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Resiliency Key to Organizations’ Ability to Thrive in Changing Times

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Alex Kwiatkowski

Director, Global Financial Services Industry Marketing, SAS

Eric Lacombe

Lead Solution Architect, Big Data & Analytics, National Bank of Canada

Change comes at us from all directions. For banks, the ability to adapt and thrive hinges on making resiliency a priority.

The banking sector has seen its fair share of fast and drastic change over the past two decades — from the 2008 financial crisis to the pandemic, to the more recent bank collapses. 

While banks have shown a remarkable ability to bounce back from these various crises, they continue to face unprecedented challenges from a political, economic, societal, business, and regulatory perspective. In addition, banks must also adapt to changing market conditions. 

“There have been so many injections of new competition into their markets in recent years like FinTech, WealthTech, and InsurTech,” says Alex Kwiatkowski, Director, Global Financial Services Industry Marketing at SAS, a global provider of business analytics software and services to large organizations. “On top of that, many consumers are now looking for banks that align with their environmental and social values.”

Resiliency on the minds of banking executives

The key to surviving, thriving and staying relevant in this environment is resilience, a theme that is on the minds of many bank executives, according to the SAS Resiliency study conducted in December of 2022. Many respondents reported that they felt unprepared for what could come their way, admitting to their own knowledge and confidence gaps and the need to close them.

The report identifies five principles needed to keep pace with or outpace market changes — agility, innovation, equity and responsibility, data culture and literacy, and curiosity — collectively referred to as “The Resiliency Rules.” Ninety per cent of the executives surveyed cite data and analytics as critical for implementing each of The Resiliency Rules and view it as important to a resiliency strategy. Additionally, the report indicates that business that identified as very resilient (93 per cent) were more likely to implement a data strategy than their less resilient counterparts (22 per cent). 

Good business intelligence key to managing risks

Data analytics can support organizational resiliency by providing valuable business intelligence (BI) to combat risk. “Fundamentally, data tells you where your vulnerabilities are and takes the guess work out of everything,” says Kwiatkowski. “If you have something of concern in your risk management practices, your fraud and financial crime detection, or customer service, there may be a gremlin in the system that you just can’t spot with the naked eye, so data and analytics is integral to identifying that,” he says.

It also unlocks the pathway to remaining relevant in constantly changing market conditions. “If your data is constantly increasing, you need analytics to tell you what the next best offer or action should be,” says Kwiatkowski. “In the era of ChatGPT or ChatBox with the use of artificial intelligence (AI), you want to make sure that algorithms are making the right decisions on your customers’ behalf in a way that’s fair, accountable, transparent, and ethical,” he says. “With many customers concerned about the bank’s social and environmental impacts, data analytics is an opportunity to use data as a force for good and identify how your firm is managing these risks and exposures so you can continue adapting to the needs of your customers.” 

Shedding legacy systems to avoid data security risks

Legacy data systems that lack the capacity to handle and adapt to today’s complex risk and data challenges are a significant barrier to resiliency. “In a digitalized world where technology is at the forefront of what we do, executives need to understand that there are risks to not keeping their analytical models up to date, to not keeping their underlying technology architectures robust, or using their technology for things it wasn’t designed to do,” says Kwiatkowski. 

However, moving to a modern data analytics system is not an easy sell or simple transition, as Eric Lacombe, Lead Solution Architect, Big Data and Analytics, National Bank of Canada, can attest. Lacombe led the Bank of Canada’s recent five-year modernization journey, a process that was fraught with challenges, myths, and significant operational risks inherent to the legacy environment. “We were constantly hearing internally that failure might be around the corner, that the current SAS system was not a crown jewel, not widely used in the bank, and that we’d have to migrate over 3 million data sets from the legacy server which implied a lot of risk,” he says. 

With the increase in regulatory constraints, relying on more model revision and manual processes implied a lot of risk, which was not where we wanted to go.

But the internal resistance slowly diminished as the potential for failure by not modernizing to an analytics platform, such as SAS Viya, as well as the missed benefits, including infrastructure cost savings and model building and management, became to hard to ignore. “With the increase in regulatory constraints, relying on more model revision and manual processes implied a lot of risk, which was not where we wanted to go,” says Lacombe.

Navigating the old while modernizing — a juggling act

Speaking at the recent SAS Innovate conference in Orlando, Florida, Lacombe shared his key lessons learned for a smooth modernization process. “First is to clean up your data before conducting the initial assessment as this provides you with more precise estimates on the amount of data to manage,” he explained.

“When we did our initial assessment, we realized we were trying to assess too many SAS programs at once which resulted in assessing duplicate objects. After cleaning up the data and assessing for a second time, we realized there  were only about 8,000 critical programs to migrate to the new system, not 3 million as originally thought.” 

Lacombe also recommends using a migration roadmap to focus and structure the migration sequence and socialize the strategy and approach. “This allows you to justify the benefits of each major milestone as you move along,” he says.

Key to strengthening the business case with internal and external stakeholders was building a proof of concept (POC) and proof of value (POV.) “We collected real use cases and involved actual users right from the beginning. We then socialized the value to all the possible various stakeholders, which proved we were able to run that critical use case in the new system in a matter of a few weeks,” says Lacombe. 

“We’ve made a 180 degree turn from the past when it comes to our resiliency as an organization,” Lacombe said while citing one of his favourite poets. “If you don’t know where you’ve come from, you don’t know where you’re going.”

To learn more about SAS and SAS VIYA, please visit sas.com/viya.

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