AI Podcast Interviews Articles and Reports

Since the advent of online banking services, customers have had several different ways of communicating with their banks. Banks need to monitor all of these incoming customer requests and respond to them in the most efficient way possible. Further, each of the various channels of communication represents a valuable way to segment customers to not only improve how they perceive a bank’s brand but also to market banking products to them better.

Although there are established use-cases for AI applications in the business world, the claims that AI vendors make about returns from their software are often exaggerated. What is also not apparent amongst the AI hype is that adopting AI and machine learning is far more challenging than it might seem.

This week we speak with David Carmona, General Manager of AI at Microsoft. Carmona discusses how redefining a business process is a very different kind of AI adoption project than working on something that is horizontal.

Our research indicates that AI applications for risk-related banking functions are more numerous than applications for other business areas. Fraud and Cybersecurity, Compliance, Loans and Lending, and Risk Management collectively made up 56% of the AI vendor products in the banking industry, as shown in the graph below:

We interviewed Jay Budzik, CTO at Zest AI, about the business value of machine learning for auto lending. We speak with Budzik about how underwriting, lending, and credit scoring is evolving as a result of advances in machine learning - both in terms of new data sources, and more advanced algorithms.

Artificial intelligence is transforming a variety of banking functions and allowing tech startups to compete with some of the largest banks for market share of key services, including lending and wealth management. Business news and media sites have been heralding the downfall of the banking industry as we know it because fintech companies are going to feel comfortable leveraging AI long before banks.

Have you ever read a blog post or a whitepaper and heard the terms "data science" or "predictive analytics" used in ways that aren't quite right? As it turns out, terms like these are often used incorrectly, but by the end of this episode of the AI in Industry podcast, you'll have greater clarity about five key terms in AI and data science that are sometimes overused in conversations about AI in the enterprise.

This week, we speak with arguably one of the best-known folks in the domain of neural networks: Jurgen Schmidhuber. He's working on a lot of different applications now in heavy industry, self-driving cars, and other spaces.