational costs. The cost saving is expected to be passed on to customers, leading to lower overall premiums. This could ensure a more thriving insurance industry for the ultimate benefit of society.

Looking forward

AI holds significant potential for streamlining and enhancing insurance operations, especially regarding efficiency of processes, accuracy of risk assessments, and consistency of outcomes. However, insurers need to be mindful of associated risks and possible gaps between the use of AI and governance arrangements.

As domain experts, actuaries are uniquely positioned to integrate their deep understanding of risks and actuarial judgement to drive the development of AI use cases and the adoption of AI within the insurance industry. Actuaries are well-placed to proactively adapt to an increasingly automated and data-driven landscape in insurance and collaborate with other functional teams to fully embrace AI.

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