Wells Fargo: The future of BFSI will rely on AI, NLP and automation

Wells Fargo’s artificial intelligence (AI) journey began in earnest in 2017 when the company created its AI team to create “technology that can help the bank deliver more personalized customer service through its bankers and on line “. 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 access to data. As Saul Van Beurden, Chief Technology Officer at Wells Fargo, said at the time, “The launch of our new digital infrastructure strategy is a critical step in our multi-year journey to transform Wells Fargo, enabling customers to do business with us more easily and create a better working environment. experience for our employees. The Wells Fargo of tomorrow will be digital first and offer easier-to-use products and services, and it all starts with speed, scalability and improved user experience through Wells Fargo’s digital infrastructure strategy. new generation that we are announcing today.

Not only has the bank made significant investments in technology, but it has also transformed the way it approaches the management of technology projects, moving from a traditional top-down approach to an agile and iterative approach. The goal? Create apps that both increase productivity and delight business users.

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

In a recent VentureBeat article, Chintan Mehta, CIO of Wells Fargo and Head of Digital Technology and Innovation, explained how they use NLP to perform sentiment analysis. As explained in the article, “His team applies short-term memory in natural language processing (NLP) and spoken language understanding to extract intent from phrasing. One example is in complaint management, extracting “specific targeted summaries” of complaints to determine the best courses of action and act quickly, Mehta explained.NLP techniques are also applied to website form requests that have more context than those for drop-down menu suggestions.

Commercially, Wells Fargo also offers these unstructured data processing capabilities to its business customers. In partnership with Dade Systems, Wells Fargo in 2021 launched a new AI-powered accounts receivable automation solution called Integrated Receivables. from payments and matching funds to invoices, Integrated Receivables can help achieve significant operational cost savings, reduce the risk of incomplete or inaccurate data entry, and accelerate cash flow. Artificial intelligence and machine learning technology allow Integrated Receivables to correct errors and improve matching logic over time, which can help businesses spend less time and resources on the application payment manual.

In partnership with the MIT-IBM research group, Wells Fargo has officially entered the world of quantum computing with the hope of developing new approaches to vector mathematics and generalized linear algebra. For a large commercial bank such as Wells Fargo, this could mean the ability to quickly recalculate prices for thousands of parallel transactions at once. Or advanced fraud detection systems capable of tracking millions of variables at once.

Although the technology is years or even decades away from being operational, as of October 2022 the company has already filed 38 quantum computing-related patents, signaling optimism that the technology will provide a competitive advantage. significant.

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