Midsize Banks and Credit Unions Level the Playing Field With AI and Advanced Analytics

Midsize Banks and Credit Unions Level the Playing Field With AI and Advanced Analytics

Show Video

LARRY HALL: Hello. I hope you're all well. And thank you for taking the time to view this session of the 2021 SAS Global Forum entitled, "AI in Midsize Banks and Credit Unions, Leveling the Playing Field." My name's Larry Hall.

And I'm with Pegasus Knowledge Solutions. Our work at Pegasus has shown that there's a great opportunity for AI within midsize banks and credit unions. But it's certainly not without its challenges. During the next 20 minutes, we're going to look at AI in the world of banks and credit unions, look at its value and use cases, the challenges facing firms that are looking to implement AI, and a key way that these firms can overcome these challenges and begin to use AI to level the playing field with their larger competitors. As I said, my name is Larry Hall.

And I'll be walking you through our session today. My focus at Pegasus is on client engagement and PKSI's solutions. Throughout my career, I've focused on helping companies apply advanced technology in order to improve operations and their competitive position. Today, that focuses on the use of advanced analytics and artificial intelligence. Since many of you may not know who we are, let me start by telling you just a little bit about Pegasus Knowledge Solutions.

PKSI is a SAS Gold Partner, who for over 20 years has specialized in helping our clients use advanced analytics in AI to solve some of their toughest business challenges. PKSI is a full-service AI firm, meaning we offer our clients resources, consulting, and products to meet their AI needs. In fact, one of these products, our Advanced Workforce Analytics Solution, won SAS's 2020 Excellence in Innovation Award at last year's SAS Global Forum. As you can probably guess from the title of this session, we're going to be talking a lot about AI and the important role is playing in many financial institutions today. So why don't we start at the beginning with kind of a basic question. And that is, is AI a thing? Now, I ask this sort of tongue in cheek, because we know it is.

After all, we've seen it in movies, like the 8-foot tall gort in The Day the Earth Stood Still, to Hal 9000 in 2001, A Space Odyssey, to the Terminator, to even Star Wars. And while maybe that's a stretch, bringing it a little closer to home, AI has been a thing in research labs, certainly since it was first given a name and identified as a field of study in the 1956 workshop at Dartmouth College. From the Hopkins Beast built at Johns Hopkins University to Shakey the Robot from the Stanford Research Institute to Deep Blue from IBM, research teams continue to make significant advances in AI over the years. But this is where it really comes home for most of us with businesses and the business press from The Wall Street Journal to Forbes to the Financial Times all recognizing the impact AI will have in transforming the future of business, and in particular financial services, and emphasizing where the focus should be for companies in 2021.

So given that AI is in fact a thing and because we're going to use the terms AI and machine learning quite a lot during today's session, it might not be a bad idea to start with a couple of quick definitions. First, artificial intelligence-- when we talk about AI we're talking about, according to John McCarthy, who coordinated that first workshop at Dartmouth, the science and engineering of making machines intelligent. A little bit more detailed definition says that AI is a simulation of human intelligence such as learning and problem-solving.

Machine learning is a subset of artificial intelligence. And that is to say that all machine learning is artificial intelligence, but not AI is machine learning. In machine learning, we apply AI to create systems that learn and improve from experience.

Importantly too, as you'll see later in the session, machine learning uses data, all kinds of data, to feed its algorithms and then to find patterns within that data. Machine learning is certainly a complex field and one where advancements are made very quickly. As we'll talk about later, this is actually one of the reasons many companies have difficulty getting started with AI. In machine learning, data scientists use a variety of techniques or methods to accomplish their goals. And whether these methods are regression, clustering, neural nets, or natural language processing, they all have the same objective in common, and that is to turn data into information and information into insight.

Much more important for most of us than the specific methods used in machine learning is what we do with these methods and all that data to solve business problems. Let me give you a quick example of one use case. For many of our clients, reducing customer or member churn is a priority. According to Bain and Company, the cost of acquiring a new customer can be 700% higher than that of retaining an existing customer. They also found that increasing retention by 5% could increase profits anywhere from 25% to 95%. So there's a strong incentive for banks and credit unions to focus on this area.

In using AI to help reduce customer churn, we start with the data. This data needs to be both internal data and external data. Since the stimuli that can trigger churn can be either internal or external to the company, product, or service performance, individual relationships between customer and relationship manager, technology, competition, market dynamics are all important inputs. And we want to start with a 360 degree view of the customer's relationship with the bank or credit union in order to derive the most meaningful insights.

Next, we feed the data into our machine learning model in order to predict the likelihood a customer will leave an importantly the drivers of their churn. Difficult customer experiences, heavy fees, recent transactions, customer sentiment, or competitor offers all contribute to understanding the customer or member's intent to churn. The end result is a churn score for each customer, along with the predictors or drivers of churn and how they impact the customer. With this information in hand, relationship managers can have the right conversations with customers and members at the right time in order to keep them with the firm.

So how re financial institutions looking at AI? Well, for one thing, the Economist Global Banking Survey reports that there's going to be huge spending on AI, starting at $7.1 billion in 2020 and doubling that number by 2024. They found a majority of bank executives say new technology will be the main driver for their businesses over the next five years. And 77% of executives believe that AI will be the most game changing of these advanced technologies. Predictions like the one on the right-hand side of the slide that sales at banks deploying AI will grow from 2.5% to 5.2%

faster than that their competitors who don't use AI is what's causing all this attention. Now, I should point out that although these studies were on AI in banking, through other studies and anecdotally, we know that the situation is similar in credit unions as well. Now, let me give you one other view of the role AI is expected to play in financial services. This one is from a report by McKinsey and Company. And I pulled the quote from the report.

The authors conclude that AI and advanced analytics are becoming core differentiators for financial services firms. They offer opportunities to lower costs and provide better customer service. They'll make interactions more personalized and deliver value propositions tailored to the individual customer or member. Now, that sounds great. But there are a couple of notes of concern included here.

The first is the note the authors include about investments, suggesting that only financial institutions with scale will be able to afford this. Now, given the title of the session, you can probably guess that we would disagree. And we'll talk more about our view of how financial services firms of all sizes can take advantage of AI in just a bit.

The other note is that the scope of AI use cases is sure to expand dramatically over the next 10 years. And we believe this is absolutely true. It is one of the reasons, in fact, that we so strongly suggest that firms get started today with bringing AI into their operations. So that they don't get left behind by those that are leading the way. So challenges to be sure, but the report also strikes a hopeful note for midsize firms.

And that is that they possess key strengths, including connections to customers, agility, and rapid decision making that give them an advantage in their markets. What is not said, but that we hopefully frankly will convince you of, is that AI is perfectly positioned to make these advantages even stronger, giving midsize banks and credit unions who choose to use AI a leg up in an increasingly competitive market. So what about value? Let's start with a high-level number. According to McKinsey and Company in another of their studies, the potential annual value of AI in global financial services is over $1 trillion. Now, that is a very big number, and it's a little hard to make sense out of.

But another part of the study looks at the areas where this value comes from, which I think is really instructive. And this clearly shows that the value of AI to banks and credit unions is across all functions in the enterprise-- marketing and sales, risk, HR, finance, and IT. There is simply no area where AI is not delivering value.

How about some more specific examples of value? Here's a small sample of the value being delivered to financial services firms through AI. AI automation saves JP Morgan 36,000 hours of work by their lawyers and loan officers annually. Financial services institutions have reduced production costs 13% using AI. Where AI has been use, revenues have increased in these initiatives on average 17%.

Finally, specific to mortgage loans, AI has been used to improve collections over 30%. Now, we know that pre-COVID, many financial services firms are already seeing changes in how their customers and members wanted to interact with them. And we've seen these trends accelerated by the pandemic. The consensus now is that many of these changes will be permanent. Customers want a more personalized and proactive experience. They're looking for speed and simplicity in their interactions.

And as is true in so many areas today, there's not much patience for sub-par customer experiences. I wish I could attribute this quote, but unfortunately I can't. Still, it really encapsulates the challenges for businesses today. Whoever is doing it best is setting the bar for the rest of us.

And that bar just keeps going up. This is a challenge, but I would argue that it's also an opportunity as well. The good news is that AI can help financial institutions adjust to these changes and then go even further by allowing them to differentiate their offerings for this new type of customer and to provide the kind of experiences they're looking for. We've been seen for a while that customers want their financial institution to know them.

They want to be engaged, advised, protected, alerted, and excited. You can see that what is common across all of these is what I call the me component. Customers want all of this to be for them. They want their experience to be personalized.

They want service to be personalized and they want it to be proactive. Here's some more good news this is right in AI's wheelhouse. We can analyze member behavior through transactions, profiles, and interactions. Combine this with financial events and known and predicted life events, and through these insights, financial institutions can predict products and services best suited for a particular client and then proactively reach out to them with these suggestions. The key to delivering the value from AI is in what we often call use cases.

Think of these as the business problems that firms are looking to address through AI. We've talked about some. But they exist across all functions of the enterprise from customer management to marketing and sales to operations to workforce management to risk and compliance. Artificial intelligence can help in acquiring and retaining customers and increasing customer wallet share, increasing the efficiency of lending operations, improving compliance and decreasing fraud.

So with all of the benefits of artificial intelligence, what's preventing banks and credit unions from having done more with AI already? Well, to be honest, there are some challenges. First, firms often lack the skills and resources required to develop these solutions. And to bring these skills in-house is both difficult and expensive. And with the pace at which the technology and methods are advancing, firms trying to do this in-house often find themselves playing a constant game of catch-up just to stay up with those changes. Next, data can be a problem.

There may be a lot of it, but it typically is in silos spread across the enterprise. And bringing it together to support integrated analytics is a challenge. And that's even before bringing in external data needed to enrich the analytics. Finally, given all these things, how does a midsize firm find a way forward to keep up with their larger competitors? When it comes to that last point, the largest financial institutions seem to be acting out the old saying, go big or go home. These firms are investing tens and hundreds of millions of dollars in setting up their own AI capabilities.

For example, RBC two years ago announced plans to spend $3.2 billion on new technology. That includes artificial intelligence. The move was a strategic one for them and had the goal of attracting 2.5 million new banking customers by 2023. To accomplish this they plan to use technology to increase new clients at a rate three times more than the current rate.

TD Bank went even further and bought their own AI company, buying Layer 6 for more than $100 million in order to deliver their own AI solutions. So how do you address these challenges if you're a midsize firm without unlimited resources? Here's one view from Karen O'Leonard at the time a consulting with Bersin by Deloitte. It's a lot of words, but the most important bit is the part I've highlighted. The secret is to find a partner, one with the tools and experience and approach to help you accelerate your path to AI. A partner can do the heavy lifting for you when it comes to AI.

But looking for a partner to work with, Karen suggests you look for four things. First, out-of-the-box advanced analytics. You can think of these as AI as a Service. Second, connections to all of your data sources and access to external data. Third, lower cost, quicker time to value, and more agility in their approach. And finally, ongoing expertise and guidance, because experience has shown that your needs and the technology will certainly change over time.

An example of out-of-the-box AI is PKSI's ABA Solution. These solutions will include prebuilt AI and machine learning models that target specific use cases, data and model management capabilities, and visualizations and dashboards for delivery of insights derived from your data. It's also important that the solution be designed as a platform that will allow it to scale and grow as your needs grow.

Finally, as Karen points out, because of the nature of analytic solutions, as opposed to some other types of software products, where the product is installed and the vendor's work is essentially done at that point, it's important that the partner you choose be committed to a collaborative relationship, one that's willing to work with you to deliver the solution that's best for your needs both today and into the future. Pulling back the covers on a prebuilt advance analytics platform like ABA, you should find three main components. First is data sources. The platform should utilize many data sources, including internal and external data, structured and unstructured data, data from core banking systems, CRM, social media, surveys and even emails. And the solution needs to be able to mine big data, whether that data comes from bank transactions, emails, online chats, or other sources.

Next is the data science and analytics engine. Data repository, machine learning models, and data in model operations are at the core of this component. And finally, the solution should include prebuilt visualizations for delivery of data derived insights. Now, another key point to make is around collaboration and agility, both keys to selecting a partner, according the Bersin by Deloitte. When it comes to prebuilt analytics, the choice should be for openness and explainability and away from black boxes. In this diagram, you can see where the solution allows customers to add their own visualizations and even machine learning models if they choose.

The flexibility makes for a more agile and open solution. And the platform should be built on trusted technology. We built our ABA Solution on the SAS Technology Stack from data tools to data science and machine learning software to visual analytics for dashboards. So how might a solution like this look to the user? Here's an example of the third piece of the Advanced Analytic Solution, Visualization of Insights. We've taken operational data as well as output from our models and are displaying the insights here, so that at a glance an executive can see KPIs, trends, the status of operations, and importantly areas that might require special attention. For the branch account or wealth manager, this view provides a single pane of glass for a particular customer or member.

Through this view, the manager can immediately see the complete view of the individual customer. She you can see all of her customer's interactions with the institution, products, services, inquiries, and concerns, and most importantly, be presented insights that point towards potential churn or additional business opportunities with that customer. Now, the consolidation of data from multiple data sources to make up this view is also essential to providing the inputs for many key machine learning use cases, like customer churn and cross-sell upsell. So in summary, what we see in our work with banks and credit unions is that AI is increasingly seen as the key to being able to effectively compete going forward.

We see that the value of AI has been proven to be significant time and again in reducing costs and increasing revenues. And while the largest firms are investing tens of millions of in AI, with the right partner AI is available to midsize banks and credit unions as well, leveling the playing field and potentially allowing them to leapfrog even their biggest competitors. For reference, here's some places where you can learn more about AI and advanced analytics in financial services. And I'd like to thank you for joining us.

And please don't hesitate to reach out if you'd like to discuss this topic further. Stay well. [AUDIO OUT]

2021-05-24 22:35

Show Video

Other news