Artificial intelligence makes money laundering difficult
Data exchange plays an important role in the fight against money laundering. But how do banks safeguard the privacy of their customers in this regard? We have developed a solution in collaboration with Rabobank and ABN AMRO: multi-party computation. Using artificial intelligence, banks jointly analyse sensitive data without actually sharing it. Find out how.
Money laundering often involves large amounts of money. But any self-respecting criminal uses multiple transactions at different banks when doing so, often involving a mix of national and international banks. Add to this the fact that criminals also regularly use cryptocurrencies in money laundering. In short, it is clear how difficult it is for banks to get a clear picture of these shady practices.
Multi-party computation (MPC)
The solution to combating money laundering lies in the mutual exchange of data. But, of course, within the limits of the Privacy Act. And yes, it is possible. But you need a good dose of artificial intelligence. In close collaboration with Rabobank and ABN AMRO, we have developed a Multi-Party Computation (MPC) solution. With this, banks perform analyses on shared data via an AI system, nationally and internationally.
Thanks to innovative encryption technology, this can be done in such a way that no person or system can see the data. Banks are using this solution internationally. Privacy and confidentiality of the data remain guaranteed. So, it is an ideal starting point for uncovering money laundering via data analysis.
Richer data sets with AI
The MPC solution also lends itself very well to fraud detection. And as a research tool. The AI system securely links databases containing the most sensitive types of information. This results in richer data sets, which in turn opens up new applications. For example, the possibilities within the healthcare sector with AI.
Alex SangersFunction not known
Christopher BrewsterFunctie:Senior scientist
Christopher Brewster is a Senior Scientist in the Data Science group and Professor of the Application of Emerging Tecnologies in the Institute of Data Science, Maastricht University. His research has focussed on the application of Semantic Technologies, Open and Linked Data, interoperability architectures and Data Governance, mostly to the food and agriculture domains.
Daniël WormFunctie:Senior consultant
Joris SijsFunction not known
Judith DijkFunctie:senior research scientist
Judith is specialised in extracting information from camera images. She now applies the subject of her PhD thesis in Physics, which she obtained 18 years ago, to her work as a research scientist at TNO, including in a research programme on camera systems for the Dutch Ministry of Defence.
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You can read about how AI is educated in Chapter 1. How can we make clear to AI which goals we want to pursue as humans? Andhow can we ensure intelligent systems will always function in service of society?
Innovation with AI
What does that world look like in concrete terms? Using numerous examples, TNO has created a prognosis for the future in Chapter 2. Regarding construction, for example, in which AI will be used to check the quality, safety, and energy efficiency of buildings before they are actually built. Or healthcare, where robots will partly take over caregivers’ tasks and AI will be able to autonomously develop medicines.
Innovating with innovation AI
How AI will change research itself is explained in Chapter 3. For example, what role will AI be permitted to play in knowledge sharing? And what will happen when we make machines work with insurmountably large data sets?
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