It’s impossible not to hear about artificial intelligence (AI) anymore. It’s on the news and social media, and nearly every conference includes a session on AI. But for those in the financial industry, can you really use AI’s machine learning techniques and still maintain your regulatory compliance? Will you lose that personal, human touch that so many of your customers love and appreciate? These are real concerns, especially for our community banks, and you may be wondering if AI and banking really go together.
There’s no doubt that AI can improve efficiency and productivity. Some examples of artificial intelligence in banking include analyzing large volumes of customer data in real time, making immediate decisions on creditworthiness, and detecting fraud. It can automate routine tasks, including compliance reporting, freeing up your team for strategic needs. It can even write reports, generate presentations, and craft marketing images.
But while the excitement may suggest jumping on the AI train, we want to caution our community banks to consider four main points first. As a leading provider of cybersecurity, risk assessments, and compliance solutions, we can help you unlock the full potential of AI in banking and finance in a safe and compliant way. Keep reading to know what you should consider before using artificial intelligence in financial services, and then book a call to see how we can help you.
1. Regulators Haven’t Issued Requirements Surrounding the Use of AI and Banking
While regulators haven’t provided guidelines and rules about AI and banking, they are certainly discussing it. The Fed, the Office of the Comptroller of the Currency, and the Senate Banking Committee all have made comments this summer on their concerns about the use of machine learning and AI. In October, President Joe Biden issued an Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence. This new directive will likely boost efforts already underway to police the use of AI with an emphasis on preventing privacy and discrimination-related concerns surrounding personal data and financial transactions. The main fears center around potential discrimination in lending and the potential for criminals to use AI to impersonate customers.
The National Institute of Standards and Technology, within the U.S. Department of Commerce, in January issued an initial AI Risk Management Framework. However, this framework likely will be updated as organizations evaluate the framework’s effectiveness in practical applications. This “Version 1.0” is a little like a testing—or beta—version.
An AI and banking report in the American Banking Association Journal quoted Acting Comptroller of the Currency Michael Hsu as urging banks to be cautious in implementing AI products. Hsu urged bankers to talk with their regulators as they start considering AI products “rather than engaging with them afterward.”
However, to be clear, at this point, regulators are at best only in the comment stage of promulgating any requirements around AI in banking and finance. We know the marketplace of financial institutions is full of competitors who may have the means to introduce AI products, so we assume your bank will, at some point, introduce AI products. But before you do that, here’s our second point.
2. Determine What You Actually Need AI for at Your Bank
What problems do you think AI can solve at your bank? How can AI help your bank? What exactly would AI help you do?
The application of AI in finance begins by looking at what AI models can do—generate text, images, audio, and data on command from a database. The Large Learning Model of AI uses deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets.
Then, think about where you have bottlenecks or unsatisfactory customer experiences. What is routine and time-consuming? Where do you need answers quickly about trends and customer experience? Common suggestions for AI use cases in banking include fraud detection, generating compliance reports, loan trends, and customer service. But what specific issues does your bank face? Are there other ways to solve these problems?
There may be some benefits of AI in banking, but you need to be cautious about which relevant data sets the artificial intelligence applications have access to. And that leads us to our next point.
3. Develop Policies for the Use of AI at Your Bank
When using AI in the banking sector, there are concerns around the use of “poisoned data,” or data that has been corrupted or altered in misleading ways. Concerns also abound regarding how the AI software is programmed. Does it incorporate human actions overruling its decision making so it can “learn”? How is the dataset the artificial intelligence application uses protected? Can a hacker corrupt it? These are all elements that should be addressed in policies governing the implementation and use of AI at your bank.
And how will you prevent the use of AI against your bank and its customers? What protections can you enact to keep a criminal from pretending to be a large customer and fooling your AI platform? What happens if “learning” corrupts a database? How will you check and realize there’s a problem?
All of these AI and banking questions are real concerns for banks across the country. And that’s why it’s more crucial than ever to partner with a team that can ensure your bank remains protected from negative AI use cases in banking, which is our final point.
4. Consider Using Outside Expertise
Your community bank is already up against strong trends in the marketplace from larger competitors, who likely have staff on board who are experts in artificial intelligence in financial services.
Here, you can realize the benefits of AI in banking for your business by leaning on reputable fintech companies, including, perhaps, your core processor. They likely have the resources to test and refine AI software to meet banking requirements and secure regulatory approval.
But beware—there are many startup companies in the marketplace offering AI-powered fintech for banks. Some are already bankrupt—or heading there, thanks to typical market forces, so you want to turn to a dependable organization for these services.
Even then, you will likely need to understand the workings, existing systems, and internal limits of the AI product and how it measures against that NIST framework we mentioned above. You may have someone on staff who grasps all the ins and outs of compliance with AI—or you may not.
If you don’t, consider consulting with a company such as ImageQuest, which routinely assesses IT and AI services for our clients. We can help you choose the correct solutions for your bank’s specific issues—and, in some cases, get those solutions customized for you. Contact us today to learn more examples of artificial intelligence in banking and see how we can help you.
Is the Application of AI in Finance in the Trough of Disillusionment?
The research and advisory firm Gartner has coined the phrase “Gartner Hype Cycle” to represent the social reaction to new technology. The cycle includes the “Innovation Trigger,” or introduction of a new or breakthrough technology, followed by the “Peak of Inflated Expectations.” We think AI hit peak excitement about a year ago.
Now, we think AI is in Gartner’s “Trough of Disillusionment,” where a new technology fails to deliver on its early promise. To be sure, the Hype Cycle continues as more people work with and understand new technology, leading to the “Slope of Enlightenment” and, eventually, the “Plateau of Productivity.”
But for now, we don’t want our community banker friends to be disillusioned over cost, mistakes, and disappointment with their plunge into AI. We think if you follow the measures above, you will be ahead of the curve when using AI in the banking sector.
Partner with a Cybersecurity and Compliance Expert Today
As the banking industry navigates the surge of AI tools, it is important to consider the current landscape and develop a strategic approach. While regulators have not yet issued specific requirements surrounding the use of AI and banking, discussions and concerns are underway regarding potential discrimination in lending and the misuse of AI by criminals.
Given the complexity and evolving nature of AI technology, community banks benefit from partnering with external experts. Consulting with organizations like ImageQuest can help banks assess their IT and AI services, choose the right solutions, and customize them to address specific needs. By leveraging the benefits of AI while addressing its challenges, banks can position themselves for success in a rapidly changing digital era.
Take the next step in your AI journey and book a call with ImageQuest today. Let us help you navigate the complexities and harness the power of AI in your banking operations.
Resources:
- https://www.wsj.com/articles/hackers-with-ai-are-harder-to-stop-microsoft-says-42d94da2?st=3k3t9rjune4bcbg&reflink=desktopwebshare_permalink
- https://www.nist.gov/itl/ai-risk-management-framework
- https://bankingjournal.aba.com/2023/06/hsu-banks-must-work-with-regulators-to-manage-technology-risks/
- https://www.gartner.com/en/research/methodologies/gartner-hype-cycle