How ready are banks’ risk teams to meet new IT challenges?

Banking is an industry heavily impacted by changing technologies, including , the Internet of Things (IoT), and artificial intelligence (AI). Sixty-three percent of the banking respondents to the Accenture Technology Vision 2018 survey say their organizations will make investments in AI over the next year, and 85 percent agree deeper integration into our day-to-day lives is shifting relationships between consumers and enterprises to forms of “partnerships.”

The pace of change has been rapid—perhaps faster than we might have anticipated. Back in 2009, the biggest challenge according to our Accenture Global Management Study was fragmented, inefficient technology not well suited to risk management needs. Steadily, over the years, technology needs have evolved, focusing more on analytics, big data and intelligent automation. But the pace of change seems to have increased exponentially.

The reasons for change are many and with the digitization of the industry, the opportunity for to innovate is everywhere. Where can risk leaders embrace the challenge?

Intelligent risk machines

While there is variation, we found that banks are experimenting with—and adopting—newer technologies at a significant pace.

Our 2017 Global Risk Management Study finds banks making positive progress already. Roughly a third of respondents are starting to use artificial intelligence (AI), robotic process automation (RPA) and machine learning (ML—35 percent, 38 percent and 33 percent, respectively). Risk is in the leading group, as at least one in four banks has begun using these technologies for their risk function (31 percent for AI, 25 percent for RPA and 26 percent for ML). Risk professionals are ambitious; interestingly, these same respondents acknowledge they aren’t using these “New Intelligent Technologies” (New IT) to full potential—signaling that their journey to higher levels of efficiency and cognitive insight is just beginning.

What is driving this? There is not one simple overarching reason. The most basic driver is a similar refrain we hear across all organizations—New IT can help relieve cost pressure. Fifty-five percent of our 2017 study respondents believe that applied intelligent technologies can deliver cost efficiency. Looking at specific technologies, we found that 35 percent of respondents believe that capabilities in big data and analytics can help their risk function address cost pressures to a great extent, while 47 percent believe these capabilities can do so to some extent.

However, the demands on banking risk functions are multifaceted, and a single reason for New IT adoption is, frankly insufficient. Risk functions find themselves processing and analyzing increasingly larger, disparate amounts of data at an ever-increasing pace, to understand an always-growing number of risks and correlations.  New IT can catalyze the evolution and sophistication of risk models used by banks, which in turn have the potential to increase both the quality and capabilities of the risk organization.


Cloud is one of the leading technologies we examined in our 2017 Global Risk Management Study, despite it being around for well over a decade. Unsurprisingly, among our banking respondents, 82 percent are using cloud in some form.

Digging deeper, however, this high number is a bit deceiving. Only 18 percent of respondents claim cloud proficiency, and many banks have not yet migrated core systems to the cloud. Over a quarter (26 percent) of respondents are only just beginning to use it, and 38 percent admit they aren’t using it to its full potential.

Now, nearly all the newer technologies we will explore in this blog series can or do reside in the cloud. This makes cloud proficiency essential for banks hoping to rapidly boost their risk management technology infrastructure. Leaders may encounter resistance when pushing for cloud—implementing it can take effort, and upfront expenses may seem costly (even though long-term cost savings can be significant), and changes to the IT operating model may be necessary, too.

Among our study respondents, we see good news. Since a strong majority have at least dabbled in cloud, it’s clear that banks see the potential. In addition, nearly 70 percent of study respondents believe that cloud, collaboration and workflow tools, artificial intelligence and machine learning can help their risk functions alleviate cost pressures to some extent. For banks, now may be the time to rethink the where and how of their cloud strategy and plan, from the perspective of both achieving cost efficiency and driving performance insight.

The promise of innovative technologies holds significant allure. And since banks are at different stages of adoption and maturity, and these technologies are not “one size fits all” solutions, can these technologies really deliver on their promise of significant cost and efficiency gains for each bank?

The rise of digital is a challenge that extends beyond New IT. What do banks need to have in place to be able to reap the benefits of these technologies and the disruptive opportunities being created by a digitized industry?

See my next post for a discussion of coordination .


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