The Question of AI Bias in Life & Annuities Insurance

Introduction 

Artificial Intelligence – AI – is everywhere.  It’s the power behind voice assistants, self-driving vehicles, and robots that can perform tasks as varied as cleaning offices and harvesting crops. Its applications in life insurance are just as diverse, from underwriting policies and assessing risks to predicting mortality rates. It would seem like the sky’s the limit for AI usage in life insurance, but like most technologies, AI has its downsides, particularly in the inherent biases present in the data used to train AI models. If unchecked, these biases can lead to unfair and discriminatory outcomes, potentially disadvantaging certain individuals or protected classes.  

Understanding AI Bias 

 AI models alone do not have any inherent biases, but if an AI model is trained on data with these biases, it is likely to perpetuate and amplify them in its decision-making process. Such biases can manifest in various ways, including:  

• Unequal pricing. AI algorithms may potentially set higher premiums or rates for certain demographic groups, based on their association with higher risks according to historical data. For example, if a specific racial or ethnic group historically had higher mortality rates, the AI model might unfairly assign them higher premiums, even if individual risk factors differ. 
• Inadequate coverage. Bias in training data can lead to some individuals being denied coverage altogether or having limited access to insurance products. This can occur when certain groups are considered higher risk solely due to their association with specific regions or socio-economic backgrounds. 
• Lack of inclusivity. Biased AI models might not cater to the unique needs of diverse customer groups. For instance, a biased model might not effectively consider the needs of individuals with certain health conditions, leading to inadequate annuity options for them. 

Mitigating AI Bias 

Before AI bias can be rectified, it must be detected. AI models need to be trained with the right data to eliminate bias. One way is to ensure a balanced training dataset or at least reduce the imbalance in the available dataset. By including data from different demographic groups and regions, the AI model can make fairer predictions. Insurance companies should also conduct regular audits and tests to assess their AI algorithms for bias and equity. This involves monitoring outcomes and reviewing decisions to identify and address discriminatory patterns. In addition, insurers should prioritize the use of transparent and explainable AI models, which can provide insights into how decisions are made. This allows for better scrutiny of the algorithm’s outputs and helps build trust with customers. And while AI can significantly streamline processes, human oversight is still the ideal way to ensure fairness and address potential biases that AI might not recognize, by identifying and rectifying errors and/or discriminatory outcomes. 

 The National Association of Insurance Commissioners (NAIC), in the United States, has working groups and committees in place that are regularly tackling the topic of AI in a variety of forms, most notably the Accelerated Underwriting Working Group which was created by the Life Insurance and Annuities Committee, as well as the Big Data and Artificial Intelligence Working Group under the Innovation, Cybersecurity, and Technology Committee.  

Sapiens will continue to keep our finger on the pulse of evolving AI usage in the life insurance space. 

The Final Word 

In the life insurance ecosystem, AI bias remains a major challenge. Evolving business models and their underlying data will constantly change, making it impossible to build a perfect training dataset. Only by embracing responsible AI development can carriers develop insurance products are accessible, affordable, and equitable for all. 

Learn more about how Sapiens powers transformation for life insurers in North America and EMEA and APAC.   

• AI
• Artificial intelligence