Tech Talk: How AI and Machine Learning Can Help People Make Better Financial Decisions

The AI Podcast connects with some of the world’s leading experts in artificial intelligence, deep learning, and machine learning to explain how it all works, how it’s evolving, and how it intersects with every facet of human endeavor, from art to science. Recently, host Noah Kravitz sat down with Intuit Senior Vice President and Chief Data Officer, Ashok Srivastava, for a wide-ranging discussion of the power – and potential – of advanced technology to help everyday people around the world make better financial decisions. Following are highlights excerpted from their conversation.

“A lot of the societal issues we’re facing have an economic and financial underpinning. A combination of people, data, and AI/machine learning can really help address some of the most basic financial challenges people are facing. That’s essentially the mission we’re on.”       

– Ashok Srivastava, Senior Vice President & Chief Data Officer

The AI Podcast: I’ve read a lot about Intuit using machine learning and AI to help customers. Can you talk a little bit about that and what your role is as Chief Data Officer?

Ashok Srivastava (AS): My role as Chief Data Officer is to help define the strategy for AI, machine learning, and data for the company in order to serve customers as best as we can. What better industry than finance to see how these technologies can be used to help really regular people do better?

The AI Podcast: I’m one of those regular people. I use QuickBooks Self-Employed which helps me track expenses and income coming in and out, that sort of thing. I started using it mainly for its ease of use. My understanding is that machine learning and AI adds another layer to make things even easier. Can you speak a little bit about that?

AS: When a customer is going through their daily lives, they have to understand things, like the categorization of transactions. We’re using AI techniques to make people’s lives easier so that in the background, the machine can automatically go through all of that data with your permission and then categorize it for you and give recommendations.

The AI Podcast: Are we at the point and, if not, how far away might we be from an AI-driven system being able to suck up all of my financial data from the year and spit out my tax return and all I have to do is sign and e-file?

AS: We’re really taking the time to build the technologies that can help people do their financial advising, and get their taxes done as quickly as possible, without having a lot of manual work.

We’re moving along that path really well. We’re building systems that can literally ingest data from the IRS. This might be tens of thousands of pages of documents to automatically ingest and build ontologies, and other kinds of knowledge graphs behind it so that the machine itself has an understanding of taxes and how taxes should be filled out.

The AI Podcast: How big of a challenge is that, and what are you trying to train your machine systems to learn and understand?

AS: There’s no doubt that the taxes in the US change, and as they change, they’re extremely complicated, and it’s very difficult to keep up with it. We’re building techniques that can actually go through the written code. Thousands and thousands of pages of code. All of the forms. It’s a system that has both machine learning – which means the ability to learn from data – and knowledge engineering in it. In other words, building in rules and rule-based systems. Expert systems that can take that information and codify it in a way that machines can understand.

As the tax code changes, the systems can keep up and evolve with it so that something new happens, it goes through the ingestion pipeline.

The AI Podcast: Is your ingestion pipeline an automated machine at this point that’s using natural language processing to parse the actual English written tax code?

AS: Yes, natural language processing, natural language understanding, and machine learning are the three major pillars on which that technology is built. We’re building systems that can actually “read” the English language that’s written in these tax forms and in the tax documents, and build a knowledge structure.

The machine learning component is really novel, because what it does is look at the data that comes through and matches it against the information that’s encoded in the ontology. It says, “Does this make sense or not?” It actually gives us the ability to explain why certain tax decisions might be made or not made. It’s a very, very foundational element of our entire technology stack. Most of it is being done in an automated fashion, but there are always tax experts and other experts that are looking at it to make sure that everything’s being done really, really well.

The AI Podcast: Whether looking back even at the year you’ve been at Intuit, or the 20-plus years you’ve been working generally (aerospace, aviation, telecommunications, finance), what if anything along the way has surprised you or maybe stands out as a breakthrough watershed moment in the work that you personally have done?

AS: I have been so excited to see how some of the techniques that we’ve worked on, like anomaly detection and prediction problems are ubiquitous across different industries, across different customer bases, and frankly across different sciences.

Anomaly detection – the ability to determine whether something that you’re observing now is the same as what we’ve seen before – is different than what you’ve seen before.

Understanding anomalies is not based on one variable, but maybe 10 variables or 100 variables or 1000 variables and more. That’s an important problem, something that I’ve worked on with my colleagues for a long time that I’m super interested in.

Another problem that I’m extremely interested in, and one that really compelled me almost to come into the field of machine learning, is the science of making predictions. Every day, I wake up and I think about new ways to build predictive models, new ways to build anomaly detection models, new ways to understand texts and written language that makes it such an exciting area to work in.

The AI Podcast: Are there predictive things that are being worked on or could be applied to the individual or the small business looking at their financial situation? Are there things that Intuit offers that use AI in this capacity or perhaps things that are coming down the pipe?

AS: Absolutely. Let’s think about a small business that might be building its business up, hiring a few employees, buying from suppliers and so forth, and they have some money coming in on a regular basis from their customer base, and they also have some debt and expenses.

If you think about it, one of the key problems that they face is understanding their cash flow. “Am I going to have enough money at the end of the week or at the end of the month to make my payments to my creditors and to my employees?” There’s a prediction problem in there. This is where machine learning and predictive models can play an important role, because they can look across not just one or two or five variables, but 50 or 100 variables and say, “You know what? You should plan to bring some money out of savings in order to make your next payment to your creditors, or the next payment to your suppliers.” Or, “You’re doing really well, take some money right now and put it into savings so that you can use that for a rainy day later on.”

Those kinds of financial decisions are things that can be made through the combination of the cash flow prediction, which is something that we’re working on, and also it can be used with other techniques like advice and recommendation systems.

The AI Podcast: As a follow up on that, when you think about reaching many people around the world, how much of the world has that ability right now to leverage the automated systems to connect the accounts so that the data can be pulled in and examined, and the recommendations can be surfaced back to the individual to help them? How much of the challenge for you in that mission is connecting more of the world’s individuals?

AS: The number of mobile phones and cellular devices there are around the world is extraordinary. Smartphones are ubiquitous. That gives everyone an even playing field, which is very, very exciting. Intuit’s mission is to build products that can help people around the world do better from a financial perspective, empower prosperity. It is a worldwide mission.

The AI Podcast: If I can ask you to put your own predictive hat on so to speak and look five or 10 years out to the future, where do you see this headed? Is there a goal in the distance or something that you think is coming that’s going to change or even be a big marker in evolving the landscape for this pursuit of personal prosperity for more people?

AS: I think there are a couple of things that are going to happen. I think we’re going to see significant acceleration in the way natural language understanding and conversational systems, and machine learning come together.

Right now, these things are related, but they’re not tightly related and coupled. As they come together, what I envision is a future where machines can help people make better financial decisions based on a huge amount of data as well as based on a huge amount of knowledge and expertise that’s encoded in the system itself.  

It’s really about what we call in the field decision making under uncertainty. We don’t know what’s coming in the future, but we want to use the data and information that we have today to make the best decisions possible.