Wells Fargo’s artificial intelligence (AI) journey began in earnest back in 2017 when the company established its AI team to create “technology that can help the bank provide more personalized customer service through its bankers and online.” In fact, by 2019, the company had launched over 300 RPA automations. 

Fast forward to 2021 when Wells Fargo announced it was revamping its digital transformation strategy by marrying its multi-cloud approach with third-party data centers and leveraging Microsoft Azure technology to democratize data access. As Saul Van Beurden, Wells Fargo’s head of Technology stated at the time, “Launching our new digital infrastructure strategy is a critical step in our multiyear journey to transform Wells Fargo, making it easier for customers to do business with us and creating a better working experience for our employees. The Wells Fargo of tomorrow will be digital-first and offer easier-to-use products and services, and all of that starts with driving speed, scalability, and enhanced user experience through the next generation digital infrastructure strategy we’re announcing today.” 

Not only has the bank made significant investments in technology, it also transformed the way it approaches technology project management from a traditional, top-down approach to an agile, iterative approach. The goal? To build applications that both increase productivity and delight the company’s users. 

Unlocking the Power of Unstructured Data Processing 

Large financial institutions such as Wells Fargo are constantly inundated with customer service chats, emails and calls. With the help of natural language processing (NLP) and other unstructured data processing tools, Wells Fargo can now seamlessly collect, structure and analyzes large volumes of customer interactions.

In a recent VentureBeat article, Chintan Mehta, Wells Fargo’s CIO and head of digital technology and innovation, explained how they use NLP to perform sentiment analysis. As explained in the piece, “His team applies long short-term memory in natural language processing (NLP) and spoken language understanding to extract intent from phrasing. One example is in complaints management, extracting “specific targeted summaries” from complaints to determine the best courses of action and move quickly on them, Mehta explained. NLP techniques are also applied to website form requests that have more context than those in dropdown menu suggestions.”

On the commercial side of things, Wells Fargo also offers these unstructured data processing capabilities to their business customers. In partnership with Dade Systems, Wells Fargo launched a new AI-powered accounts receivable automation solution, dubbed Integrated Receivables, in 2021. As Danny Peltz, head of Treasury Management & Payment Solutions for Wells Fargo, told Payments Journal, “By automating the capture of payments and the matching of funds to invoices, Integrated Receivables can help produce significant operational cost savings, reduce the risk of incomplete or inaccurate data entry, and accelerate cash flow. Artificial intelligence and machine learning technology enables Integrated Receivables to correct errors and improve matching logic over time, which can help companies devote less time and resources to manually applying payments.”

Quantum Computing is the Next Frontier

In partnership with MIT–IBM’s research group, Wells Fargo has officially entered the world of quantum computing with the hopes of developing new approaches to vector mathematics and generalized linear algebra. For a large, commercial bank such as Wells Fargo, this could result the ability to perform rapid pricing re-calculation for thousands of parallel trades at once. Or advanced fraud detection systems that can track millions of variables at once. 

Though the technology is years if not decades away from being operational, as of October 2022, the company had already filed 38 quantum computing-related patents signaling its optimism that the technology will deliver significant competitive advantage.