FinCrime Dynamics wins second Innovate UK grant for £65k

It is a pleasure to announce that FinCrime Dynamics (formerly Ealax Ltd) has secured a second grant from Innovate UK for £65k. The grant will help provide funding for the project CP-Mark and further develop our innovation in the area of finCrime analytics. This grant follows our first Innovate UK grant of £100k for the FRAUDSIM project.

With the current COVID-19 pandemic, crime has evolved to a new normal and so has the behaviour of non-fraudulent people. Evidence of this is the rise in digital ecommerce and the types of fraud where there is no presence of the card holder.  These problems require new ways for control systems to rapidly adapt to this reality, which will demand a corresponding benchmark that can appropriately measure the performance of transaction monitoring systems, which is one of the biggest challenges financial institutions face today.

The granted project is called “CP-Mark: A conformal prediction benchmark for measuring the performance of transaction monitoring systems”.

Financial institutions tune their control systems according to applicable regulations, which carries two clear objectives: detect and prevent as much criminal activity (increasing true positives), and reduce the number of innocent people wrongfully accused (reducing false positives). Financial institutions’ efforts to achieve these goals are hindered, mainly because of their inability to adequately assess the actual amount of hidden crime present in their datasets, rendering conventional metrics with little benefit.

This project will allow us to evaluate the use of Conformal Prediction (CP-Mark) as a reliable benchmark tool to test the effectiveness of our software and other Machine Learning algorithms used as part of transaction monitoring systems.

We will then perform benchmarks on several controls using these datasets with a state-of-the-art machine learning framework called conformal prediction to build predictive models capable of detecting known and currently undiscovered patterns of fraud.