Privacy-preserving personalised federated learning financial fraud detection

Financial institutions increasingly utilise AI-based applications to enhance fraud detection. However, in today's highly interconnected world with higher access to information and technology, fraudulent activities are also becoming increasingly sophisticated. Thus, models trained only on local historical data may struggle to identify such complex transactions effectively. To address this challenge, the institutions may share their data with each other. However, such data sharing activity is constrained by regulatory compliance and institutional trust requirements. We propose adopting Federated Learning (FL) with Privacy Enhancing Technologies (PET) as a state-of-the-art solution to bolster fraud detection capabilities while addressing concerns related to data privacy and competition. Financial institutions face the dual mandate of improving fraud prevention and maintaining the security and privacy of customer transaction data. FL offers a path to achieve these goals by enabling collaborative model training across multiple financial organisations without the necessity of sharing sensitive transaction details.