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3 Ways Synthetic Data is Revolutionising Financial Crime Compliance ./
An introduction to the opportunities and risks of synthetic data for financial crime compliance.
Synthetic data has been hailed by analysts, commentators, and vendors as the next great accelerator of AI for financial crime compliance.
At first glance, it appears to be a silver bullet for some long-standing obstacles to AI model development and testing. It promises to provide a cost-effective, efficient, and accurate alternative to “real” data, which can be expensive to source, labour intensive to manage and patchy or inconsistent.
But, as ever with AI, the whole story is not quite as simple as the hype.
Learn 3 ways synthetic data can revolutionise financial crime compliance, if used in the right way.
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"AI is all about pattern detection, so when a model gets a hit for money laundering it has a risk of ‘overlearning’ everything about that transaction. As a result, biased and discriminatory outcomes are possible because they’re based on flawed data, which is inadequate and unrepresentative of the populations from which they are drawing inferences.”
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- What is synthetic data?
- How synthetic data...
- ...accelerates model testing and training
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- ...can mitigate bias
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- ...promises to transform financial crime compliance collaboration