{"id":14828,"date":"2022-02-03T17:16:00","date_gmt":"2022-02-03T16:16:00","guid":{"rendered":"https:\/\/www.uni.lu\/snt-en\/?post_type=news&p=14828"},"modified":"2025-02-25T16:14:44","modified_gmt":"2025-02-25T15:14:44","slug":"snt-luxhub-partner-to-provide-open-finance-with-federated-learning","status":"publish","type":"news","link":"https:\/\/www.uni.lu\/snt-en\/news\/snt-luxhub-partner-to-provide-open-finance-with-federated-learning\/","title":{"rendered":"SnT & LUXHUB Partner to Provide Open Finance with Federated Learning"},"content":{"rendered":"\n
\n

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<\/i> preserve data privacy and security? Enter Federated Learning.<\/p>\n\n\n\n

\u201cFederated 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\u2019s premises \u2013 in the case of a bank, down to a single branch \u2013 to avoid any risks, but the system allows every participant to leverage their collective intelligence to train the global model. It\u2019s a win-win: everyone is better off by collaborating, while ensuring data security and privacy at the same time,\u201d said Prof. Radu State<\/a>, head of the Services and Data Management <\/a>(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<\/a>.<\/p>\n\n\n\n

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\u2019s 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. <\/p>\n\n\n\n

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.<\/p>\n\n\n\n

\u201cMachine learning provides financial institutions the flexibility they need to dynamically detect novel fraud attempts, instead of blocking transactions that fall within certain static rules,\u201d says State. \u201cFederated 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,\u201d he adds.<\/p>\n\n\n\n

\u201cLUXHUB 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\u2019s DNA,\u201d underlines LUXHUB\u2019s Chief Operating Officer, Claude Meurisse<\/a>. \u201cLeveraging 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,\u201d said Director of SnT, Bj\u00f6rn Ottersten<\/a>.<\/p>\n\n\n\n

On SnT\u2019s side, the project is led by State\u2019s 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 \u2013 one of the centre\u2019s 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. \u201cDeveloping a federated learning platform will also have spillover effects onto other areas of FinTech. Research is about opening doors, oftentimes some you didn\u2019t even know existed,\u201d concludes State.<\/p>\n<\/div><\/section>\n","protected":false},"excerpt":{"rendered":"

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.<\/p>\n","protected":false},"author":335,"featured_media":14829,"template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false},"news-category":[4],"news-topic":[],"organisation":[184],"authorship":[335],"acf":[],"yoast_head":"\nSnT & LUXHUB Partner to Provide Open Finance with Federated Learning - SnT I Uni.lu<\/title>\n<meta name=\"description\" content=\"Artificial intelligence can help financial institutions with fraud detection, anti-money laundering, product optimisation, and more. 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