The Evolving Role of Artificial Intelligence in Regional Banks

AI has proven to be a pervasive force in the banking sector. In NVIDIA’s recent The State of AI in Financial Services: 2023 Trends survey, 64% of survey respondents reported that their executive leadership team “values and believes in AI,” up from just 36% the year prior.

For large, well-established banks, implementing AI solutions and integrations is a no-brainer, as investing in AI at large institutions has proved time and time again to be a profitable endeavor. McKinsey even reports that AI can unlock $1 trillion (USD) in incremental value for banks.

Yet, for smaller regional and community banks, leveraging this technology is not so cut and dry.

Where large commercial and retail banks can attract a much wider pool of clients, regional banks are more limited in their geographic scope. Moreover, the clients that regional banks attract are rarely making as frequent or big of deposits as clients at the larger banking alternatives, giving regional banks less overall liquidity to work with when investing in emerging technologies.

This is just one challenge of several that can inhibit a regional bank from adopting an AI-first approach.

To overcome these challenges, regional banks must tailor their approach to AI to address the specific business hurdles their institutions face. If successful, regional banks might just unlock the key to not only keeping up with, but surpassing the larger competitors within their regions.

The Benefits of AI for Regional Banks

There’s no question that a large bank is inherently more capable of investing in innovative technologies.
Compared to their larger competitors, regional banks have a steeper technical hill to climb when it comes to implementing these same technologies. This can be especially true in regards to adopting AI at regional banks, as these institutions need to consider several key factors, including:

Data Sufficiency

A major challenge of AI for regional and smaller scale banks is determining if there is even enough data available to work with to create effective AI models. In the NVIDIA survey discussed earlier, 26% of financial services professionals named insufficient data quality and quantity as an issue in direct conflict with AI model training and accuracy. To solve this problem, the solution lies in better enabling open banking across regional banks so that mid-size institutions can share data and collaborate more closely.

Available Liquidity

Regional banks can sometimes have less available liquidity to use for technology and digital investments. While this can present a problem when it comes to the initial upfront costs of AI implementations, it can also indicate a smaller overall scope in terms of data needs. As such, it is crucial for regional banks not to compare themselves too closely to their larger competitors when it comes to the amount spent and invested in AI, as regional banks can likely make much larger organizational changes with smaller investments in the technology.

Specialized Talent

The third key challenge that regional banks face when adopting AI is finding the right specialized talent needed to implement, manage, and maintain AI technologies. This can be especially difficult for regional banks or branches operating in more rural areas with smaller overall talent pools. Thankfully, the rise of remote work and FinTech providers has opened up many new pathways toward finding the talent a regional bank needs for AI initiatives.

3 Key Ways AI is Used in Regional Banking

Though the three challenges above are certainly major hurdles for regional banks to overcome, AI still presents enormous value to these institutions. Despite these challenges, regional banks can still leverage AI to their organization’s benefit with the right resources and support in place.

The most essential piece of your AI strategy as a regional bank is a thorough evaluation of how to leverage various digital and tech-based solutions to make AI work for your unique needs.

Trying to mirror the AI strategies of banks twice your size or bigger is not the best move — instead, you must consider how to creatively employ existing technologies and digital resources for AI deployment.

With this in mind, here are three vital ways to optimize your banking environment to utilize AI for regional banks:

1. Solving Insufficient Data Problems via Open Banking

Regional banks can struggle to overcome the challenge of insufficient data when they remain rooted in legacy technologies and are segregated from the global banking ecosystem.

Yet, what if these banks could connect to that ecosystem with ease?

Open banking has taken many strides in recent years toward becoming an industry-standard in the banking sector. Regulators are even widening their acceptance of open banking technologies, with key pieces of regulation like PSD2 helping to set clearer requirements surrounding data transparency.

With open banking capabilities enabled, customers can share their personal financial data from all of their banking services and accounts, regardless of if those accounts belong to your institution or not.

AI and open banking are a match made in heaven.

By implementing an open banking or open finance solution, regional banks can connect to a larger pool of shared data from not just big banks, but also fellow regional banks. This provides them with quantity and quality of data to effectively leverage AI. Moreover, AI can then provide further value by supporting systems that can understand, organize, and act on data insights in real-time.

2. Enhancing Customer Service & Financial Inclusion with AI

Within their local communities, regional banks already have a leg up on big banks. Unlike big banks that operate on a national and even international level, regional banks have the flexibility to become more deeply rooted within a specific geographic region. In turn, regional banks have greater opportunities to get to know customers in their area on a personal level, all while understanding the context of the financial environment in that specific region.

Through the use of AI, the services your regional bank can offer to customers become even more tailored and personalized. Additionally, AI for regional banking may even hold the solution to improving financial inclusion.

For example, let’s consider how AI can enhance both the customer experience and the financial inclusion of the lending space in banking. According to a recent PwC report on social banking, AI can potentially increase credit approvals by 15% to 30% without causing a change in loss rates. The report further states:

“A core advantage in using AI [credit] models is they’re better suited to handle larger amounts of data, even if it’s poorly structured. Some models might consider over 500 variables, working to detect hidden interactions across these data elements to provide the insights and predictions required for decision-making.”

Implementing AI-powered credit models can help regional banks make decisions for lending services at a much faster rate and increase the pool of potential lending customers. As a result, banks can build more cost-effective lending processes while ensuring that overlooked customers with healthy credit receive the same access to lending services as existing customers.

3. Leveraging an AI-Enabled Platform Solution

Platforms are quickly becoming the name of the game in the banking sector. However, platform implementations are only the first piece of the puzzle. As the platform market becomes larger and more diverse, so is the range of highly-tailored platforms intended for specific banking solutions and services.

Without the proper platform strategy and roadmap in place, a regional bank can quickly become overwhelmed by the quantity of data coming in from various platform solutions. Plus, more platforms require a larger and more complex scope of platform management, which can put a strain on your bank’s IT team — who may already be juggling a variety of ongoing tech investments and implementations.

AI can be a key pillar of support in your bank’s platform strategy, particularly in terms of automation.

In McKinsey’s 2021 Building the AI Bank of the Future report, it is stated that AI technologies can help lower costs via “efficiencies generated by higher automation, reduced error rates, and better resource utilization.” By leveraging AI for platform automation, you can reduce both the scope of compliance and the complexity of platform management for you and your team.

Building Your Regional Bank’s AI Advantage with Strategic Partnerships

For regional banks, the key to unlocking the value of AI is finding the right strategic partnerships with FinTechs and other financial service (FS) providers.

The technologies needed to enable not just AI models, but also open banking, platforms, and other vital solutions can be costly to invest in independently. Comparatively, a strong strategic partnership can provide your bank with the ideal resources and talent needed for both implementations and ongoing management of various services.

At Exadel, we’re here to help.

Along with providing the solutions you need to enable digital-first open banking, Exadel has years of AI expertise to place at your disposal. Our AI capabilities can assist with many key business functions, such as:

• Fraud detection and activity monitoring
• Client onboarding and KYC/AML compliance
• Loan application processing
• Customer support
• Investment recommendations

Plus, Exadel has the connections you need to major platforms, like Calypso, Murex, Finastra and Moody’s Credit Lens. We can provide your regional bank with essential support through every step of the platform process, from implementation and production to customization and ongoing support.

Exadel’s FS and FinTech expertise is crucial for building an AI advantage as a regional bank.

Get in touch with the Exadel team today to learn more about our AI, open banking, and platform services.