The insurance industry has faced scrutiny for years over “fair bias” practices. Business practices and bias are well-known associates of the insurance industry, leading to marginalized populations.
Some industry experts, including a former insurance commissioner in the US, believe that discrimination will become a major issue in AI regulation. Customer data can easily reveal adverse data, allowing insurance companies to select only the most desirable risks.
What constitutes bad data for insurance businesses?
When building models, training data is crucial. For example, the use of body mass index (BMI) in life insurance demonstrates how a lack of diverse, representative, and high-quality insurance data led to biased assessments. Recent research has shown that BMI does not accurately assess risk for many people, as it was based on a predominantly white male dataset.
Insufficient data can lead to availability bias, resulting in poor outcomes. Since data fuels artificial intelligence, feeding bad data into AI systems will yield inferior results.
What are algorithms and why do they matter?
AI algorithms are step-by-step instructions designed to solve specific problems. Synthetic data generation, using algorithms like machine learning and neural networks, plays a significant role.
Bias: A 4-letter word
Historically, insurers have used zip codes or territory codes to calculate premiums, which can hide bias related to sensitive data like race or gender. An example from Chicago showed disparities in auto insurance premiums based on zip codes, with minority areas paying higher premiums.
If biases are not addressed, vulnerable populations will be further marginalized, especially with the use of AI.
Efforts to promote AI literacy, inclusivity, and trustworthiness have become prominent.
Where does generative AI come in?
Generative AI, particularly synthetic data, can address data concerns like privacy and fairness in insurance. Synthetic data allows for the creation of reliable models without relying on masking sensitive personal data.
A real-world example of synthetic data results
In 2022, a project using synthetic data showed more reliable results than anonymized data, maintaining statistical patterns for advanced analysis. IDC predicts that by 2027, 40% of AI algorithms used by insurers will utilize synthetic data for fairness and compliance.
Synthetic data for insurance: holy grail or AI snake oil?
Synthetic data alone cannot fix all biases, as it relies on original data that may already contain biases. Organizations must acknowledge biases and develop trustworthy AI principles to lead in the industry.
What’s next for synthetic data in insurance?
Insurers can use generative AI models to identify risks, predict outcomes, inform pricing decisions, automate claims processing, improve fraud detection, and enhance customer experiences. Synthetic data can break the cycle of bias in the insurance industry.
By focusing on data quality, the insurance community can reduce bias, protect privacy, and unlock the value of generative AI.
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