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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
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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

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

Fighting Financial Crime with Synthetic Data​: The Financial Crime Vaccine

Just as vaccines protect our physical health from harmful viruses, synthetic data can protect our financial well-being from financial crimes. In the same way that vaccines use a small, harmless sample of a virus to build immunity, synthetic data uses real-world financial data to create a realistic, yet safe environment for AI systems to learn and detect fraudulent activity. Financial crime, such as money laundering, fraud, and cybercrime, is a growing concern for individuals and institutions alike. These crimes not only harm the victims, but also undermine the stability of the financial system. Traditional methods of detecting and preventing financial crime, such as manual review and rule-based systems, are becoming increasingly ineffective as criminals become more sophisticated. One example of financial crime is the use of mobile money services to launder money, which is a problem that financial institutions are facing globally. One way to tackle this problem is to use synthetic data

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

Simulation is our telescope to unveiling the fraud in the financial universe

Two years ago, I once said: “Simulation is my telescope to explore the universe of financial fraud” just a few days ago, this comparison came back to my mind when the first ever picture of a black hole was achieved through a coordinated effort of more than 200 scientists and observatories all around the globe. “I can think of myself as an astronomer, eager to learn more about the universe, but without a proper tool or telescope, it would be hard to learn more than I can see with my eyes. Simulation is my telescope to explore the universe of financial fraud.” Dr Edgar Lopez-Rojas (interview at Kaggle 2017). Some years ago, I published a synthetic dataset in Kaggle that contained a generated scenario from a real dataset sample for a Mobile Money Payments system in an African country. The PaySim dataset was the first synthetic transactional dataset shared openly and released in a known data scientist community platform. This fact led Kaggle to name the PaySim dataset as the “dataset of th