Banks view digitalizing credit-risk function as urgent but face people challenges

Customer experience focuses are heading beyond call centers, chatbots and mobile banking to include prioritizing credit-risk infrastructures.

By John Hintze

Banks are urgently seeking to apply advanced analytics to their core credit-risk function, both to catch up to nonbank lenders and also to meet customer expectations of more personalized and seamless services.

Banks are by no means strangers to advanced analytics such as artificial intelligence and machine learning. However, they have typically applied the technology to specific business lines or products. Now they’re focusing on core bank functions and especially credit risk, although they are likely to face significant challenges, ironically, in finding necessary human expertise.

In fact, 79 percent of respondents to a 2022 SAS survey of members of the Global Association of Risk Professionals placed a medium-to-high priority on credit-risk transformation—digitizing the credit-risk function—with 55 percent planning to complete such platforms in less than two years.

And just over half of respondents said they are pursuing credit-risk transformation to further their business objectives, especially to optimize credit decisions, compared to just 27 percent checking off return-on-investment as the primary objective. Both the speed and de-emphasis of ROI are unusual for traditionally pragmatic and cautious banks when pursuing projects fundamental to their businesses, SAS pointed out in its survey report published in December.

Enhancing customer experience

One driver has been the recently uncertain economic, financial and geopolitical environment that has prompted the realization among risk managers that the requirements they’ve been pursuing over the last several years are often insufficient.

“Resilience is the key here, because risk managers are realizing that what they’re doing today will not be enough to jump ahead of the competition or to deal with future crises,” says Zeynep Salman, who heads up risk decision advisory activities at SAS

Salman added that the other driver, to be expected, is ongoing digitalization. Banks have focused on enhancing the customer experience in areas such as call centers, chatbots and mobile banking, and now they are prioritizing credit-risk infrastructures.

Rakesh Gajjar, global head of integrated credit risk practice at CRISIL’s global research and risk solutions, said the emergence of buy-now-pay-later and other non-bank, fintech lenders whose platforms are built on newer technology are driving bank credit-risk transformation, which is spreading beyond consumer banking to commercial lending and wholesale banking.

“We see credit-risk transformation playing out on a wider scale across different businesses at banks, driven by increasing competition from fintechs, gaps in monitoring infrastructure, emphasis on sustainability, a focus on driving efficiencies, and tighter regulatory asks [and]heightened supervisory scrutiny,” Gajjar says.

Credit-risk transformation is fundamental to banks’ operations and business and so a bigger challenge to pursue. However, it also promises to give banks a competitive edge over non-bank lenders and firms that may offer competing products but not banks’ broad set of potentially interconnected financial services. That edge will come from developing a platform that connects those products and services with credit-risk decisioning, applying advanced analytics to personalize those decisions and meet customers’ specific needs, Salman says.

On the consumer banking front, for example, the platform’s advanced analytics may determine that withdrawing cash from an ATM is atypical behavior for a customer and indicates financial challenges. Upon further analysis, the platform can then offer the customer an unsecured loan, in this case via a text while the customer is still at the ATM. The text would include a link to a personalized web page that allows the customer to choose from a selection of loans and adjust the terms if appropriate. And if the customer has questions, he or she can be connected to a chatbot for answers.

“To do all this, the bank’s platform needs to connect the systems so, for example, the chatbot can be fully informed of all the customer information and make credit decisions,” Salman said.

SAS survey respondents—more than 300 holding executive and administrative positions at financial institutions, large and small—see banks’ credit-risk transformation as urgent, with 55 percent anticipating completion within six to 24 months, and another 31 percent in three to five years. The survey also found advanced analytics to be the area respondents are most struggling with. Even though a significant majority of banks that have succeeded in incorporating AI and machine learning have experienced improvements across different credit-risk functions, such as predictive power scoring, data quality and operational productivity.

Challenges ahead

The challenges banks face pursuing credit-risk transformation may stem less from figuring out how to incorporate the technology and advanced analytics into their businesses than finding the human staff to implement and maintain it.

“The role of the credit-risk manager has changed dramatically since 2010—the only thing they now have in common is the title,” says Stella Ioannidou, senior manager of research at the Josh Bersin Co., leader for the Global Workers Intelligence project in banking.

GWI uses a combination of data, research and education to advise business and human-resource leaders on workforce trends globally. A key finding in the banking vertical, Ioannidou adds, is that in a post-pandemic world, physical branches are out of favor and digital servicers are in. Management understands that, but banks must connect services that currently function on isolated, legacy systems.

“Most banks cannot effectively address this need because they lack the next-gen tech and digital capabilities needed to materialize that strategy,” Ioannidou says.

She adds that the “next generation of skills” for credit-risk professionals includes proficiency in data analysis, predictive and risk analytics, as well as data visualization solutions such as Tableau and the Python programming language.

“The next-gen credit-risk manager is closer to the skills profile of a data scientist,” Ioannidou says, adding that the role of credit-risk executives is shifting from understanding current market dynamics to modeling those dynamics and predicting where they will lead. “Now they have to understand not only current data and have reporting systems in place, but also predict patterns, model behavior, and adjust course based on what they’re seeing.”

The “trailblazer” banks identified by GWI that are furthest ahead in terms of credit-risk transformation have 1.3 times more staff in IT-related positions than traditional banks. That’s to be expected, Ioannidou says, but those banks also have 1.6 times fewer employees in middle- and back-office positions, reflecting their lead in automating and tech-enabling functions such as credit risk.

GWI’s analysis focused on large consumer banks globally, Ioannidou said, but the pace of change in CRT currently is “frenetic” and even smaller banks must have at least some level of advanced analytics and forecasting to understand their credit risk.

“That means ensuring their credit-risk staff have the necessary advanced analytics skills,” she said.

John Hintze frequently writes for ABA Banking Journal.