FRAUDSIM: A fraud control optimisation tool for readjustment to the new normal

The importance for our society to reduce criminal profit and deter future generations from finding financial crime as a rewarding lifestyle is one of the key aspects that we are envisioning to contribute with this project. Criminals leave a fingerprint of their activity in the financial institutions on their financial records. Unfortunately, financial data is very constrained by customer privacy regulations such as GDPR. This hampers the possibility of collaboration between different stakeholders in financial problems such as optimising fraud controls tools and reducing financial crime. Solutions based on Machine Learning (ML) are starting to arise, but the quality data required to properly train the models is not available.

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 to rapidly adjust to this new normal.

We address these problems using advanced financial simulation. Our innovation is called FRAUDSIM, and it enables enhanced and rapid deployment of machine learning for financial crime analytics. Our solution creates digital synthetic twins of financial data especially enriched for improving machine learning fraud controls. FRAUDSIM captures the fraud dynamics and combines them into tailor-made scenarios that explore diverse threats that financial organisations are exposed. Our simulator outputs augmented non-confidential financial synthetic data, resulting in trustable datasets ready-to-use for solution providers of advanced financial crime analytics. With FRAUDSIM, we will be able to rapidly develop, benchmark and compare advanced solutions based on machine learning across the industry.

These enriched synthetic datasets are the new oil for ML engines to tackle not only the fraud control optimisation problem, but also a diverse set of complex problems currently present in the interaction between government regulators, financial firms, academia and third-party providers.