Cyber threats and data breaches are on the rise, particularly in financial services. To safeguard institutional assets and customer information, the banking sector is seeking innovative solutions, with one promising avenue being the integration of Artificial Intelligence for enhanced security, especially in fraud detection. Fintech Review spoke with Ittai Dayan, Co-founder and CEO of Rhino Health, to learn more.
Rhino Federated Computing Platform: Revolutionizing Data Collaboration
Rhino’s Federated Computing Platform (FCP) enables data scientists and AI developers to work with data without actually possessing it. By combining Edge Computing (EC) and Federated Learning (FL), FCP facilitates streamlined data collaboration while keeping the data behind owners’ firewalls. This allows financial institutions to unlock the value of previously isolated data, perform queries, analyses, and deploy predictive models on inaccessible data, all while maintaining data sovereignty and reducing storage costs.
The Journey of Rhino and Its Founder
Ittai Dayan, a former physician and researcher, initiated Rhino with the aim of breaking down data silos in healthcare. Having experienced the potential of digital transformation in healthcare, Dayan transitioned to academic medicine, leading AI deployment at Mass General Brigham’s Center for Clinical Data Science. Witnessing the power of FL in a consortium with Nvidia and international institutions, Dayan recognized its broader potential, leading to the formation of Rhino. The company initially focused on healthcare but now offers its technology to developers in various industries, including financial services.
Understanding Federated Learning and Its Relation to Edge Computing
Federated Learning involves training a model on decentralized data, allowing for collaboration while maintaining data privacy. This distributed machine learning technique retains the raw data on local devices, sending only the model parameters to a central server for aggregation and updating. Edge Computing, on the other hand, processes data closer to its source, intersecting with FL as a critical step to ensure proper data analysis and preparation. The Rhino Federated Computing Platform seamlessly combines these technologies, providing a comprehensive solution and overcoming challenges often associated with DIY approaches.
Potential Applications for Financial Institutions
Federated computing holds numerous applications for financial services, including smarter credit scoring, collaborative fraud detection without sharing specific transaction details, anti-money laundering pattern identification, risk assessment models for portfolio management, and streamlined trade settlement processes.
Predicting the Future of Fraud Detection and Data Privacy
As financial transactions continue to evolve, the scalability of AI is essential for keeping up with the growth and sophistication of transactions. Federated Computing presents an elegant solution, allowing robust fraud detection models while preserving customer privacy and complying with regulations – addressing the conundrum of balancing AI potential and privacy concerns.
Exciting Innovations in the Fintech Landscape
Looking ahead, the rise of ‘regtech’ excites Rhino, as it offers innovative solutions for fraud detection, systemic risk mitigation, and improved back office operations. These solutions address privacy concerns associated with AI and provide a cost-effective alternative to manual processes, benefiting all participants in the financial ecosystem. The company’s success in healthcare technology deployment bodes well for its traction in the financial services sector.
