Resources
The latest insights on synthetic data and financial crime prevention
What synthetic AML testing actually measures
Synthetic AML testing makes it possible to measure AML system performance objectively using known ground truth. This article explains what is measured, how results should be interpreted, and why this approach enables clearer evidence than production data.
Why production data is the wrong foundation for AML testing
Most AML testing relies on production data that was never designed for testing or performance evaluation. It is legally sensitive, difficult to access, historically biased, and severely label-scarce. As a result, it produces weak and often misleading evidence about how AML systems actually perform.
This piece explains why production data is the wrong foundation for AML testing, why the problem becomes more acute with modern and AI-based models, and how synthetic datasets with known ground truth enable objective, repeatable evaluation without relying on production data.
FCA: Authorised Push Payment Synthetic Data
Read about our Authorised Push Payment (APP) fraud synthetic data, which covers individual and business identities, transactions, telecom data, and fraud to improve detection.
FCA: Using Synthetic Data In Financial Services
Comprehensive summary of the use cases for synthetic data across financial services.
Gartner: Market Guide for Data Masking and Synthetic Data
Privacy and test data use cases have been addressed by niche data masking controls for years. As it matures, the data masking market is consolidating controls into data security platforms. This research helps security and risk management leaders understand the market evolution for data masking uses.
FCA: Synthetic Data to Support Financial Services
Call for input on the use of synthetic data across financial services.