By Kevin Levitt, Global Business Development, Financial Services, NVIDIA
Financial institutions are using AI-powered solutions to unlock revenue growth opportunities, minimise operating expenses, and automate manually intensive processes. Many in the financial services industry believe strongly in the potential of AI. A recent survey by NVIDIA of financial services professionals showed 83% of respondents agreeing that AI is important to their company’s future success. The survey, titled ‘State of AI in Financial Services’, also showed a substantial financial impact of AI for enterprises with 34% of those who replied agreeing that AI will increase their company’s annual revenue by at least 20%.
The approach to using AI differed based on the type of financial firm. Among fintechs and investment firms, the most cited AI applications were algorithmic trading, fraud detection, and portfolio optimisation. This reflects a primary focus on protecting and maximising client returns. In contrast, banks and other financial institutions noted fraud detection, recommender systems, and sales and marketing optimisation as their top AI use cases. Consumer banks not only focus on fraud detection and prevention, but also build AI-enabled applications for customer acquisition and retention along with cross-selling and up-selling personalised products and services.
From capital markets to consumer finance to fintechs, AI is powering the future of finance. Traders are using AI and high-performance computing (HPC) to accelerate algorithmic trading and backtesting, while meeting industry regulations through explainable models. Fintechs and traditional banks are transforming the delivery of financial services across services and products—such as banking, lending, insurance, and payments—with AI-enabled solutions. And AI is improving productivity for financial institutions through virtual agents in call centres and automated analysis of lengthy financial documents.
In this article, we’ll highlight the role of AI across capital markets, retail banks, and fintechs with real-world examples of AI in action.
Building the AI-powered bank
AI is enabling incumbent financial institutions to deliver smarter and more secure services to their clients and customers. Take Royal Bank of Canada (RBC), for instance. RBC built a private AI cloud for banking to run thousands of simulations, train AI models, and analyse millions of data points in a fraction of the time than it could before. The private AI cloud has helped reduce client calls and resulted in faster delivery of new applications for RBC clients. As a result, RBC expects to transform the customer banking experience with a new generation of AI-enabled smart applications.
Firms are also using AI solutions to create robust fraud detection and prevention systems, accelerate risk calculations and fraud detection. BNY Mellon, one of the world's largest cross-border payments service providers that processes more than $1 trillion daily, built a collaborative fraud detection framework that runs Inpher’s secure multi-party computation — which safeguards third party data. The bank’s ML and AI models were trained on over 100 million data samples, and improved fraud prediction accuracy by 20%, while preserving the privacy and residency of the input training data.