As AI gap widens, midsize banks must act now

Less than 20% of midsize FIs have AI plans for 2020

Ask a banker about technology and innovation, and more than likely, you’ll hear a reference to artificial intelligence (AI). Whether it’s the promise of algorithms, the complexity of big data, or the dreaded rise of the machines, the buzz about AI makes the finance industry sound like Silicon Valley. 

The potential benefits are numerous:

• Transforming your customer experience with faster, more relevant information 

• Injecting efficiency into systems and processes

• Improving fraud detection and risk management 

• Gaining new insights from previously trapped data

Unlike Silicon Valley, however, the challenge at most banks is turning that talk into action. The biggest risk—especially for midsize banks and credit unions—comes from standing still. 

Take a look at these statistics:

At large banks 

• Over 70% of large global banks studied have implemented AI for front- or back-office functions.¹

• The seven largest U.S. banks each have AI strategies, teams and projects in place, with billions of dollars invested to date.² 

At midsize banks

• Just 2% have deployed chatbots, machine learning or other AI technologies. 

• Only 13% plan to invest in or implement these tools in 2019.

• For nearly half, AI is “not even on the radar.”³ 

No one’s arguing that smaller  banks should compete dollar-for-dollar with their mega-sized counterparts. Rather, the gap sparks concern because of how AI works. The longer FIs wait, the harder it becomes to catch up. FIs that start early gain a head start of months—even years—to gather data and “train” their self-learning, intelligent applications. The longer AI operates, the smarter and more useful it becomes

Current AI projects support customer service, operational efficiency

AI refers to a number of techniques that enable computers to emulate human behavior. These can include machine learning, intelligent automation, natural language processing, chatbots and virtual assistants, and certain types of robotic process automation. (In future articles in this AI series, we’ll take a deeper dive into these techniques.)

For now, know that the power of AI stems from its ability to harness your bank’s data in three key ways: to analyze, to act, and to improve by self-learning.

Among the largest U.S. and international banks, the greatest focus today is on conversational interfaces, such as chatbots and virtual assistants.

• In 2017, the  Alberta Treasury Board debuted Pepper, a humanoid robot designed to interface with customers at the branch.

• Bank of America’s Erica, an AI-powered financial assistant embedded in its mobile banking app, recently passed one million users.

• Numerous other FIs are leveraging AI-driven chatbots and algorithms to support existing customer service channels with faster, more consistent intelligence.

AI initiatives for back-office efficiency, automation and process excellence take a close second in bank priorities. In 2017, BNY Mellon became an early AI mover. The bank implemented more than 200 bots to automate routine activities, which resulting in improved accuracy and shorter processing times.

This AI use case complements solutions like  Integrated Receivables, which leverages machine learning to automate matching of electronic remittances and improve straight-through processing.

AI succeeds with small pilots and smart resources

There is good news for FIs that have yet to embark on their AI journey. Making progress does not require a Herculean effort; it typically begins with identifying your organizational goals, readying your data and finding the right partners. Nearly all the examples showcased in this article, in fact, occurred with the help of  FinTech resources.

Secondly, most AI projects start small. Pilot programs and innovation labs give banks and credit unions a chance to test, learn and refine your AI initiatives for a relatively small cost, before seeking funding for full-scale rollouts.

Analysts project a sizable opportunity for the financial services industry as a whole: 

• Up to $1 trillion in productivity gains and lower overall employment costs by 2030.

• A 34% increase in revenue and 14 percent net gain in jobs for companies that effectively utilize AI.

Banks that act now can capitalize on the power of automation and intelligence to truly transform their organizations.

Up next in our blog series on AI: How well can you speak AI? From natural language processing to neural networks and deep learning, test your knowledge and explore the fundamental technologies that encompass AI. 

¹Mandal, D. (2018, April 30). How Banks Are Using AI as a Tool for Transformation. Medici.

²Sennaar, K. (2019, March 5). AI in Banking – An Analysis of Americas 7 Top Banks. Emerj. 

³Cornerstone Advisors. (2019). What’s Going on in Banking 2019.

4Manning, J. (2018, July 4). How AI is Disrupting the Banking Industry. International Banker.

This content is accurate at the time of publication and may not be updated.