Ally All Ears Podcast | AI and its impact on auto retail

In this episode, Sathish Muthukrishnan, Chief Information, Data and Digital Officer at Ally Financial, discusses the impact of AI in the financial services sector, Ally’s approach to AI and how the company is thinking about using AI to support its auto business.

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Emma Hancock: Hi everyone. Welcome back to the All Ears podcast. I'm Emma Hancock, host and strategist at Automotive News. This podcast is sponsored by Ally Financial and produced by the Automotive News Content Studio. In each episode, we explore topics that are important to leaders in automotive retailing. Our guests include experts in their field from Ally, plus dealers from around the country, and we cover tips and explore insights that can help dealerships successfully navigate the transformational changes taking place in our industry.

Today, we catch up with Sathish Muthukrishnan, Chief Information, Data and Digital Officer at Ally Financial. We'll be discussing the impact of AI in financial services sector and of course, what that translates to in automotive retail. With both financial services and auto being heavily regulated, how can the powers of AI work their magic to improve business performance and customer experience? Hi Sathish. Thank you so much for taking the time.

Sathish Muthukrishnan: Emma, thank you for having me. Thrilled to be part of this podcast and talk about all the great work that teammates at Ally are doing to serve our customers.

Hancock: It's great to have you here. So Sathish, talk a little bit about yourself and your role at Ally.

Muthukrishnan: I'm coming in close to four years with Ally. Four exciting years. I would say, as you said, I'm the Chief Information Data and Digital Officer. And what that means is we have a responsibility for defining product roadmaps, defining user experiences in which will bring that product to life for our consumers and dealers. Use technology to build that, run technology, play with data, protect all of technology and data as well as digitize experiences for our internal consumers, internal employees, as well as our external customers.

Hancock: Well, we're hearing a lot about artificial intelligence, AI. What is it? And what's Ally's approach?

Muthukrishnan: We are, aren't we. AI, in fact, you may not know if it is the real Sathish, or an artificial intelligence Sathish talking, especially because it's a podcast. AI has been around for a while. In fact, it's been over 70 years when the first neural network white paper was published. And AI is very simply teaching computers to behave like humans. As humans, we see, read, hear constantly, and that translates into signals into our brain and it gets stored. And then the next time you are given an input where you're expected an answer, you observe that input, you process it and you provide that output whether you're writing it or you're talking. You're asking the computer to do exactly that. And what does a computer do? Get access to all of the data that is available in the world. You think back to the Internet. When the Internet started, we started creating web pages. And those web pages talked about the service the company provided, what products they sold, and they were all structured. You knew exactly what products they sold, what price it was. You could read the description of the product, and there was a contact us section where you could go and contact that company. The companies created websites across the globe, all of those converted into structured data. You also started collecting customer behavior as they interacted with those websites. Those were also structured data because you knew exactly where customers were clicking, what products they were buying, how many they were returning. And then you can translate that into dealerships as well. Customers walking in to purchase a car, inquiring how you are capturing that interaction. All of those were structured data. Then, social media came into the scene. You got introduced to what we call unstructured data because you're no longer talking in pure English. You no longer were structuring your conversations and you were responding back in small texts or words. Think about Twitter. Everybody knows BRB is "be right back." IRL is "in real life." LOL is "laugh out loud." And the communication was not structured. Sometimes it was one way. Sometimes it was two way. Sometimes it was one too many and that created unstructured data. Add to it pictures that you were posting of yourselves, images that were created. Now, we gave all of the structured data, all of the unstructured data that we created over the last decade, and we asked the computer to learn from it. So just like I said how humans observe and learn, the computer started learning, and once we gave all the data, now we provided input to the computer in generative AI terms. We gave it a text and we said, "Can you make sense of this text and give me an output?" And that text could be, "tell me what is the greatest car in terms of performance over the last hundred years?" The computer would tell you, "I only have knowledge until September 2019" because that's the data that was fed into this first, what we call large language models that drive generative AI and it'll give you an answer based on the information that we had. Again, you were giving it an input, it was processing the input based on the knowledge that he had gathered, and it was giving you an output. That in effect, is a long answer to what AI is.

Hancock: Well, a very clear and concise answer. Thank you, Sathish. So how is Ally thinking about using AI to support its auto business?

Muthukrishnan: We have been using AI. In the past, AI was primarily used for analytics. You could think about our underwriting. When you underwrite an application, we use several models as part of AI to make the decision. From manually approving every application, we slowly moved into auto decisioning those applications because we understood speed of decisioning was important to dealers because they have a customer sitting across the desk and how can you give a quick answer that required computers to auto decision those applications? We now auto decision applications north of 65%. That could be an approval or a decline. And that is powerful for dealers and powerful for our customers because we can immediately tell them whether we can underwrite and approve their loan or not. And that's where we started using AI. We have explored that into customer service. For customers calling in, how do you provide the best answer to them for customer care associates? We have a chat bot that lives both on our app and our website where customers can log in and ask questions about when their payment is due, etc. We also internally use AI models to predict performance of a particular loan. Will the customers continue to pay their dues or will they face hardships and maybe become delinquent? So those are models that we have historically used to predict and sometimes customers have a thousand things going on in their lives and they might miss a due date. So, would providing a reminder to them help them make their loan? And we figured out that in fact was helping our customers so we would use models to figure out who are the habitual late payers of their loans and give them a reminder and that would help them make payments and be on time. So, we have used models historically, and those are deterministic AI models, mostly for analysis and then driving effective customer experience for those models. But generative AI, we are moving from analysis to generating. What are we generating? We generate code, we generate summarization, we generate text, and we could even generate unique experiences for our customers. And that's what we are exploring, where we can take our existing AI journey to using generative AI.

Hancock: It's useful and it really becomes a complement for the dealers that are using it. So interesting Sathish. There is a lot of hype surrounding AI. So, is it a big deal or is it just another tech fad?

Muthukrishnan: Great question. Whether it is a big deal or not will be determined based on how we use generative AI. Is the technology here to stay? Absolutely. It's game changing and as a technologist, I feel it's our responsibility to educate the organization around what the powers of generative AI is, how we can effectively use it for driving unique experiences for our consumers and dealers. The president of Auto Business, Doug Timmerman, says we should use AI both effectively and efficiently. And I thought he put it very simply. Efficiently meaning there are tasks that our internal employees do on a daily basis. Can we use AI to automate and augment what they do every day to make them even more effective for our consumers. Can we use generative AI to make it more effective? As an example, we have a SmartAuction platform where dealers sell cars to other dealers across the nation. Can we effectively determine what the price of each car should be based on AI? Yes, we can. So those are some of the use cases that we are exploring and figuring out if we could use AI to make it more effective and efficient.

Hancock: Definitely not a fad. Definitely here to stay it sounds like. Sathish, thank you so much. You've given us really some great information.

Muthukrishnan: Thank you very much.

Hancock: That is it for this episode of the All Ears podcast. I hope everyone found this helpful. I certainly did. On behalf of Ally Financial and the Automotive News Content Studio, thanks for listening and bye for now.

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