You Ask, I Answer: Success for Finance Professionals in an AI World

Ashley asks, “Assuming everyone adopts AI what will separate finance professionals that are the most successful from everyone else? What will they do differently?”

We review the core promises of AI, what AI is and isn’t good at, and what AI is bad at today with a focus on finance professionals. This is how to plan your career in the immediate and medium-term future.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In today’s episode,

Ashley asks, assuming everyone adopts AI, what will separate financial professionals, finance professionals, the most successful from everyone else? What will they do differently? This is a really good, very interesting question. And it goes back to two things. Number one, understanding what the core benefits of artificial intelligence are. And number two, understanding what artificial intelligence is and is not good at so let’s bring up the first thing here. What is AI good at artificial intelligence delivers three promises, number one, acceleration, get to the answer faster. Number two accuracy, develop better answers than humans can develop. And, number three automation alleviating us from doing repetitive tasks stuff that it’s just not a good use for our time, our intellect, that’s what AI is really good at. And what the problem is, is, and companies will transform these promises into higher profits, more productivity, without adding headcount, freeing up workers time to do more valuable work, more interesting work. I mean, nobody wants to be the copy paste version. And these are all things that artificial intelligence using machine learning, supervised unsupervised reinforcement, deep learning wide learning, whatever you want to pursue all these forms of machine learning deliver on these promises. Now, here’s where things get interesting. Artificial Intelligence is really good at taking a data set that we know and that we may not know the answers and, but we know it, it’s good quality, we are aware of it, and we can transform it into stuff. It’s we start with good raw materials and end up with a good result. This is in the in the Rumsfeld matrix, the known knowns, the unknown knowns, we the data is good, we don’t know about it. Artificial Intelligence is as good for that. That’s still as much more traditional data science right now exploring this data that is good, but we don’t even know it so that it exists. So figuring out what’s out there, what can we bring together, what can we sequence and you can use machine learning techniques on a tactical level, to help speed up those processes to explore the unknown knowns. But at the same time, it’s very difficult to to automate that process beginning to end equally true, it’s difficult to work with the known unknowns, we know that the data exists, but it may not be in great condition. It may not be prepared or structured for use by artificial intelligence systems. Remember that artificial intelligence requires a great deal of information of data. And that data has to be in good condition, the training data and the testing data has to be in good condition in order for AI to build a model successfully on it. If it’s not, then you’ve got a real problem. Again, you’ve got more of a data science problem there. Whereas you have this data, it’s known, but the quality, the content, the stuff inside is still an unknown. That’s where, again, you’re going to need more data science, and you will machine learning and artificial intelligence. And then the final quadrant in the Rumsfeld matrix is the unknown unknowns, we don’t know and we’re don’t know even know what if there’s data for or what condition the data and that’s where humans still will play a key role in the process of business, these unknown unknowns, things we can’t see inside the the data is even get answers. So what are some of those unknown unknowns that you still need humans for that in finance, in capital markets, in lending and all these things? What are those? Well, they really center around four key areas don’t they, they it is empathy, judgment, general life experience, and relationships. machines don’t empathy, we can simulate aspects of it, particularly with natural language processing. But we can’t do the actual process of being empathetic. We’re even sympathetic with machines, that is something that is still people need to do. So if you are a banker or lending officer or a stockbroker, you can probably have some level of empathy with the person that you’re working with you with your customer with a client, that machine will never be able to do, unless you’re a jerk, in which case CS we can get a chat bot to be less of a jerk to the customer. But we’re assuming that these finance professionals that actually is referring to are actually good at their jobs.

The second is judgment, human judgment is very difficult for machines to put together because judgment comes from such a wide set of inputs, that it’s very difficult to capture that right now, we don’t have general purpose wide AI, we’re very narrow AI. And so being able to say, yeah, look, the numbers also a, that you are a credit risk, but I just kind of feeling that that that it’ll be okay. Right, something like that. That’s human judgment. And there is a lot of emotion in that, again, machines and emotions, not their strong point, general life experiences. The third thing where, again, finance professionals, particularly people who have a little more gray hair can really go ahead and and bring their life experience. Remember that a lot of AI comes from training data, right comes from training data sets, and you can capture a tremendous amount of it. But it’s very difficult to capture tremendous amounts of it over massive periods of time, and still have it makes sense because the models would have to change over time. So someone who lived through the stock market crash of 87 remembers that the conditions for that market crash were very different than say, the the Great Recession 2007 2008

when, when all sorts of lending just collapsed in on itself, very different set of experiences, very different period of time, very different data, very different system inputs. But human behavior, very consistent machines have a much harder time modeling that then people who lived through these different financial crises can remember and work with. So general life experiences, that really important third bucket. And finally, the fourth bucket where, again, finance professionals who want to continue to succeed must be heavily invested in human relationships. Yes, you can, and should automate the process of remembering when your clients birthdays are, for example, but

it would be very difficult to remember. Oh,

Dinesh, his kids love Pokemon. And so we’ll get him I’m gonna send a cupcake with a peek at you on it.

It’s those little touches, it’s those human relationships. It’s that being willing to go out and have a beer with somebody, or have a cup of coffee over breakfast, or lunch, or whatever, with somebody that again, machines would really struggle to do machines will augment us machines will give us the data that we need, you know, as we’re walking into that coffee, we open up our device, we go Oh, yeah, that’s right. All these things, the machines can remind us of the data. But then we have to be the ones who bring that data to life through our relationships to be able to to to remember that Dinesh as kids birthday or that for the stocks he was talking about last month, man not so good. Or man, his you know that that small business loan, there’s been a change in regulation. Yeah, I gotta remember to talk to him about that, because something’s changed. So those are the other aspects of relationships that again, very difficult to automate. Because relationships by nature are very wide, they cover an enormous amount of ground and AI is very narrowly focused on solving problems. So

if a finance professional wants to be as successful as possible, you need AI to augment your capabilities, you need that data processing, you need that acceleration, that accuracy that all automation but you also need to double down on empathy, judgment, life experience, and most critically those human to human relationships. Without that,

yeah, you absolutely can be replaced by machine if you were just doing very narrow tasks day in and day out, guess what machines are real good at narrow and if you are not that at the top of your game machine will absolutely take your job. If, on the other hand, you’re focusing on the people machines aren’t people so great question, challenging question as always, but it has a lot to unpack and there’s a lot of hope for people to be able to continue to have rich, vibrant, very successful careers as long as they focus on what human beings are good at and let the machines be the best at what they are

best at. Please subscribe to the YouTube channel to the newsletter and I’ll talk to you soon. Take care

if you want help with your company’s data and analytics. Visit Trust Insights calm today and let us know how we

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