How AI is reshaping insurance underwriting

In the continuously evolving industry landscape, the integration of artificial intelligence (AI) has emerged as a transformative force, particularly in the realm of underwriting. For insurers, AI technologies offer unparalleled opportunities to enhance risk assessment, standardize and streamline processes, and ultimately optimize decision-making. As carriers navigate this digital frontier, it becomes paramount to understand the nuances of AI. 

Over the next decade, AI and its related technologies are expected to have a seismic impact on all aspects of the industry – from distribution to underwriting to pricing and claims – according to McKinsey & Company.  Looking at the potential impact to underwriting alone, integrating AI and automation into the underwriting process has the potential to generate savings of up to $160 billion over a span of five years as reported by Accenture. This projection is based largely on the AI's capabilities to eliminate non-core tasks and administrative responsibilities that are traditionally handled by underwriters.

In the past five years, AI adoption has already more than doubled – across industries,  and investment in AI continues to increase. In a December 2022 McKinsey Global Institute survey of 1,492 respondents, those who reported the most significant gains from AI adoption—20 percent or more EBIT (earnings before interest and taxes)--tend to employ advanced AI practices, use cloud technologies, and spend efficiently on AI. And they are more likely than others to engage in a range of AI risk mitigation efforts.

The evolution of underwriting with AI

Traditionally, insurance underwriting has relied heavily on manual processes and historical data analysis to evaluate risk and determine premiums. This approach, however, can be time-consuming, prone to human error, and limited in its ability to adapt to dynamic risk factors. In addition, the process includes a major relationship element, which at times can lead to human bias playing a role in outcomes.

Enter AI.  

Equipped with advanced algorithms and machine learning capabilities, it is already changing the face of underwriting for early adopters across the industry. AI produces more consistent underwriting results while taking any potential human bias out of the process completely.

AI algorithms can analyze vast amounts of structured and unstructured data with unprecedented speed and accuracy. This includes demographic information, claim histories and more. By synthesizing this data, AI can uncover intricate patterns, correlations, and risk indicators that human underwriters might overlook. Consequently, insurers can make more informed decisions, leading to more accurate pricing, reduced losses, and enhanced profitability (and this can ultimately lead to lower premium costs for consumers).

Evolving role of human underwriters

An increasing number of organizations are integrating AI tools into their underwriting processes, although human oversight remains integral. While machines run their designated tasks, seasoned human underwriters meticulously review the results to guarantee precision. Companies that have embraced AI in underwriting, report significantly expedited procedures. 

Of course, AI is only as good as the data -- and the people using it. AI is not here to replace people, but rather enhance the work they do. Underwriters taking advantage of AI capabilities are already becoming superstars within their organizations, which also applies to any professional leveraging AI, across all industries. 

In the short term, there are certain products that insurance carriers will not allow AI to underwrite. Human underwriters are, therefore, becoming specialists in these products. In certain states, carriers exhibit more caution when considering AI adoption due to its volatility concerning state-specific rules and regulations. In these circumstances, a human underwriter continues to drive the entire underwriting process.

AI in insurance underwriting: The pros

There are distinctive advantages of using AI in insurance underwriting, which include: 

Standardization and speed: Establishing standardized processes and protocols ensures consistency and reliability in data analysis and decision-making. 

Risk assessment and pricing: AI algorithms analyze diverse data sources to assess an applicant's risk profile accurately. By incorporating a broader array of variables, such as lifestyle choices or behavioral patterns, AI can offer personalized pricing reflecting individual risk levels more accurately.

Customer segmentation and targeting: AI-driven analytics enable insurers to segment their customer base more effectively based on demographics, behavior, and preferences. This facilitates targeted marketing efforts and personalized product offerings, enhancing customer satisfaction and retention.

Automation and efficiency: AI streamlines underwriting processes through automation, reducing manual workload and operational costs. Routine tasks, such as data entry, risk assessment, and policy issuance, can be expedited with minimal human intervention, enabling underwriters to focus on more complex cases.

Considerations for carriers: While the benefits of AI in insurance underwriting are undeniable, several considerations must be addressed for successful implementation:

Data quality and privacy: The efficacy of AI algorithms hinges on the quality and integrity of the data that is analyzed. Carriers must ensure data accuracy, relevance, and compliance with privacy regulations, in order to maintain trust and mitigate regulatory risks.

Transparency and explainability: AI-driven underwriting models should be transparent. Carriers must be able to articulate how AI algorithms arrive at decisions, ensuring fairness and accountability in the underwriting process.

Ethical considerations: As AI becomes increasingly integrated into underwriting practices, carriers must navigate ethical dilemmas surrounding data usage, algorithmic bias, and discrimination. Proactive measures, such as bias mitigation techniques and ethical guidelines, are essential to uphold ethical standards and social responsibility. 

Talent and expertise: Building AI capabilities necessitates a skilled workforce proficient in data science, machine learning, and domain expertise. To cultivate the requisite skills and knowledge base of its employees, carriers should consider investing in talent development initiatives and strategic partnerships.

The future

Carriers aiming to harness AI in underwriting should establish an AI governance group, which should include members from both the legal department and product teams. This collaborative effort ensures proactive management of AI implementation, enabling the organization to stay ahead of disruptive changes in the industry. Additionally, it is prudent to engage in networking and attend industry events where AI is anticipated to be a prominent topic of discussion, if not the central theme of the event, to stay informed of the latest trends and information.

AI represents a paradigm shift in insurance underwriting, offering carriers new opportunities to hyper-personalize offerings, enhance risk assessment, streamline processes, and drive profitability. For example, AI can offer the ability to make recommendations inside of the application process — think cross-selling opportunities. There's also a major opportunity to streamline and optimize the process of weeding out aging and orphaned policies. 

AI can help organizations better understand the agent in the field, putting product recommendations at their fingertips that are a direct match to the customer filling out an application. AI innovation is behind the rise of instant-issue products, where consumers receive immediate notifications of approval or decline upon submitting an application.

By harnessing the power of AI-driven analytics, insurers can unlock new insights, improve decision-making, and deliver greater value to customers. Successful integration of AI, however, requires careful consideration. As carriers embrace AI in underwriting, they should navigate these challenges thoughtfully, leveraging technology to innovate responsibly and sustainably in the digital age.

Roy Goodart, vice president, product management at iPipeline, also contributed to this article.