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 Crime Vaccines: A Consortium Approach to Tackle Financial Crime Using Trustworthy AI and Synthetic Data

Financial crime continues to pose a significant threat to the global financial system, and traditional methods of detecting and preventing these crimes are often inadequate. In recent years, the use of artificial intelligence (AI) has emerged as a promising solution to this problem, but it requires high-quality data to be effective. This is where financial crime vaccines come into play. Financial crime vaccines use synthetic data to train AI systems, creating a more robust and effective defence against financial crime. Using synthetic data, financial institutions can train AI models to identify and prevent financial crime without exposing sensitive customer information. This makes financial crime vaccines a safe and secure way to fight financial crime. But the creation of a financial crime vaccine is a complex task requiring multiple financial institutions' collaboration. This is why a consortium is necessary to develop and deploy financial crime vaccines successfully. Financial c...

Synthetic Data: The Future of Data Privacy and Security

In today's data-driven world, companies and organisations across industries constantly collect, analyse, and utilise data to inform their decisions and strategies. However, using real-world data can present various challenges, particularly in data privacy and security. This is where synthetic data comes in as an innovative solution that balances data privacy and data utility. In this blog post, we'll explore the importance of synthetic data and why it should be considered a best practice in various industries, focusing on personal identifiable data and GDPR. What is Synthetic Data? Synthetic data refers to artificially generated data that mimics the statistical properties of real-world data. It can be created using various techniques such as generative adversarial networks (GANs), agent-based simulations (ABM), variational autoencoders (VAEs), and other machine learning algorithms. The resulting synthetic data can be used in place of real-world data in various applications, su...

Measuring the Effectiveness, Efficacy, and Efficiency of Financial Crime Controls with Financial Crime Vaccines

Financial crimes such as money laundering, fraud, and cybercrime pose a significant threat to both individuals and financial institutions. To effectively detect and prevent these crimes, it is essential to have robust financial crime controls in place. These controls range from manual review processes to advanced artificial intelligence (AI) systems. Financial regulators are increasingly cracking down on financial institutions with poor Anti-Money Laundering (AML) controls. This is because AML is a critical part of financial crime prevention, and regulators are responsible for ensuring that financial institutions take the necessary measures to detect and prevent money laundering. The lack of effective AML controls can result in significant financial losses for institutions and harm the financial system's reputation as a whole. This is why financial regulators are motivated to issue significant fines to institutions with poor AML controls. Using synthetic data to test the effective...

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