Seizing change in seas of change: a 2019 compass for AI in banking
Tumultuous shifts in the financial services industry over the past decade have cast many banks adrift in stormy seas of change. As they’ve increasingly digitized to meet customer demands for new services beyond traditional offerings, they’ve simultaneously paddled to keep abreast of ever-stricter customer data protection measures. Both issues make the other more complex.
Despite their heroic work to date, financial services firms will face the same competitive and regulatory challenges in 2019 as in the past several years. Significant ones include compliance around risk and fraud, while advancing customer experience and operational excellence. Navigating around the three-headed beast—competitive disruption from other established firms, fintechs and new digital-only banks—represents another big issue.
Thus the overarching mission for at least the next year involves digital transformation. Enter artificial intelligence (AI), which promises to forever transform banking and propel the industry deeper into the digital age.
State of the industry
Less analytically mature organizations are just catching up to traditional “big data” challenges, while other banks report considerable progress. Wherever a bank sits on this spectrum, data’s volume, variety and velocity grow rapidly, along with the expectation among executives and consumers that this data will help facilitate faster, more accurate decisions to reduce fraud and customer friction. Unfortunately, that expectation and the reality often fail to match.
At the same time, consumers are becoming more savvy and cautious regarding the use of their data. GDPR and other privacy regulations such as the California Consumer Privacy Act, along with 2018’s nearly constant data breach notifications, have heightened this sensitivity. As a result, data privacy has become business critical.
Though consumers cite banking among the industries they most trust to secure their personal data—second only to health care in a recent survey—even the most analytically mature organizations must remain vigilant with privacy and security, while delivering optimal customer experience. How can they protect customers and their data without resorting to overly cumbersome security measures?
Tremendous opportunity exists in 2019 to accelerate the move to digital banking. To do this successfully, firms must focus on customers’ expectations, unspoken needs and desires. What do customers say they need from firms? Do firms have the same punch-list? A recent BAI Banking Outlook survey shows that while the punch-list remains the same, bank priorities don’t align with consumer priorities. Once they’ve achieved alignment, firms can examine how analytic technologies such as AI and machine learning can meet customer expectations—and strategize accordingly.
A deluge of data
Banks will continue to adopt AI and machine learning technologies in 2019. Why? Perhaps the most urgent reason centers on “in-the-moment speed.” Despite having plenty of customer data, banks by and large lack the capacity for instant analysis and interpretation.
Data volume, and the pace at which consumers create it, still stands as one of the greatest challenges financial institutions face. The continuous date volume surge far outweighs the ability banks possess to capture and effectively use it. And the volume will only grow as banking becomes more digitized. Firms struggle to understand their most valuable data and the ways to always use it for their customers’ benefit. Getting their arms around those foundational issues will stand out as a critical task as banks try to navigate industry disruption in the short and long term.
Furthermore, it’s not just data volume that presents the biggest obstacle. Inexpensive technology to process billions of transactions is commonplace—but extracting value and insights from that data remains difficult. Advanced analytics paired with good data management practices can help detect threats and uncover new opportunities.
One common data management theme has emerged in 2019: that data equals the new energy source that runs modern businesses. To derive maximum value and help banks and other businesses run at peak, enterprises must iron out the kinks in data protection, even as they meet customer demands and realize benefits from emerging technologies.
Fighting fraud, stifling cyber crime
AI and machine learning will allow institutions to more effectively authenticate customers, improve customer experience and greatly reduce fraud risk, particularly in digital channels. We’ll see aggressive adoption of machine learning in intelligent process automation, financial crimes protections, compliance, cybersecurity and automation of real-time fraud strategies.
Specific to fraud and financial crimes, identity verification will remain a major focus. As one example, the problem of synthetic identities—where crooks create people who don’t exist to drain banks via bogus credit accounts—will continue to grow. Organizations will face the onslaught both during the application process and in the authentication of online users. Ironically, proper authentication has evolved to require ever more information about an individual as personal information is more tightly controlled and consumers grow less willing to share it.
Banks must strike a balance between strong fraud and security protections and the low-friction transacting that customers expect. That’s one area where AI and machine learning have major roles to play. AI helps organizations extract the maximum value from the information they have.
The bottom line is this: All financial institutions, especially those whose market is global, must use AI, machine learning and the streaming big data to meet the demands of customers, regulators and an increasingly changing market. Bankers cannot simply tread water—it’s imperative that they use the best technology to swim to the safety of the shore. Otherwise, who knows where the rising tide and the data tsunami could take them.
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