How AI and ML Are Transforming Banking and Capital Markets

The banking industry has long been dependent on technology, but innovations in artificial intelligence (AI), machine learning (ML), and automation are reshaping the sector in transformative ways. Business leaders know that AI and ML are no longer technologies of the future, but an established presence in our everyday lives.

In Workday’s “AI IQ: Insights on Artificial Intelligence in the Enterprise” report, a whopping 86% of respondents in financial services agree that leveraging AI and ML is a requirement to keep their business competitive, and two-thirds say AI and ML have already increased productivity and operational efficiencies. AI can help create financial sustainability, standardize process flows, and automate repetitive tasks so employees can focus on high-value activities. 

And while banking industry respondents understand the importance and urgency of AI and ML—73% of the decision-makers surveyed are under pressure to increase adoption or investments in AI and ML—there are still significant hurdles to overcome in a fast-changing environment. 

A new infographic from Workday and IDC reveals some of those hurdles. Banking leaders identified their organizations’ “biggest challenges in managing their systems, processes, and function in the past 18 months”:

• 48% selected the need to upskill, reskill, and cross-skill to maintain legacy knowledge, adapt to modern IT systems, and address changing customer needs.

• 45% selected improving data quality and availability while reducing non-value-added tasks to leverage data as a valuable organizational asset.

• 44% selected streamlining IT processes to increase organizational agility and meet the growing demand for actionable data and insights. 

All three of these challenges can be addressed by AI and ML. With increased connectivity, automation, and new technology, there’s been a dramatic shift around in-demand skills. AI can help human resources (HR) teams identify and tackle skills gaps to help their organizations build a workforce that’s ready for what’s next.

For banks, AI capabilities are particularly relevant due to the data-intensive nature of the industry. At its core, AI enhances our ability to leverage the large volumes of data generated in day-to-day business activities, enabling us to identify patterns and make predictions. It can drive value creation by automating routine tasks and streamlining IT processes, freeing up people to focus on more critical work. 

“The future is endless,” said Alejandro Barcena, vice president and head of finance systems at Cushman & Wakefield, about AI and ML in a recent podcast conversation. “But to me, the key is how we are going to be able to transform all the data that we have into a meaningful business opportunity, to add more value to our customers and to impact the life of our people.”

But if banks aren’t happy with the timeliness and reliability of organization-wide data they’ll use for AI and ML, or don’t trust that the AI will be used ethically, transparently, and responsibly, then they may not be able to reap the full benefits of AI. 

It’s a lot to think about and banking and capital markets leaders may wonder how and where to start. In this article, we share practical applications of AI and ML and how others are finding success with these technologies that help drive their organization’s goals.

AI for Finance in Banking

AI and ML are already transforming the finance function across the banking industry and helping deliver better employee experiences, improve operational efficiencies, and provide insights for faster data-driven decision-making. To modernize finance, teams need to eliminate manual, repetitive tasks to free up time for strategic work.

“Applying AI and ML is equally essential to the future of finance,” says Sayan Chakraborty, co-president and leader of Workday’s product and technology organization. “Finance teams can get help managing risk and eliminating inefficiencies by reducing what used to take months or weeks down to just hours or minutes.”

In Workday and IDC’s recent enterprise software survey, banking leaders worldwide selected the top AI and ML capability they’re using or considering using in their organization. 

• 36% automation (auto-skip approvals, self reconciling accounts)

• 29% anomaly detection (journal entries, expense reports, plans, outlier reports)

• 33% recommendations (customer payment matching, spend category recommendations, supplier invoice automation, and intelligent demand forecasting)

Interestingly, only 1.6% were not considering any of those options, showing the real need for the help AI and ML can bring. 

Traditionally, day-to-day finance functions—from detecting anomalies to identifying fraud to predicting outcomes—were done manually. Now, as finance faces increased expectations to work efficiently and provide strategic insight, organizations must adopt AI technologies that offer greater automation, integrity, accuracy, scenario planning, and data-driven predictions.

A big need for accounting departments is to reduce incorrect numbers or inaccuracies through anomaly detection, which is challenging with the sheer amount of data, invoices, and reports. One way to address this through ML is journal insights. ML helps surface erroneous journal lines for controllers—dramatically reducing the time and overhead spent by finance teams to close the books.

“Workday Journal Insights means one less thing for our end users to check off their list at the end of the month. They can correct issues and can fix them throughout the month. It makes it a continuous process,” said an enterprise resource planning business analyst at IMC Financial Markets.

Journal Insights uses machine learning to detect anomalies in accounting entries by comparing them to other entries for similar transactions. Because entries are flagged in real time, a user can correct potential reconciliation issues and fix them throughout the month in a continuous process—avoiding the end-of-the-month bottleneck. This enables accounting teams to spend more time on analysis and tackle more strategic initiatives.

For financial planning and analysis (FP&A), accurate forecasts are essential. With ML, FP&A teams can leverage historical data to further drive predictive demand forecasts. With real-time analysis, AI can help incorporate other data sets to drive greater precision. This opens the door to a new kind of planning that continuously learns from data and adapts to a changing world.

This investment can help banking and capital markets executives run sophisticated scenario planning while allowing more time for strategic analysis. It can also allow them to easily track details from various subledgers—a process that’s nearly impossible when using legacy systems—to quickly analyze critical metrics and create more nuanced risk analyses. 

“It’s about having a platform that’s going to give you the ability to react, to be agile, and to be resilient to all those changes in the industry,” says Viren Patel, strategic industry advisor for financial services at Workday.

AI for HR in Banking

Many financial organizations still face intense competition for top-tier talent. Attractive compensation packages alone won’t be enough to secure the highly agile workforce banks need. Financial institutions also must take a hard look at their company culture and the employee experience. Building and maintaining a top-performing, future-ready workforce requires cultivating and celebrating finance professionals who are strategic thinkers and lifelong learners.

By providing insights and predictions that identify and align skills with jobs, AI and ML transform employee data into a strategic advantage. This combination of data, technology, and automation can help financial leaders allocate resources more effectively, improve productivity, and make better use of their talent.

The entire sector is seeking workers with strong data analytics and technology chops to help them anticipate and respond to change. In fact, 98% of financial services organizations rank technological proficiency or the ability to adapt to new technologies as the top skill they need to develop over the next five years, according to a Workday and PwC industry perspective.

To stay competitive and fill their workforce needs, financial organizations must augment outside hiring with intensive upskilling. “Banks must expand their talent pool by tapping into skill adjacencies and proactively help employees develop new skills at speed,” explains Aurelie L’Hostis, a senior analyst at Forrester. 

That’s where AI and ML come into play—enabling organizations to focus less on traditional degrees and linear career progression to adopt a more skills-based approach. 

By providing insights and predictions that identify and align skills with jobs, AI and ML transform employee data into a strategic advantage. This combination of data, technology, and automation can help financial leaders allocate resources more effectively, improve productivity, and make better use of their talent.

The Workday Skills Cloud was built using neural probabilistic language models to map the relationships between more than 200,000 skills. On top of this foundation, we brought additional searching, reporting, measuring, and matching capabilities together. This enables us to connect skills to people and to their relationships to jobs, opportunities, projects, and much more. Think of Skills Cloud as the flywheel from which other skills-related features can spring.

As humans and AI collaborate ever more closely in banking, companies will be able to reshape how they operate, becoming more efficient, fluid, and adaptive. The key is to make sure that AI and ML are human-centered and augment people rather than displace them.

AI and ML: The Future of Work in Banking

Banks are optimistic about their sector right now: 64.4% of banking leaders worldwide believe that the banking and capital markets are increasing at moderate-to-significant growth. And many are focused on setting the foundation for long-term success. In that same survey, technology, customer satisfaction and innovation are the top priorities for improving or competing in the banking sector, ahead of even profitability and market share. 

According to a recent Deloitte survey of IT and line-of-business executives, 86% of financial services AI adopters say that AI will be very or critically important to their business’s success in the next two years.

Looking ahead, banking leaders must dive deeper in connecting AI and ML with business value and growth, while maintaining trust, transparency, and responsibility. Among other things, that will involve preparing their workforce via change management and upskilling, updating legacy systems, and improving the quality and accessibility of data.