Banking’s Next Analytic Advantage is a Hashtag

Global banking dynamics and norms are perpetually evolving and frequently shaped by technological advancements. How competently a modern bank can horizontally and vertically integrate new methodologies defines the relationship between its growth capacity and technological prowess.  Whether directly impacting banking or the industries and economies being served, banks will always be affected by the advancements of each era.  Now, this data revolution-generated wave of change provides information immediacy and the secondary benefits of predicting business outcomes, all while meeting financial reporting requirements. 

Responsibly and purposefully, banks historically invest in data for its valuable insights.  For example, banks of all sizes principally inform their strategic-level retail information with two aggregated data sources: deposit balances and credit scores. That paradigm has worked well for decades; monitoring the volatility and relationship of these indicators is predictive of future losses.

Unfortunately, banks that continue to rely heavily on just these two signals will soon fall behind what modern technology has to offer. Specifically, the most savvy bank CFOs must invest in new forms of data, and that data, in 2024, is social listening.

Social listening is the process of monitoring digital conversations to understand what customers are saying about a brand or industry online.  It involves analyzing gathered data for insights into needs, preferences, and perceptions.  This capability enables a new paradigm for informing business decisions. 

Social listening is particularly compelling for bank marketing strategists, who can deftly wield open-source and purchased data to better allocate their marketing budgets, resulting in higher profit per new customer. For example, imagine a large regional bank that is deciding which new market to expand into with a large sponsorship event, like the Macy’s Day Parade. Wouldn’t it make sense to invest time and resources into local internet searches to match prospects’ needs and bank campaigns?

An analysis by Deloitte confirms the effectiveness of such targeted marketing strategies in banking, citing the 5-year change in preferences away from branches in favor of digital banking.  Across all age groups, Americans who do not use online banking fell by 30%. While largely due to an improved online banking experience, this sustained change in behavior was enabled by targeting marketing.  This is the new level of how strategic decision-making becomes reliant upon software modeling to drive growth.

Tomorrow’s banks will need the same principles.  While the norms of structured practices continue, opportunities for optimization exist across any bank’s key activities.  In retail banking, existing software already streamlines operations and enhances customer experience.  AI-driven tools are used for personalized marketing and predictive analytics, helping banks optimize their branch footprints and tailor services to customer needs.  These tools enable breakdowns and highlights of behavior patterns, which incorporate customer feedback, to hone hybrid branch staffing models and coverage maps. 

Analysis software, and more importantly, the math, play a crucial role in managing the credit card and mortgage portfolio.  While advanced algorithms assess credit risk, machine learning models predict loan defaults.  Both capabilities enable banks to manage their portfolio more effectively, highlighting how and where to focus and where to pull back.  Predictive risk assessments of the future will use up-to-date market activity, and advanced portfolio monitoring will shift entirely from granularity management to signaling and correlation analyses.  This means there will be a natural plug-in for social listening data to assist in forecasting portfolios and, more importantly, default rate volatility.

“Skate to where the puck is going to be, not where it has been.”

– Wayne Gretzky

Asset Management and Institutional Investments stand to be among the most disrupt-able lines of business.  In this area, modeling already facilitates sophisticated investment strategies.  AI algorithms analyze market data to guide investment decisions and research, while big data analytics offer deeper insights into market trends, statistical drivers and investor behavior.  Tomorrow, intelligent modeling will optimize for diversification and volatility characteristics, then dictate an intricate financial action plan with a concrete strategic direction.  A bank executive who wants to prove a modern edge will be dedicating investment resources towards data acquisition strategies that create advantage, including social opinion and listening data.

Transformation amid these actions will demand real-time information. Commercial banking and syndications will contend with the transition from two sides.  The use of blockchain and AI in commercial banking has revolutionized syndication processes, enhancing security and efficiency.  Predictive analytics helps banks identify viable commercial segment opportunities and manage syndicated loan portfolios. However, at the institutional level, banks must develop more robust analyses to understand the dynamics within the industries they serve. 

This leaves no chance social listening will not be explored as a tool in corporate banking.

Highlighting how the visibility into the bank partner ecosystem is aided by insights and analyses of the challenges, fast adopting banks will reap the greatest rewards.  The opportunities found through analyses will draw on a multiplicity of syndications’ market activity data threads.  The quality of foresight will become the determining factor for choosing which banks lead their syndicates.

As momentum builds with software capability finding business applications, capital markets will ramp up how it to uses these new capabilities to navigate asymmetrical growth patterns in its business.  Perpetually serving as the variable ballast of many banks’ liquidity management strategies, the trading floor will embrace new tools–especially AI and machine learning, which have already significantly impacted how analyses is conducted and by whom.  Algorithmic trading, automated risk management and real-time market analyses are just a few examples of how technology drives operational efficiency in this sector.  Next will come the codification of methodology when incorporating new data sources for smart scenario analyses, followed by the intuitive ability to interface with educated dashboards matched to policy and procedures.

The ability to make decisions that will drive banks toward ecosystems, environments, trends, and opportunities will become the differentiator for competitive banks.  Specifically, and maybe counterintuitively, bank management’s objective is to identify historically problematic combinations of factors that have not yet catalyzed into a new dynamic shift.  This is done by incorporating information on the industry’s environment, beyond the scope of bank operations alone.  The demand for insights lays the ground for the new data acquisition paradigm. 

Of course, no banker is going to turn down an additional source of data—but there are reasonable objections to social listening as a burgeoning economy. For one, it’s an unproven data set. Historically, strong correlations exist among deposit balances, credit scores and default rates. Bankers don’t have nearly as much historical data to substantiate social listening for financial decisions. And for two, it’s difficult to translate into action using forecasting models without identifying drivers to monitor, such as a spike in a tracked hashtag.

However, both objections are myopic.  First, when banking leaders overlook the magnitude of not only economic data, but also industrial and demographic drivers, they miss out on a grand-scale holistic view of an ecosystem they have proprietary access to. Second, the volume and age needed to substantiate social listening doubts as a viable data source overlooks the most important factor: its accuracy.  While time will help answer the questions regarding true correlation versus causation, the immediately available math says this new indicator is on the money.

Creating models from opinions, clicks, online activity, and public opinion will require its own methods.  While new, banks that organize and orchestrate the available information will blaze a trail.  Their methods will blend and blur over time to create a new banking behavioral set, optimized and tested by the entire financial community.  The question is now, which banks will find decision-making benefits that drive market capture?

Final Thoughts

Take Finance further in banking with insights empowered by software advancements and capabilities that help banks gain the next competitive edge in the industry:

• Enhanced Data Management and Integration:  Modern software allows banks to integrate and analyze data from various sources, providing comprehensive insights for decision-making.
• Incorporation of External Data:  The ability to include and analyze external economic and industrial data is crucial for making accurate projections and informed decisions.
• Software Instruments for Diverse Banking Purposes:  Applying technology across various uses within banking enhances efficiency and positions banks to seize timely competitive opportunities.