The digital economy heavily depends on data, but acquiring high-quality, real-world information is often costly, time-consuming, and legally complicated. As artificial intelligence (AI) models advance and rely more on data, this issue has led to a transformative solution: synthetic data.
In 2024, Gartner forecasted that 60% of the data used to train AI models would be synthetic, a significant jump from just 1% in 2021.
This shift is driven by synthetic data's ability to address key challenges, including privacy concerns, data shortages, and algorithmic bias. However, this powerful new resource also introduces notable risks, including potential compromise of AI model integrity and violations of emerging regulations.
A formal Synthetic Data Policy is crucial to harness the full benefits of synthetic data while safeguarding the organization’s reputation, ensuring legal compliance, and upholding the long-term integrity of AI systems.
This podcast dwells into the foundational pillars of drafting Synthetic Data Policy for banks and financial institutions.
You can read it as a blog here: https://kasrangan.substack.com/publish/post/172322173
