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Daniel Faggella

The AI in Financial Services is for non-technical financial services leaders looking to find AI opportunities, build an AI strategy, and deliver ROI.

Each week, Emerj AI Research team (and CEO Daniel Faggella) interview leaders from global financial services firms and unicorn AI startups to get to the heart of AI ROI - uncovering what’s working and what’s not working for FinServ leaders.

Visit our advertising page to learn more about reaching our executive audience of AI adopters in the world’s largest banking and insurance firms: https://emerj.com/advertise

AI in Financial Services Podcast

Navigating the complex world of AI hiring in financial services is no easy feat. In this debut episode, Nirmal Ranganathan, Vice President of Engineering and AI at Rackspace Technology, joins host Matthew DeMello to explore the unique challenges financial services enterprises face when integrating AI talent. With the sector still in its early adoption phase, generative AI’s impact is evident, but leveraging these innovations requires addressing fundamental infrastructure and regulatory hurdles. Throughout the episode, Nirmal shares his insights into the practical applications of generative AI, from enhancing customer-facing operations to transforming back-office workflows. The conversation highlights the essential role of data governance, the impact of AI bias, and the transition smaller financial institutions must make to fully digitize and prepare their data foundations. If you’ve enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

Hiring in AI for Financial Services - with Nirmal Ranganathan of Rackspace

Today’s guest is Nirmal Ranganathan, Vice President of Engineering and AI at Rackspace Technology. Rackspace, headquartered in San Antonio, Texas, is a global cloud computing company with additional offices across North America, Europe, Asia, and Australia. It operates data centers in key locations including Amsterdam, London, Frankfurt, Hong Kong, and various cities in the United States. Nirmal returns to the show to explore the accelerating journey of AI adoption in financial services. Pulling from Rackspace’s recent AI survey, we discuss key findings: 86% of companies report tangible AI benefits, and 60% are transitioning from ideation to early implementation. However, as organizations race to integrate AI, many still face skills gaps, with a majority seeking talent in machine learning and software development. If you’ve enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

Driving AI Growth in Financial Services - with Nirmal Ranganathan of Rackspace

The Emerj team discusses insights from our AI in Banking Vendor Scorecard and Capability Map report. Which banking functions are banks eager to automate with AI? Which functions are sucking up all of the venture capital funding? Which AI capabilities are all hype? Emerj CEO Daniel Faggella and lead analyst Raghav Bharadwaj break down what our data tells us about the future of artificial intelligence in banking in this episode of the AI in Banking Podcast.

Analyst Deep Dive: The Future of AI in Banking - With Daniel Faggella and Raghav Bharadwaj

Customer experience is what wins, according to Lee Smallwood, COO Markets and Securities Services at Citi. In this episode of the AI in Banking Podcast, we speak with Smallwood about how fintech companies are already using AI to improve the customer experience and how banks will likely have trouble competing with them on that front. 
 
Smallwood, however, also talks about how banks can leverage their existing assets to retain market share in functions in which fintech companies don’t have the resources to compete, such as asset management. Learn what banks should be thinking about when it comes to developing winning AI strategies to compete with fintech startups.

Banks vs Fintechs: The Battle for Customer Experience - With Lee Smallwood

Banks have several challenges to AI adoption. One of them is their legacy IT and data infrastructures which aren’t built or organized for machine learning. Nishant Chandra, PhD, Senior Director of Data Products at Visa, outlines how banks can transition their legacy systems to systems ready for AI.
 
Chandra discusses the importance of leadership teams in cultivating a company culture necessary for adopting AI with any success, and he gives examples of times when he’s seen old systems hold a company back from adopting AI and when he’s seen leadership do the same.
 
Discover how banks can overcome some of the pitfalls to AI adoption in this episode of the AI in Banking Podcast.

How Banking Leaders Can Overcome the Challenges to AI Adoption - With Nishant Chandra

According to Sergey Gribov, Partner at Flint Capital, “banks are the ultimate commodity.” Fintech startups can offer banking services with leaner teams and better customer experiences using artificial intelligence. But banks need to differentiate themselves somehow from not just fintech companies, but other banks, especially as they decide which banking functions they want to focus on in the coming decade. Banks will almost certainly become leaner operations to stay in business, and they’ll need to make strategic decisions that direct their future. 
 
In this episode of the AI in Banking podcast, Gribov offers a frank picture of the banking landscape in the coming years and how banks will need to respond to it.

How to Survive the Dangers of Fintech and AI Disruption - With Sergey Gribov

We interview the former Head of Artificial Intelligence at HSBC about how banks should go about developing not one AI strategy for their business, but multiple AI strategies for each business function, each department at a company. He also discusses the importance of the Chief AI Officer in steering the company toward success with AI.
 
How are banks experimenting (or not) with AI now, and what do they need to transition to the kind of experimentation that will actually deliver value with AI in the coming few years? Find out in this episode of the AI in Banking Podcast.

Why Banks Need a Chief AI Officer - With Ian Wilson

This week, we speak with Gunnar Carlsson, co-founder of Ayasdi, one of the better-known anti-money laundering AI vendors and one of the highest ranking in our AI in Banking Vendor Landscape report. Carlsson pioneered his own branch of mathematics that eventually became the backbone of Ayasdi's AI software.
 
We speak to Carlsson about methods of calibrating and understanding risk at banks, new sources of data and algorithmic approaches (and what they mean for banks), and the difference between supervised and unsupervised learning. Both machine learning techniques can be used to solve business problems in unique ways.
 
Lastly, we discuss with Carlsson something of critical importance in risk-related functions: knowing your customer.

How Banks Will Win With Unsupervised Learning - With Gunnar Carlsson

This week, we speak with Adam Hunt, CTO of RiskIQ, about how cybersecurity is shifting in the world of banking and financial services and why AI will be necessary to handle those changes. Fraud and cybersecurity startups selling to banks have raised more than vendors for any other business function, and we talk about why AI is such a good fit for cybersecurity in banking.
 
Hunt also discusses how banks can secure AI talent in banking. Working on cybersecurity at a bank may not be as flashy as working on self-driving cars or working for Amazon, but there are things banks can do to attract AI talent they'll need to build and maintain cybersecurity systems.

How to Prepare for the Evolution of AI in Cybersecurity - With the CTO of RiskIQ

This week on the AI in Banking podcast we interview Ohad Samet, CEO of TrueAccord, about the applications of artificial intelligence for debt collection. We discuss personalized messaging, collection forecasting, and the debt collection AI lessons that might be applied to other finance use-cases.
 
This is the third episode in our month-long focus on risk-related AI functions in banking.
 
 
 
Subscribe to the AI in Banking Podcast:
 
https://emerj.com/ai-in-banking-podcast/
 
 Related article:
 
 https://emerj.com/ai-sector-overviews/artificial-intelligence-applications-lending-loan-management/

Machine Learning for Debt Collection and Personalized Messaging - with Ohad Samet of TrueAccord

Today’s guest is Enate Founder and CTO, Kit Cox. Enate makes a comprehensive AI-enhanced business orchestration platform to empower organizations to deliver exceptional services with precision and efficiency. Kit returns to the program to discuss specific use cases that showcase the potential of AI in enhancing customer interactions at critical points in financial services. At the point of sale, AI technologies like sentiment analysis, email triage, and data analysis streamline customer engagement. At the point of need, AI tools play a crucial role in fraud detection, lost card scenarios, and handling complex insurance claims, showing how automation can improve responsiveness and accuracy in high-stakes moments.

Point of Sale and Point of Need Use Cases in Financial Services - with Kit Cox of Enate

Point of Sale and Point of Need Use Cases in Financial Services - with Kit Cox of Enate

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