Artificial intelligence can help financial institutions with fraud detection, anti-money laundering, product optimisation, and more. But when it comes to financial data, how can we train an AI and preserve data privacy and security? Enter Federated Learning.
“Federated Learning allows training a model on your own data without sharing the data with someone else; you just share updates to a global model. The data stays on each client’s premises – in the case of a bank, down to a single branch – to avoid any risks, but the system allows every participant to leverage their collective intelligence to train the global model. It’s a win-win: everyone is better off by collaborating, while ensuring data security and privacy at the same time,” said Prof. Radu State, head of the Services and Data Management (SEDAN) research group at SnT, and principal investigator of the research project in federated learning for PSD2-compliant data analytics, an initiative launched this week in partnership with LUXHUB.
In this framework, SnT and LUXHUB will work together to create added value, service-based financial data. The partnership will implement ground-breaking technology in the field of artificial intelligence, while respecting the industry’s main concern: the safety and privacy of sensitive data. Experts from FinTech/ICT research and the financial sector will be working together on a federated learning model for the benefit of the entire financial service industry.
The research will consist of two research projects, and aims to develop a platform and service based on federated learning for business cases specific to LUXHUB. The first project will focus on the design and management of the platform, while the second project will test the models against concrete financial use cases, such as fraud detection, anti-money laundering, loan risk prediction, and transaction categorisation.
“Machine learning provides financial institutions the flexibility they need to dynamically detect novel fraud attempts, instead of blocking transactions that fall within certain static rules,” says State. “Federated learning, in addition to respecting data privacy, also allows the use of data from all over the world, in full respect of the different national jurisdictions, whereas sharing financial data across countries, even within the same bank, is not allowed,” he adds.
“LUXHUB is all about fostering innovation and collaboration. That is what we have been doing since our very inception, being created by four major banks to mutualise their PSD2 compliance efforts. Collaboration is deeply rooted in the company’s DNA,” underlines LUXHUB’s Chief Operating Officer, Claude Meurisse. “Leveraging data and the knowledge of SnT researchers, this project with LUXHUB on federated machine learning is highly innovative aiming at identifying illicit financial activity by enabling shared learning, but without any risk in sharing data. The project outcomes have tremendous potential allowing the design of solutions that leverage data across several stakeholders in a secure and compliant manner,” said Director of SnT, Björn Ottersten.
On SnT’s side, the project is led by State’s research group SEDAN, which has a long-standing experience in developing FinTech solutions, especially in the areas of anti-money laundering and know-your-customer technologies – one of the centre’s latest spin-offs, digitalUs, started from a project within the group. They will also analyse the meaning and the outcomes of this project in relation to its potential for FinTech at large. “Developing a federated learning platform will also have spillover effects onto other areas of FinTech. Research is about opening doors, oftentimes some you didn’t even know existed,” concludes State.