The mass is high because image and video synthetic data will be a critical enabler for complex and advanced CV use cases across most industries, and therefore, a downstream impact on their insurers. CV technology is in early mainstream adoption now. The need for relevant, privacy-compliant and comprehensive training data for accurate model training will drive the adoption of image and video synthetic data. CV vendors are adding more capabilities and offerings, use-case expansion, covering the majority of industry verticals to provide the solutions for industry-specific needs.
Image and video synthetic data is most beneficial when:
Real data lacks unusual events or scenarios, known as edge cases. ■
Synthetic data is benefiting CV vendors, in particular, by cutting labeling costs, easy availability of data and fast iteration cycles, hence a faster go-to-market strategy. This has the potential to accelerate training for insurance-specific models for risk analytics, AI- enabled remote inspection solutions and damage estimation. Insurers are also in the unique position of being exposed to the use of synthetic image and video data to commit a range of different types of fraud. In the long term, synthetic imagery could be used to falsify object damage for vehicles and property, or to manipulate facial data to remove signs of smoking, substance abuse or obesity to commit life insurance application fraud. For investment products, synthetic facial imagery could be used to commit more sophisticated early-surrender fraud as well. Fraud analytics vendors, in particular, will need to pay attention to new types of insurance fraud enabled by this technology to help insurers stay one step ahead. Real data is restricted due to personally identifiable information (PII) and other regulatory compliance. ■ Use cases require complex labeling for training a supervised model for higher accuracy. ■
Recommended Actions:
Focus on the emerging applications of image and video analytics by providing synthetic data that is balanced by including edge cases and preserving privacy for training accurate AI models. ■
Educate customers by presenting real-world use-case examples and customer references for the successful application of synthetic data. ■
Gartner, Inc. | G00786204
Page 38 of 48
This research note is restricted to the personal use of abhishek.sharma@fractal.ai.
Made with FlippingBook - PDF hosting