About

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.

SHORT BIO

Dr Edgar Lopez-Rojas (The inventor of FinCrime Vaccines) has a PhD in Computer Science from BTH in Sweden. He is the founder of FinCrime Dynamics (UK). This company specialises in Simulation for Financial Crime Analytics using Synthetic Data to solve financial firms' challenges in benchmarking their controls. He has been working on innovative products for AML Compliance and Optimisation with the FCA, leading AML vendors and financial firms in the UK for the past three years. Previously he was a post-doc researcher at NTNU with more than 20 years of combined academic and industrial work experience in software development and data analytics in corporations and startups across the Netherlands, Sweden, Norway and the UK.

Dr Lopez-Rojas is an international speaker on Fighting Financial Crime using advanced analytics in several scientific and professional venues worldwide in the past five years, including top conferences such as Security and Privacy and prestigious universities such as Oxford University and King’s College. He has been a visionary and a mentor at the FCA TechSprint in 2020 and 2021.

As a researcher, he developed a method to synthesise financial transactions to measure the performance and effectiveness of finCrime analytics. These methods have the advantage over traditional methods in that they can quantify the impact of the countermeasures in FinCrime Analytics. He made available the PaySim simulator for the scientific community in Kaggle, and the code is available on GitHub.

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Financial Synthetic Data is the New Oil for FinCrime Analytics

Financial Data is significantly constrained by customer privacy regulations such as GDPR, which hampers the possibility of collaboration between different stakeholders in financial problems such as optimising Anti-Money Laundering (AML) tools and reducing financial crime. Solutions based on Machine Learning (ML) are on the way, but unfortunately, the quality data required to train the models is unavailable. The three most significant drawbacks of using ML for AML are lack of 'labelled data', the 'imbalance class' of misbehaviour financial activity and finally, the evolving threat of finCrime that makes 'training datasets obsolete'. These drawbacks are derived from the unknown ‘hidden crime’ problem. We address this problem by creating digital synthetic twins of financial data significantly enriched for advanced solutions based on machine learning. We add to our models the known crime dynamics specially tailored to match undesirable scenarios in our financial ins

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