Tuesday, 11 May 2021, 7:00pm - 8:00pm

Synthetic data can be defined as entirely new data points which resemble, or keep the statistical properties of, the real dataset. In finance its use-case has gained significant interest in its adoption of improving financial crime detection and compliance.

One of the greatest challenges financial institutions face today is to rapidly adapt their control systems in the wake of evolving financial crime. Thus, many have turned to implementing machine learning fraud detection algorithms to play a more effective role in finding the hidden correlations between user behaviour and fraudulent actions. However, these solutions are often constrained due to the lack of quality data and GDPR restrictions in sharing.

This talk will take you through the world of financial crime and compliance, illustrating how synthetic data can become the standard for machine learning, and features a fascinating case study on how this can be applied even to the most complex of financial crimes.


Edgar Lopez Rojas, Founder & CEO, EalaX
Entrepreneur, Researcher and Consultant based in London. International Speaker. Experienced Researcher with a demonstrated history of working in the industry and higher education. Skilled in Computational methods for Security and Financial Crime Analytics. Strong research professional with a Doctor of Philosophy (PhD) in Computer Science from Blekinge Institute of Technology.
I have a PhD in Computer Science and am currently working as a financial crime analytics consultant and researcher at the company called EALAX in the UK. Previously a post-doc researcher at NTNU with more than 18 years of combined industrial and academic experience.
For about 9 years, I have been a researcher in the topic of simulation for financial crime analytics with more than 20 scientific publications and around 150 citations. I developed a method to synthesize financial transactions to perform effective fraud analytics. These methods have the advantage over traditional methods that they have the possibility to measure the impact of fraud and the counter measures in fraud control.
I have an eager interest in addressing the problem of financial crime and I am seeking to improve my knowledge and skills in the financial crime domain that help me to progress and develop my career.

Daniel Turner-Szymkiewicz, Data Scientist, EalaX
Graduate of BSc Pharmacology and MSc Neuroscience with an interest in the analysis and refinement of synthetic data through machine learning in the field of financial crime. Currently working as a Data Scientist for EalaX in the application of machine learning models on synthetic data in fraud detection. His other interests and pursuits are; explainability in machine learning models to address bias and fairness, application and viability of synthetic data to create robust clinical trial models, and neuro-pharmacological and immuno-pharmacological targeting of brain haemodynamics and metabolism in the treatment of neurodegenerative disease.

You can find out more about EalaX and the team via their website, ealax.com


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Last updated 11th May, 2021 at 10:26am