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Challenges

Winning the battle against financial crime

Regulators are increasingly expecting firms to monitor and report on the effectiveness of their financial crime systems. But combating money laundering is an enormous task and comes with substantial costs and risks.

01

Historical data is not enough

Current approaches to financial crime analytics are limited to historical data, which is not rich enough to develop better controls.

02

Evolving fraudulent behavior

The static nature of today’s controls means they often fail to detect the adaptive behavior of criminals.

03

Data privacy issues

It’s difficult to procure and get approval to use real transaction data due to privacy laws like GDPR.

04

High rate of false positives

Rules-based transaction monitoring systems result in a high number of (expensive) false positives.

Simulation for financial crime analytics
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Our Solutions

Agent-based financial crime analytics

Agent-based simulation has proven to be indispensable to tackling several real-world challenges for preventing and detecting financial crime.

01

Generate synthetic data

Create synthetic data that contains no personal customer information and is completely compliant with privacy regulations.

02

Reduce false positives

Use labeled data to generate information about missing crime (false negatives) and calculate the real reduction of financial crime.

03

Self-evaluate your system

Use simulation to add increasingly complex scenarios of criminal behavior and suspicious activity, then evaluate your controls.

Simudyne is ground breaking technology that is currently being leveraged across Barclays and enables us to model multiple scenarios on huge data sets, so we can understand our risk, exposure and options.
Jes Staley
Chief Executive Officer at Barclays Bank
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