What is synthetic data, and how are insurers using it?
Advances in generative AI have enhanced the accuracy of synthetic data, which makes it a valuable tool.
By 2027, as many as 40% of the AI algorithms used by insurers will integrate synthetic data in order to ensure fairness within their processes and comply with regulations, a recent report from IDC FutureScape predicted.
Synthetic data is created artificially through computer simulation or generated by algorithms as either an alternative or supplement to real-world data. This data allows AI models to mirror real-world scenarios while also protecting the privacy of customers.
According to TechTarget, to create synthetic data generative AI models “use a set of training data to learn the statistical patterns and relationships in the data and then use this knowledge to generate new synthetic data that’s similar to the original data.”
Currently, about 18% of enterprises integrate synthetic data, notably to help insurers comply with privacy regulations and facilitate secure data exchanges. IDC notes that advances in generative AI have enhanced the accuracy of synthetic data, which makes it a valuable tool.
“Across various sectors, AI is revolutionizing processes and driving transformative changes. In the insurance industry, AI adoption is reshaping operations, from enhancing risk assessment with synthetic data to optimizing sales through generative AI-based tools,” Surya Narayan Saha, Research Manager, Financial Insights, IDC Asia/Pacific, said in a release.
“Integrating AI with emerging technologies like trusted data exchange and digital twins promises significant advancements, such as improved customer experiences and reduced operational costs,” Saha continued. “As AI becomes ubiquitous, businesses are leveraging its capabilities to innovate and stay ahead in the competitive landscape.”
Outside of its use in protecting customer privacy, the benefits of synthetic data include:
- Organizations are able to customize synthetic data to specific conditions that can’t be obtained with authentic data.
- Synthetic data is often more cost-effective than real-world data.
- Since organizations don’t have to wait for actual events to occur in order to collect specific data, synthetic data allows for data sets to be created in a much shorter amount of time.
Synthetic data is not without its negatives, however. TechTarget notes that synthetic data does lend itself to inconsistencies when trying to replicate the complexity of original data, and lacks the ability to replace authentic data outright.