Global Technology Company
Validating Financial Crime Models
Accessing real-world transaction data with accurately labelled criminal activity is extremely challenging, which makes model validation slow and expensive
At an FCA TechSprint focused on combating financial crime, a global technology leader participated with the goal of showcasing the potential of machine learning to detect illicit financial activity. Facing strict regulatory limits on data sharing, the team needed a way to train and test advanced models, but without using real-world financial data.
By leveraging Syntheticr Ecosystem, they rapidly developed and validated powerful, multi-institution financial crime detection models in a fully privacy-safe environment. The outcome was a compelling prototype and a clear demonstration of how synthetic data can unlock innovation in compliance and risk analytics.
Financial institutions and technology providers are increasingly called upon to collaborate on anti-money laundering (AML) initiatives. Yet, sharing or accessing real transaction data poses significant legal, ethical, and logistical challenges—especially in a fast-paced setting like a TechSprint. There were three key challenges for the team:
Model Effectiveness
Develop machine learning models capable of identifying suspicious transactions where the criminal actors intentionally spread their operations across the financial ecosystem.
Data Privacy
Comply fully with data protection laws (e.g., GDPR) by avoiding the use of real or sensitive data.
Demonstration-Ready
Present a working solution to a large public audience, including senior figures from regulators and financial institutions, within a few days.
Syntheticr Ecosystem provided a rich structured dataset of simulated financial transactions, including embedded money laundering activity executed by sophisticted peer-to-peer networks. This enabled the team to:
Develop Models Quickly
Train supervised and unsupervised learning models using labelled, realistic transaction flows.
Test Detection Capabilities
Validate the models on diverse scenarios without requiring any access to real-world financial data.
Showcase Innovation
Build a compelling, regulator-friendly demonstration of next-generation AML technology.
As a result of using Syntheticr Ecosystem, the team successfully delivered an award-winning prototype capable of detecting complex financial crime patterns. The solution demonstrated highly accurate detection using fully synthetic data and a ‘Compliance by design’ approach with no risk of exposing personal or financial information.
The use of Syntheticr Ecosystem during the FCA TechSprint also proved that synthetic data isn’t just a workaround, it’s a critical enabler for agile and compliant financial innovation.
Whether you’re prototyping, training, or validating your models, Syntheticr helps you move faster.