Artificial intelligence may be the future of community banking
Recent technological developments — such as artificial intelligence (AI), robotic process automation (RPA) and machine learning — are rapidly changing the way we do business. And the banking industry is no exception. Although large banks were the first to embrace these technologies, an increasing number of community banks are now recognizing their value. It may be some time before smaller banks can afford these technologies, but their potential benefits shouldn’t be ignored.
At first glance, AI and automation may seem inconsistent with the personalized attention most community banks rely on to distinguish themselves from their larger competitors. But in fact, these technologies enhance a community bank’s ability to personalize a customer’s experience.
Of course, technology can’t replace human judgment, but by automating and streamlining routine tasks, it can free up staff to focus on what they do best: onboarding new customers, developing personal relationships with current customers, and educating all customers about products, services and promotional opportunities.
Take advantage of new technologies
The potential uses for AI, RPA and other new technologies are virtually limitless. For instance, community banks can use these technologies to:
Open accounts. Some banks are using RPA to automate the account opening process and even accept loan applications. It may take a staff person only five or 10 minutes to open an account. But when you consider the many thousands — or tens of thousands — of accounts opened every year, automating the process can save a significant amount of staff time. Plus, automated systems can help ensure all required information is collected.
Change addresses and other information. Typically, when a customer calls a bank to change his or her address or other information, an employee must go through multiple computer screens in the bank’s system to process the change. With RPA, once the initial information is input, the system can complete the remaining steps automatically, saving time for both customers and bank staff.
Detect BSA/AML crimes. Banks can use AI and machine learning to support their Bank Secrecy Act (BSA) and anti-money laundering (AML) compliance efforts. For example, these technologies can sift through enormous amounts of transaction data and identify suspicious behavioral patterns that would be virtually impossible for humans to detect. And by minimizing the number of false positives and negatives, they can help ensure that investigators focus on truly suspicious activities rather than legitimate transactions.
Improve cybersecurity and fraud protection. The ability of AI to mine huge amounts of data and quickly spot anomalies makes it a powerful fraud detection tool. It’s particularly effective when it comes to cybersecurity. A bank’s IT department may receive hundreds of thousands, or even millions, of cyber threat alerts every month — too many to investigate effectively. AI can comb through this information and alert the bank to potential threats that require immediate attention.
Mind the data gap
As advanced technologies become more commonplace, one of the biggest challenges for community banks will be to ensure they have sufficient data to use these technologies effectively. To do their jobs, AI and machine learning require large amounts of data from which to learn and train. For large institutions with millions of customers, this generally isn’t an obstacle — but many community banks lack the data they need to ensure these technology solutions are effective and accurate.
To prepare to take advantage of the many benefits offered by AI and machine learning, banks should start by taking inventory of their own data. If necessary, banks can supplement this data through data-sharing arrangements or by purchasing data from third parties. A relatively new technique that shows promise is “synthetic data,” which is generated by applying algorithms to a bank’s existing data.
Ready for prime time?
AI and automation have great potential, but it may be some time before community banks fully embrace the technology, which is expensive to implement and maintain. In addition, there may be significant costs associated with gathering the data needed to run it effectively. Nevertheless, it’s important for community banks to monitor developments in this area and consider how these technologies might improve their businesses down the road. © 2020