The AI-Powered Bank The key to successful AI implementation is a full-stack solution that includes both hardware and software—AI as a platform. This implementation of a shared, centralized infrastructure for AI consolidates expertise, productivity, and scale; shortens the life cycle from development to deployment; and drives down total cost of ownership with efficient utilization of compute and storage resources. Enterprises that successfully embrace AI as part of their operation get an additional benefit that sets them apart—they become the kind of organization that attracts the world's best talent. The people who lead AI innovation come to companies that offer these tools and scale, enabling them to do their life's most important work. View eBook: Building the AI-Powered Bank (PDF 14.3 MB) Watch Webinar: AI-Enabled Data Science Platform for Finance (56:35 minutes) Read Article: RBC Builds an AI-Powered Private Cloud (July 2020)
Fraud Detection Some of the biggest AI wins are those related to fighting transaction fraud for banks and credit card companies—a multi-billion-dollar problem. Detecting true fraud is critical, but traditional systems have historically generated many more false-positive than true-fraud signals. Now, advanced machine learning and deep learning techniques with solutions like NVIDIA Triton™ Inference Server are improving detection and, at the same time, drastically cutting false-positive rates. AI is revolutionizing multi-trillion-dollar industries like financial services and powering growth around the world. From PayPal to American Express to Ping An, firms are leveraging AI to improve customer outcomes, reduce costs, and combat fraud. Read E-Book: Natural Language Processing (NLP) for Fraud Detection Watch Webinar: Deep Learning Powers Better Decision Making in Finance (63:16 Minutes)
Enhanced Customer Service Conversational AI is enabling consumers to manage all types of financial transactions, from bill payments and money transfers to opening new accounts. By offering these self-service interactions, banks can free customer service agents to focus on higher-value interactions and transactions. At the heart of conversational AI are deep learning models that require significant computing power to train chatbots to communicate in the domain-specific language of financial services. Once those models are trained, the bots need to be able to engage in life-like conversations with customers in real time. This low-latency performance, as well as the compute power needed to train the deep learning models, are enabled by NVIDIA GPUs. Watch Session: Democratizing Conversational AI with Square Assistant (26:33 minutes)
Personalized Banking Offers On some of the largest commercial platforms, recommendations account for as much as 30 percent of revenue, which can translate into billions of dollars in sales. That’s why banks and insurance companies are using recommender systems to drive every action consumers take, from visiting a webpage to prioritizing which debt to pay off first. Recommenders also increase conversion by providing personalized messages to consumers, improving customer loyalty and satisfaction with the bank. NVIDIA Merlin™ is an end-to-end recommender-on-GPU framework that provides fast feature engineering and high-training throughput to enable fast experimentation and production retraining of deep learning models. Merlin also enables low-latency, high-throughput, production inference to deliver personalized customer interactions with speed and accuracy. Read Blog: NerdWallet’s Recommendation Engine (April 2020)