The Algorithmic Underwriter: The Ethics of AI in Insurance and Lending
A critical look at how AI is being used to make decisions about loans and insurance, and the profound risk of creating a new, automated form of discrimination.
Introduction: The Black Box Deciding Your Financial Future
For centuries, the process of underwriting—the assessment of risk in insurance and lending—has been the job of a human expert. But this is changing, and it’s changing fast. The insurance and banking industries are increasingly turning to artificial intelligence to make these critical decisions. AI algorithms can now analyze a vast and often unconventional range of data to determine your “risk score,” a number that can affect your ability to get a loan, the price of your car insurance, and even your access to healthcare. The promise is a more accurate and efficient system. But the peril is the creation of a new, automated, and deeply opaque form of discrimination.
The New Data Sources of Risk
The AI underwriter is not just looking at your credit score. It is building a much more detailed and intimate picture of you, using a wide range of “alternative data”:
- Your Social Media Activity: Some insurers are experimenting with analyzing your social media posts to assess your personality and risk-taking behavior.
- Your Shopping Habits: Do you buy a lot of organic food? The algorithm might infer that you are a health-conscious person and give you a lower health insurance premium.
- Your Driving Behavior: Telematics devices in your car can track your speed, your braking habits, and the time of day you drive, all of which is used to calculate your car insurance premium.
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The Problem of “Proxy Discrimination”
The core ethical problem is that these algorithms can learn to discriminate, even if they are not explicitly told to. For example, an algorithm is not allowed to use race to determine your loan eligibility. But it can use your zip code. And if that zip code is a historical proxy for race due to redlining, the algorithm can inadvertently create a discriminatory outcome. This is “proxy discrimination,” and it is incredibly difficult to detect in a complex, black box AI model.
Conclusion: The Need for Algorithmic Accountability
The use of AI in underwriting is a powerful tool for assessing risk. But it is also a technology that is fraught with ethical peril. The lack of transparency, the potential for algorithmic bias, and the use of our personal data in ways we don’t understand are all serious concerns. As these algorithmic gatekeepers take on more and more power over our financial lives, we need a new era of “algorithmic accountability,” with stronger regulations, a commitment to transparency, and the right for every individual to understand and challenge the decisions that are being made about them by a machine.
How do you feel about an AI using your social media posts to determine your insurance premium? Let’s have a critical debate in the comments.
