Ep 133: How AI Will Change Financial Risk Management
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Artificial Intelligence (AI) has become a disruptive force across various industries, and the field of financial risk management is no exception. With cutting-edge AI technologies at their disposal, businesses can now make more informed and efficient decisions, mitigating risks and creating opportunities for growth. In this article, we will explore how AI is revolutionizing the financial risk management landscape and the implications it holds for business owners and decision-makers.
The Power of AI in Credit Risk Assessment:
Traditionally, credit risk assessment has relied heavily on manual labor and a limited set of structured data. However, AI has the potential to transform this process by harnessing the power of machine learning and natural language processing. By analyzing vast amounts of unstructured data sources and correlating real-time events with their economic impact, AI can provide a more accurate outlook on a borrower’s creditworthiness.
One of the key advantages of AI-based risk assessment is its ability to leverage unstructured information, such as news articles, social media data, and other non-traditional sources. This enables a more comprehensive evaluation of a borrower’s ability to repay credit, making it possible to extend credit to individuals and businesses without a traditional credit history.
Enhancing Decision-Making and Productivity:
AI tools, like the powerful language model ChatGPT, are revolutionizing decision-making processes in financial risk management. They allow for faster and more efficient analysis, enabling businesses to respond quickly to emerging risks and opportunities.By staying up-to-date with the latest AI capabilities, decision-makers can enhance both their personal and professional lives. Leveraging large language models can assist in connecting different data sources, identifying risks, and making data-driven decisions. Incorporating AI into risk management processes can automate tedious tasks, freeing up professionals to focus on more critical and value-adding activities.
Navigating Challenges and Ensuring Regulator Confidence:
As AI becomes more prevalent in financial risk management, it is essential to address challenges and concerns that arise. Regulated industries, such as banks, may be hesitant to fully embrace AI in decision-making due to concerns about inaccuracies and a lack of explainability.
To overcome these challenges, human oversight and input in the modeling process are crucial. This ensures that decision-makers have a clear understanding of how AI models generate outputs based on inputs, promoting transparency and regulators’ confidence. Industry leaders need to monitor and assess the success of AI implementation to ensure its alignment with regulatory frameworks and deliver accurate and reliable outcomes.
Opportunities on the Horizon:
With AI technologies continuously evolving, business owners and decision-makers must adapt and embrace the possibilities they offer. By leveraging AI in financial risk management, organizations can unlock new opportunities, gain a competitive edge, and navigate the complexities of today’s dynamic business environment.
Furthermore, AI’s potential extends far beyond risk management. It has the power to optimize processes, enhance customer experiences, and drive innovation across various sectors. As AI becomes more ingrained in business strategies, decision-makers must remain open-minded and embrace the opportunities it presents, while also being mindful of the ethical implications and potential social impact.
AI’s integration into financial risk management has the potential to revolutionize the industry, enabling businesses to make more informed and efficient decisions. By leveraging AI’s capabilities in credit risk assessment, enhancing decision-making processes, and ensuring regulatory compliance, organizations can navigate risks and seize opportunities with greater confidence.
As business owners and decision-makers, it is crucial to stay abreast of the latest advances in AI and related technologies. By embracing AI in financial risk management and staying informed about the tools and methodologies available, you can position your organization for success in an increasingly dynamic and challenging business landscape. Let AI empower you to make data-driven decisions, enhance productivity, and create a brighter future for your business.
Topics Covered in This Episode
1. Current State of Financial Risk Management
2. AI and Financial Risk Management
3. Challenges and Opportunities in AI Implementation
4. The Future of AI in Risk Management
Jordan Wilson [00:00:17]:
How will AI change financial risk management? You know? And how how are financial institutions going to be impacted with in The rise of AI, and what does that ultimately mean for us, the consumer? Alright. That’s what we’re gonna be going into today and more on Everyday AI. Welcome. My name’s Jordan, and this is your daily podcast Free newsletter, livestream, helping everyday people like you and me make sense of what’s going on in the world with AI and how we can actually use it to grow our companies and grow our careers. So we’re gonna be diving into what’s happening in the world, with financial risk, management and, How AI is impacting all of this. It’s super interesting because, you know, AI has been used very widely for many decades in the financial institution, but Recent, kind of, updates, and features, I guess, in generative AI, are going to be impacting financial institutions as well. So Very excited to get into that. But before we do, let’s go over the AI news.
Daily AI news
Jordan Wilson [00:01:21]:
And as a reminder, if you’re joining us live, like we have here, Doctor Harvey Castro Joining us live. Christy Slack joining us live. Thank you. Get your questions in. What do you want to know about how AI will change The financial risk management sector. Alright. Let’s get going. Go over AI news.
Jordan Wilson [00:01:39]:
We’re actually just going over 2 pieces today because they’re bigger pieces. But as always, there’s more news. Go to your everyday AI .com. Sign up for the free daily newsletter. Alright. So, the White House Has released the US government’s 1st ever executive order on AI. So president Biden unveiled a new executive order on artificial intelligence that aims To address safety concerns, protection of civil rights, and support for workers in the industry. So this new executive order involves creating new standards, Protecting consumer privacy, promoting innovation and and competition, and also collaborating with international partners.
Jordan Wilson [00:02:15]:
This executive order is the first Binding action ever taken by the US government on artificial intelligence and includes regulations for large companies to share safety tests with the government before release. That’s an important part. We’ll see if that happens. Also, this prioritizes the development of AI standards for testing and watermarking As well as guidelines for agencies using commercially available data. So pretty big, announcement. Again, we covered this last week when the news came out, But this executive order was just released, and we’re gonna have a lot more on this one in the newsletter today. Alright. Our 2nd piece of AI news.
Jordan Wilson [00:02:53]:
And last 1 for today, some new ChatGPT updates. Alright? So nothing official yet from parent company OpenAI, But if you’ve paid attention at all over the last, 2 or 3 days over the weekend, to the Internet, to social media, you’ll see that, ChatGPT is already starting, to unveil some pretty big updates. So many users are now sharing, these new updates, and probably 2 or 3 of the bigger ones are a reported, updated, knowledge cutoff date of September 2023. So this has already changed, once it went from September 2021, till January 2022 for GPT 4, the paid model. But apparently now it’s being rolled out. The knowledge cutoff is all the way up to 2023, as well as kind of the other, big feature or the big announcement here is the all tools mode, which is essentially being able to, upload photos, PDFs, receive DALL E images all inside one mode so without having to flip back and forth in Between multiple modes, so really getting the 1st taste of multi mod, multimodal in 1 single chat versus having to, you know, go into multiple, different modes. Alright. A lot more on that later in the week.
About Sandeep and Raven Risk Intelligence
Jordan Wilson [00:04:11]:
We’re actually gonna have a dedicated show on, new ChatGPT updates and the future, of, ChatGPT with the, developer conference coming up this week. Alright. But you didn’t hear you you you didn’t tune in to hear about Chattopty. And you can, again, always go to your everyday ai.com for more on that. You are here to learn about how AI will change financial risk management. So I’m very excited to have today on our show, and please please help me welcome. We have. There we go.
Jordan Wilson [00:04:41]:
We got him got him on the screen now. Sandeep Maira, the Founder and CTO of Raven Risk Intelligence. Sandeep, thank you for joining us.
Sandeep Maira [00:04:50]:
Thank you so much for having me. Really, really honored to be on the show.
Jordan Wilson [00:04:54]:
Yeah. Absolutely. Let’s let’s start high level real quick. Just tell everyone a little about yourself and, about what, you’re doing at Raven Risk Intelligence.
Sandeep Maira [00:05:02]:
Yeah. So I’ll keep the part about myself pretty brief. You know, my background’s in, Computer science actually took an AI class at Cornell 30 years ago. It’s kinda remarkable when you think about the what’s happened, you know, recently versus the last 30 years. And, one side anecdote was in that class. I took out the textbook from the class. I still have it a few days ago, and it said, There’s a small section neural networks, which is what LLMs are based on and ChatGPT is based on. And it said, you know, like, it’s not showing much promise yet, but the people, the neural network researchers, think that with enough computational capacity and data, it’s gonna emulate human decisioning.
Sandeep Maira [00:05:41]:
And it said only time will tell. Actually said in the textbook. Only time will tell what actually happens. So, anyway so, basically, I think there was a long AI winter. I mean, in the meantime, while I had a interest in AI, More broadly had an interest in analytics. I worked a lot of financial firms and, you know, applying essentially, I would say, algorithmic techniques For financial risk management in particular, and some trading, you know, systems. So I’ve worked at JPMorgan, Citigroup, BNY Mellon, and more recently founded earlier this year, an AI venture called Raven Risk Intelligence. And, you know, I’m happy to talk a little bit about the objectives of The, you know, of the venture, Jordan, if you wanna meet want me to go there next.
Jordan Wilson [00:06:23]:
Yeah. Yeah. Let’s let’s just go high level. Let’s just Talk about a little bit about what it is so everyone can understand. So, yeah, just tell us a little bit about that.
Sandeep Maira [00:06:29]:
Okay. So, essentially and I think very broadly, the 3 thesis is twofold. So one of them is, You know, in the the venture is actually in commercial and corporate credit lending, not the consumer credit space. At the commercial credit space, there’s a lot of manual done by, you know, large teams of credit analysts to try to gather gather information about the borrower, you know, and including and also the the economy. So it includes things like, you know, finding out about the business strategy of the company, the strength of the management, you know, any competitive threats to the company. And then from a more macroeconomic standpoint, it would be, you know, things have that might be happening in the economy and so forth. Now, today, basically, most of the Information automated information that they get, tends to be pretty static. You know? They have to actually kinda scour essentially, unstructured data sources like the ones that I’m just mentioning to come up with a view on whether this is actually a good credit or not.
Sandeep Maira [00:07:28]:
So our first goal is to, you know, help, automate that, which will lead to actually increased, ability to and Process more loans and frankly be be able to give more loans to more companies by taking into account a broader set of inputs rather than just, you know, Is the company profitable today or not? And that actually, frankly, I think will enable a lot of smaller borrowers to, to get loans more easily. And the 2nd part of it is, you know, what we’re calling predictive risk, analyses, broadly speaking, which is, you know, how are things gonna perform Over, you know, like a wider period of time, but using fairly advanced analytics and machine learning analytics to draw correlations between, you know, different things that are happening in the In the industry and in the economy.
How AI changes financial risk management
Jordan Wilson [00:08:13]:
I love it. And and and, Sydney, maybe help us also, you know, for for for those of us that don’t follow the financial sector very closely. You know, let’s just talk, you know, briefly about, you know, financial risk and and and risk management and And and and kind of historically, you know, where where it’s been recently and how you see it changing now with advancements in generative AI. You know? And and and like like you talked about, you know, I love that you mentioned the the textbook from from 30 years ago and and, you know, kind of the AI winter. But Now we’re getting to the point where, you know, AI is really helping in that decision making process. So, like, what does that mean broadly for the financial risk management industry?
Sandeep Maira [00:08:53]:
Yeah. So I think, you know, like, very I think broadly speaking, you know, some of the things that I think you so let me just back up a little The way that, you know, a risk management works today and even actually the more advanced risk management techniques, you know, tends to be taking into account what we call structured data, and that data is, you know, things that are tabular in format, like, you know, rows and columns. So things you know, simple stuff, frankly. Like, you know, what is the, revenue of the company? You know, what essentially they do take into account some macroeconomic like, you know, what is the GDP growth in the economy, etcetera. You know, and I think and that’s the that has been the cutting edge, unbelievably. Like, that’s the limit essentially of what their their automated sort of tools and risk management can do today, and then they take those, industrial structured risk factor structured input. Sorry. And then, you know, put them into models that attempt to make, decisions about or Outlooks of, risk, for a given, let’s say, company or sector.
Sandeep Maira [00:09:54]:
You know, now the problem with that, which just kinda alluding to earlier is that it’s actually relatively narrow. It doesn’t take into account things that, you know, let’s call it unstructured, which are information pieces that might, you know, happen, that are not, you know, tabular format, frankly. So an example could be that, you know, there’s a war that breaks out somewhere, You know, like, maybe in Taiwan or wherever. And, you know, these models actually can’t really take that into account at all. You know? It’s human judgment that then tries to figure out, oh, well, What exposure does this company have to Taiwan? And, you know, and that can be, a, very manual, and, b, not very comprehensive leading to frankly inaccuracies. So I think the objective is that, you know, with the event of machine learning, you could take these events that happen and, basically, in real time, which is pretty amazing, And then, you know, correlate that with, impacts to, you know, to the economy and to even individual companies.
Jordan Wilson [00:10:50]:
Yeah. And and and just real quick, and maybe, Sandy, if you can even help help me better understand this because I’m always trying to learn as well. So with with unstructured, or or sorry, with structured data, that’s been used in the, you know, financial industry and for risk management for decades. Right? So that’s where, You know, machine learning and AI gets all of these data points and and they can categorize them. Right? And they can say, yes. This pattern of data over the course of, you know, Hundreds of thousands or millions of data points. We could make decisions based on this structured data. Whereas unstructured data, it’s a little harder and For AI models to be able to understand that and to be able to translate that to risk because it it it could be things that require more interpretation or more, interconnectivity that may be hard for traditional AI models to perform those tasks.
Jordan Wilson [00:11:35]:
But that’s maybe where now with large language models where you can start to make use of some of this unstructured data and and and tie it to, to risk management or to assess risk. Is that kind of, a a good overview? And then if so, in How do you think large language models might be able to help pull this all together?
Sandeep Maira [00:11:54]:
Yeah. Well, firstly, I think that’s an incredibly, perceptive observation I was worried. I was worried. Let’s see. I wouldn’t say that you don’t know a lot about how to use AI at risk. You probably actually know quite a lot, because I think, you know, you’ve connected a lot of the dots actually, which is what these models, you know, obviously, are doing in terms of trying to figure out what, you know, the impact is to, to risk. So, yes, I mean, I think, you know, the you’re correct, firstly, that structured data and having models, Even some machine learning models actually draw draw correlations on structure that that has been around for, you know, some some years. And, you know, they’ve done a pretty good job, actually.
Sandeep Maira [00:12:35]:
So for example, in fraud detection, you know, whether it’s credit card fraud or, you know, even a trading fraud, you know, these models have been around for few years where they look at different patterns of behavior of, let’s say, consumer borrowing. So let’s say that, you know, you go basically abroad somewhere that you haven’t been before. You know, you’ve noticed quite often that the credit card company will, you know, call you up or even block your card from usage, because they’re noticing, you know, essentially, an anomaly in your, you know, in your in your credit behavior. So that’s been around for a few years. I think what is new though is to, you know, use essentially this for other use cases and do it at a much larger scale. Mhmm. And so an example is, You know, Silicon Valley Bank actually might be a good example. So in Silicon Valley Bank, you know, what happened was that the, you know, the Fed raised interest rates very rapidly.
Sandeep Maira [00:13:24]:
The bank essentially had a what’s called the liquidity. So that’s called market risk. You know? Rates are considered to be like, market, market events. That led to what is called liquidity risk, issues, which is that, you know, the bank didn’t have enough money, cash on hand, you know, to actually satisfy all its depositors. Because banks take depositors’ money money and loan them out. They’re just not they’re not just sitting in the bank because they have to own interest for the bank so that they can pass that interest on to the depositors. So they had what’s called a liquidity, risk issue. And because of that, you know, they had what what is called a credit event, which is the bank essentially, and For practical purposes, defaulted.
Sandeep Maira [00:14:03]:
Right? Which is essentially meaning that they could not satisfy, you know, their their creditors, who actually their the creditors in this case. So that you know, I think that interconnectivity would have been much more easily apparent With the use of proper, training of AI models on how different risks are interconnected to each other. And I think what happened at Silicon Valley Bank Would have been almost completely predictable with the better use of this interconnected AI models that I think you’re talking about. And large language models in particular, You know, just to double down on that part a little bit are actually really good at that. So they basically you know, I know they call large language models, but Underneath the covers, what they’re doing is looking at, you know, connectivity and correlations between different things and then figuring out essentially, you know, what to so called generate. And that’s why it’s called generative AI. But you can use that not only just for pure language, but you can actually use it for drawing, you know, correlations and patterns essentially between all kinds of datasets that was not achievable before. So I think those large language models can be very And those techniques, I guess, the modeling techniques that are used in LLMs can be very useful for, you know, risk analytics as well.
Challenges of adding GenAI to financial risk
Jordan Wilson [00:15:16]:
Yeah. And and, hey, as a reminder, if you’re just joining us, live midway through, we have send, Sandeep my Myra, the founder and CTO of Raven Risk Intelligence. And if if If you have questions, please get them in now, so we can, give Sandeep a question, a chance to answer your questions. And, Sandeep, I’m I’m so glad you brought up, you know, this, Silicon Valley Bank, kind of, collapsed because I think that’s maybe one of the most, relatable, for for for many people in terms of financial risk because we saw, unfortunately, things go down, an unfortunate, path, for for many involved. And I think some of the, initial response to that is people said, hey. You know, with with all of this data, With all of this, you you know, artificial intelligence and and machine learning, how did this happen? And you you kind of started to, you know, help us solve that. It’s, you know, kind of Different, I guess, models or different sets of data that maybe weren’t talking to each other. So, you know, with with even generative AI, I guess that could potentially help solve this in the future.
Jordan Wilson [00:16:19]:
What are still those obstacles to overcome, in until we can have, you know, generative AI, You know, help, kind of, you you know, quote, unquote, connect all these different, you know, pieces of of data or these different models together. In What what do we still have to do, and then maybe even what are the risks of doing that?
Sandeep Maira [00:16:38]:
Yeah. No. That’s a great question. So, you know, so firstly, I think, You know, these models can are only as good as the data and how the data is essentially presented, to them. And so, you know, they’re not magic. I mean, they basically might seem like magic, but the reality is that all even ChatGPT is doing is it’s taking all the data on the Internet and And, you know, trying to do its best essentially to come up with what makes sense from an output perspective. But not everything is on the Internet. So particularly, I think in some of the, you know, business domains like in, commercial credit lending, you know, a lot of the, you know, a lot of essentially the inputs actually come from human inputs that are not codified on the Internet.
Sandeep Maira [00:17:23]:
You know? So for example, like, you might have something that is and Somewhat subjective about, you know, essentially, let’s say that, you know, there’s this going to be a change in the business strategy of the company as an example. And then quite often, there’s a subjective decision made by the bank about, you know, does that business strategy lead to potential risk to company or not? And, you know, and what and how big is that risk? You know? Like and I think so the these large language models are not yet at the clarify things that are and even come up with correlations with things that are not, you know, readily present in that data. And so, human what’s called human reinforcement learning, there’s actually a couple of terms for it. One is called long unwinded term. I mean, these guys come up always in the based with very long acronyms and, you know, on obscure terms, but it’s called reinforcement learning with human feedback, RLHF. And that actually is actually pretty hot area of, even research ironically is to actually get humans to at least partially train the more obscure and more critical parts, essentially, of these models because the impact of these models and business decisioning can be pretty severe. So if somebody could be denied a loan, for example, and, you know, could actually mess up their business, if the models present data that is, you know, outputs that are not not completely accurate. So that’s one thing.
Sandeep Maira [00:18:44]:
So that’s, I think, one thing that I think is, you know, like a challenge, but I think there’s some ways, like I said, it’s an active, Spaces do not to take these automated models and LLMs and charge GPT like models, but then in you know, apply some human Oversight and inputs onto that, onto the modeling process. The second 1 is, you know, that, in finance and particularly in regulated finance like banks, you know, they tend to be very highly regulated. And so the regulators are very nervous about using machine learning frankly in general for decisioning purposes. They started to get somewhat more comfortable about using machine learning for using structured data, particularly for things even like consumer credit. But they’re not yet there in terms of using unstructured data and, and and machine learning to come up with decisioning. Yeah. I think we all know about you know, many of us know about hallucinations, so these models are not completely accurate. You know, quite often, frankly, if you’re there, ask very specific Questions.
Sandeep Maira [00:19:46]:
You know, they can, some of the data is inaccurate, which is frankly not acceptable in the finance I think the regulators are very nervous about that and and probably rightly so. So I think one of the things that, you know, I think is going to be A challenge is actually, a, getting the models to be more accurate than they are today. So moving them from a from a consumer space to an enterprise decisioning space, and then getting you know, once that happens and getting the regulators comfortable, which is not always easy with, hopefully, improvements And, you know, in the in the decision recommendations from these models. And then a related point to that actually is something called explainability is that the regulators and even the firms themselves, You know, don’t like black boxes. So they wanna know essentially some idea of how the, models came up with these outputs and recommendations Based upon the inputs. You know? So they want some traceability between the inputs and the outputs. And, you know, unfortunately, today, deep neural networks and are are not able to do that. I mean, they’re the models are so large that it’s not easy to actually trace How ChargeGPD came up with the outputs based upon, you know, billions of points of input from the Internet.
Sandeep Maira [00:21:00]:
So that’s gonna be another frankly challenging area as well.
Jordan Wilson [00:21:04]:
You You know, so as AI can help in all of these areas, and I’m sure that’s where the, you know, the financial risk management, You know, experts are starting to spend their time in. Where do humans kind of, fall into this this future equation? Right? Like, Is their job going to to change? Are their responsibilities going to change? And and like I said, is this maybe a good thing Or a bad thing. And and and, you know, what are even the risk of of that as, you know, leaders, working in this space are maybe using and leveraging More, more AI. What do, what do we have to keep an eye on to make sure that this is successful, in in in In terms of, you know, risk management and handing these things off because it sounds like AI can really help in some of these areas and to, you know, help connect Some of these disjointed, you know, verticals where we have all this different data that exists, but then, you know, how does that change then? You know, what ultimate responsibilities, you know, lie on us us humans.
Sandeep Maira [00:22:10]:
Yeah. So, basically, you know, firstly, I think in terms of the, you things related. I think people are nervous, you know, certainly. And I think, frankly, the more people, use to have GBT, in some cases, the more nervous they get because they see, you know, how powerful it is. Right? So people, you know, for for good reason, you know, while we’re worried about their jobs. I even get questions from people about, you know, what fields their kids should Study in, you know, that will be less adversely impacted by AI. You know, so there’s a lot of, I would say, valid actually questions and concerns about, You know, the impact to, you know, to social impact, but also, you know, impact to the workforce. You know, my view is somewhat more somewhat really positive, at least in the long run.
Sandeep Maira [00:22:53]:
So, you know, I think in the long run, things that are more tedious will be taken away. And, you know, essentially, machine learning and AI can Automate those tasks. But there are things that, you know, I think are harder actually for machines to be, responsible for that humans actually could play a bigger role. So as an example is even in this risk space, you know, and the productivity, that I was talking about. You know, I was talking to somebody very senior at a big bank and risk, and he was saying that, You know, he’s found that their credit risk analysts actually find this gathering of information and then trying to summarize it to come up with some, you know, outlooks It’s very tedious, and, actually, the turnover he’s found in that in that, you know, in that part of his team is actually pretty high. So I think, essentially, it, You know, it will remove some of the more tedious tasks and enable humans to focus things, frankly, that are more interesting and more value add. So I think I think there are things that we can do in the future that we don’t even know yet. You know? I mean, an example could be in the media space that, You know, people are very worried about AI basically generating movies automatically and taking away actors’ jobs.
Sandeep Maira [00:24:01]:
Now in the short to medium term, that’s a valid concern. But in the longer term, if you think about it, somebody who’s very creative, you know, could essentially, as 1 person, potentially, you know, let’s say, you know, venture in the future, create a full art movie On their own, you know, which today is very difficult for creative people, frankly, to break into, you know, getting large audiences. It’s, it’s not an easy task. I do think that there’s opportunities for leverage here. If you think about it, just one last point on that, it’s not completely dissimilar to the Internet, you know, where, Essentially, people were very worried that the Internet would take away, you know, lots of, jobs, particularly in some sectors like, retail, you know, and Amazon with the advent Amazon and so forth. But I think if you look at, you know, another way, there were many jobs created related to the Internet that, you know, in many ways offset The more than offset the job losses in other sectors.
The impact AI has on consumers
Jordan Wilson [00:24:53]:
You know, one thing one thing AI, as a super aside, one thing AI can’t help is me charging, my my mouse battery. So apologies. I do see I do see some great comments coming in, but my mouse actually died, so I can’t bring them up. I’m sorry. But maybe, Sandeep, you you know, as as we look, as we look forward, to the future of risk management, so one thing I I maybe wanna get get your thoughts on is how this ultimately impacts consumers. Right? Because I think, you know, if if we’re suit like, if if if we’re looking, like, What’s actually very tangible, to consumers. You know, one thing that we probably worry about is, you know, risk and fraud. How might we, the average the average, you know, bank consumer, you know, we have our savings accounts, our 401 k’s, our IRAs, credit cards, all those things.
Jordan Wilson [00:25:44]:
How might we be impacted, by all of these changes that we’re kind of talking about and even as it comes to risk? You know, are are consumers ultimately more at risk in the long run or maybe are we less?
Sandeep Maira [00:25:59]:
No. I think actually in risk, it’s a net frankly, a net positive because, you know, like, I think one of the issues in the consumer space Today is that people who don’t have what’s called a traditional credit history find it hard to get, you know, credit, including credit cards and loans. And, you know, I think by taking essentially what we’re calling unstructured data sets, like, for example, let’s say that somebody doesn’t have a long credit history, But they’ve got a, you know, a good history of paying the regular bills on time, like the utility bills, etcetera. Right? And they they’ve been you know? So I think those kinds of datasets that haven’t been Today, could provide essentially better, you know, like, I would call outlooks in terms of what the consumer’s Ability or or, you know, ability to pay back essentially that credit looks like. So I think it actually will expand, in fact, access to credit for consumers who have had a harder time, you know, getting credit today. And frankly, that includes, you know, minority populations, or people who, you know, who and of their own have had, let’s say, a rough time. Right? But, inherently, you know, they probably can be a good credit going forward. So I actually think it’s a net positive.
Sandeep Maira [00:27:08]:
On the commercial space, the impact is probably less directly visible. But another way to look at it is that, you know, if Companies get easier access to credit too. You know, that essentially helps these business owners. You know, some of them are small business owners as well, not just large corporations. And then, you know, that ultimately helps the economy by ensuring that the economy is more productive and reduces prices for consumers. So you don’t want an economy where the access to credit by both large and small business businesses is appropriately allocated Because that ultimately drives, you know, what benefit and prices that consumers pay, you know, on the street.
Jordan Wilson [00:27:47]:
So, Sandeep, we covered an awful lot here. You you know, we talked about, you know, historical use cases For AI and machine learning over many decades. Right? Like, even going back to, you know, a a course you took some 30 years ago, And then we kinda got caught up to to to current day. And, you you know, some of the challenges and also some of the opportunities, that that are associated with, You know, financial risk management, in the new age of of generative AI. But maybe maybe what’s that one, that one point that you would really want to Stick with people. Right? So whether they’re, in the financial industry or if it’s just everyday person, what’s kind of that one big takeaway that you would want us all, to to hopefully understand so that we can better understand kind of where, financial risk management is going now, now that we have access to better, and More powerful and more more connected, AI systems.
Sandeep Maira [00:28:45]:
Yeah. I think the general point that I have, and maybe it’s not just specific to financial risk management, This may be somewhat obvious perhaps, but is that, you know, I think that, everybody should stay current with, you know, the tools that are out there like ChatGPT. There’s another way to think about it is that if you’re not using those tools, Then, you know, your counterpart may be using their tools and improving their productivity. Right? So, like, you know, we we wanna you wanna be a little bit careful from your own, I would say career perspective as an example that you’re staying current with what the openly available capabilities are for, like, you know, large language models as an example, because you don’t wanna be put at a disadvantage. Right? You wanna you know, it’s my strong recommendation is to stay current with what’s happening with at least the widely available tools So that, you know, you can use them, obviously, where allowed, you know, to improve essentially both your personal life as well as Maybe a productivity at work. So that’s that I I think what I would say to, you know, most people.
Jordan Wilson [00:29:44]:
Sound sound advice. Sandeep, thank you so much for joining the Everyday AI Show. We very much appreciate your insights.
Sandeep Maira [00:29:52]:
Thank you so much for having me. Really appreciate it, Jordan. Alright.
Jordan Wilson [00:29:55]:
And, hey, there was a lot there. Don’t worry if you missed a little bit. Go to your everyday AI.com. Sign up for that free daily newsletter. Will not only be, hey, a lot more AI news, but just more insights and more depth into what Sandeep was talking about. Thank you for joining us, and we hope to see you back For another edition of everyday AI. Thanks y’all.