Artificial intelligence makes money laundering difficult

Thema:
Artifical intelligence

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

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.

Encryption technology

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.

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Educating AI

Informatietype:
Insight
27 September 2022

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

Informatietype:
Insight
27 September 2022

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

Informatietype:
Insight
27 September 2022

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?

David Deutsch on the development and application of AI

Informatietype:
Insight
27 September 2022

Peter Werkhoven, chief scientific officer at TNO, joins physicist, Oxford professor, and pioneer in the field of quantum computing, David Deutsch, for a virtual discussion. Deutsch set out his vision in 1997 in the book, The Fabric of Reality. Together, they talk about the significance of quantum computing for the development and application of AI. Will AI ever be able to generate ‘explained knowledge’ or learn about ethics from humans?

Georgette Fijneman on the promise of AI for health insurers

Informatietype:
Insight
27 September 2022

Hanneke Molema, senior consultant healthy living at TNO, interviews Georgette Fijneman, CEO of health insurer Zilveren Kruis since 2017. Both look at the same topic, health, from a completely different perspective. What is the promise of AI for one of the Netherlands’ largest health insurers?