This article focuses on how banks and financial service institutions can create a more meaningful customer experience, gaining an advantage in an intensely competitive marketplace where startup disruptors continuously threaten traditional practices with high-tech innovations. Keeping this in mind, we will dive into institutional banking’s response and why data today is arguably more valuable than all their clients’ deposits combined.

Indeed, financial services institutions (hereon referred to as FSIs) are only getting into their “data stride.” For example, leveraging predictive analytics to elevate the banking customer experience (BCX) to new heights is relatively new, as is using it to remove gaps in the way of a seamless banking customer journey (BCJ).

In addition, incremental gains from existing services are no longer in the FSI success formula as insufficient to sustain long-term stakeholder returns. No, things are severely more complicated, calling for groundbreaking strategies that are nothing short of revenue breakthroughs. The latter, in turn, only emerges from identifying and maximizing hidden opportunities. So, the open sesame to all this lies in big data and innovative machine learning as an inextricable collaboration.

How does big data help FSIs with BCX-driven operations?

Gone are the days of people wandering in and out of branch premises, standing in teller lines, or talking to personal advisors in satellite offices. COVID-19 ended all that, closing numerous branch locations and turning the “drive-in teller” option into a mainstream attraction. Moreover, online banking for transfers, fund wiring, and moving money between accounts (e.g., through Zelle) instantaneously came barging through the barriers into our internet-connected lives.

These changes didn’t appear on the off-chance that they would align with BCX. On the contrary, data insights paved the way. All it took was for IT techies to translate it into usable information. Now, data across a broad spectrum of market segments will continue to alter the banking landscape at a pace many of us cannot comprehend.

You are right to ask, “How is that going to happen, and what can I expect?” However, before we go into more detail, it’s crucial to appreciate that the data paradigm covers how FSIs:

• Think about risk
• Interact with their customers to promote brand loyalty
• Optimize synergy to open new services to established customers
• Make sense of diverse data to reveal hidden drivers
• Overlay “data-think” on all vital aspects of their business
• Personalize their services
• Map out crucial touchpoints pertaining to the BCJ (if engaging) or aborting them (if emotionally disruptive).

What are the most successful strategy ingredients in FSIs today?

The bottom line is several enterprises fail out of the gate because they don’t recognize the root causes of faulty data planning. Successful FSI strategists, on the other hand, laser-focus on the following:

• Data quality: No matter how impressive an algorithm is, it’ll be a matter of “garbage in, garbage out” if the data isn’t reality oriented. Thus, they think first of more relevant, practical, and higher-quality data sets before climbing on the ML, AI, and Data Visualization bandwagon.
• Data volume: Without millions of category transactions and BCX reactions to refer to, data readings are inevitably inaccurate. With data volume, this becomes one thing banks don’t have to worry about.
• Data structuring: Volume is only half the story on its own, the rest is structuring dedicated talent to collect, store, and curate data. Otherwise, it transcends into a mixed-bag info dump that’s next to useless.
• Data cleanup: Preparation of data (i.e., cleaning it) before overlaying AI/ML applications.
• Data security: Most crucially, employing governance, security, and compliance specialists, without which data-reliant initiatives fall apart.
• Data-led CX: Directing all the data functions to create an unsurpassed BCX and erase all corrosive touchpoints in the BCJ.

For example, the US FSI category includes giant enterprises like AMEX, Mastercard, Visa, Wells Fargo, Chase, Bank of America, and Citi (to mention only a few), with data pools unmatched by almost any other industry. Did you know that in 2019 (the latest published stats), the leading credit card companies (CCCs) accounted for close to forty billion transactions? And that number has probably grown significantly since then. Consequently, they have the data volume to put spending patterns, CC usage, the massive shift to “buy now pay later,” SMB micro-loans, payment channels using electronic wallets, and more under analytic spotlights, thus using it with unsurpassed accuracy.

So, where has data-driven banking made the most headway?

1. Better BCX

Years back, customers were content with one or two banking accounts, paper checks, and laborious record keeping of cash flow transactions. The changes since are mind-boggling where FSI data reflected a fast multiplication of bank accounts with many going into self-employed ventures and undeniable customer demands for the following:

• Instant money transfers
• Ability to log into bank accounts via computer and mobile devices.
• Water-tight ID and account balance security, notwithstanding the escalation in bank facility usage.
• A seamless and authoritative support line, either by online chat or direct calling, to help navigate FSI digital systems.
• Simple to read bank statements with no “nickel-and-diming” fees that traditionally generated massive income for banks when added up over millions of accounts.
• Kicking ridiculous credit card APRs that start at 15% and go as high as 29% to the curb. Indeed, they want shorter-term, more manageable, bite-size loans with zero interest rates. In fact, FSIs that missed the signal from their data sources are today behind the eight-ball on the galloping trend toward BNPL!
• Automated credit approval or granting loans without it (closely aligned with BNLP). How is the latter possible? ML can extract and scan a customer’s data at blinding speed, such as payment history, purchases, and debt, to generate an instantaneous individual risk profile. Thus, loan approval is a matter of clicking a button for a fast and less aggravating banking service. Moreover, there’s no longer waiting for weekends or bank holidays to pass before getting the go-ahead.

There’s a clear signal that customers still trust their banks more than other quasi-financial entities. As a result, they look to them to take the lead on the behavioral variations described above. Accordingly, the data signals that:

• Mainstream FSIs have a ready-made springboard to offer integrative services to customers, even if it means providing specific services as a loss leader.
• BCX will shift to being less debt-reliant, enjoying lower transaction costs, and exercising a higher financial responsibility.

2. Better Risk Management

The downside of any FSI business is risk management getting out of control. It’s all very well building robust BCX and streamlining BCJs, but if it all backfires with unexpected losses, the benefits will also implode. Consequently, FSIs must deploy their data to minimize calamities that impact the bottom line. Much of this is described above in the section on building data volume and cleansing it before relying on its insights. Indeed, one mustn’t underestimate the latter as an essential prerequisite to remedy risk in FSI strategies. Conversely, insufficient data with iffy or negative results erode confidence when translated into stuttering initiatives, bringing a data-driven BCX approach to a screeching halt. So, as a last word on risk, the more you know, the less risky decisions become, and data is the solution to making that a reality.

3. Better Capital Reconstruction

FSI’s massive headache is balancing its capital resources. More specifically, they find it challenging to identify where capital shortfalls enter the picture and the best alternative for deploying the capital at hand. You might respond, “So why is that such a big deal? Every business has to worry about resource allocation.”

Here’s the thing, when banks make capital commitments, it’s generally for multi-million dollars tied up for lengthy periods. By the time they’ve detected the move was an error, losses are in the bag, and transitioning out is slow, thus prolonging the agony.

However, when big data and ML combine enter the picture, they deliver two massive benefits:

• First, it has predictability capabilities – projecting what an investment in different verticals will look like down the line.
• Second, the bank will get early alert signals that can put the brakes on added allocations to a losing bet and signal a pull-back strategy before too much capital gets tied up.

Most businesses don’t have large enough databases to derive the above advantages nor the skills to create the insights. Thus, big data is the key to eliminating stakeholders’ frustration over ROI improvements that can show up quickly in the P&L. Beyond that, it creates never-before-experienced flexibility in reallocating funds to the most profitable options in the customer interface.

4. Better Fraud Detection

Fraud disruption and profitable financial servicing are two sides of the same coin. Margins in many aspects of FSI services are skinny and fragile. Thus, one audacious fraud can wipe out profits from multiple transactions. Therefore, fraud disruption has been a concern for bankers and FSI CEOs for years, but data accumulation can minimize that issue and cut the losses substantially.

Simply put, big data can help with fraud detection and prevention. This is done by feeding organized data to algorithms inside ML formats, the latter highlights the most likely fraudulent transaction areas and can simultaneously detect real-time frauds in the system. Of course, it may not be perfect, but there is no doubt that the big-data and ML two-pronged attack is a fast-acting, formidable force against the criminals that perpetrate FSI manipulations and fraudulent transactions. Doing so reduces overall FSI costs resulting in better value propositions for customers.

5. Better Customer Promotion

This ties in closely to BCX in (1) above, but from a different angle. Unless the message via social media, billboards, and cable resonates emotionally with banking clients and prospects, customer retention is unsustainable. Therefore, marketing campaigns must go to extra lengths, conveying that the bank prioritizes customer needs and bends over backward to fulfill them.

For example, big data showed Chase that contactless banking was a priority customer desire. Thus, the bank (and others) followed suit, shifting media campaigns to revolve around the concept of emails and full-spread debit card engagement mailers demonstrating contactless card versions!

Conclusion

Data-driven banking has recognized that guesswork is no longer a permissible luxury in a competitive environment, with disruptors constantly exploding on the scene. Customers hold significant power and jump ship quickly if the BCX doesn’t meet expectations. Today, FSI stakeholders must embrace big data – from collection to analytics.

That’s where Sogolytics comes in. With a robust solution that focuses on delivering exceptional customer experiences for financial institutions, we understand the power of data to deliver insights and identify drivers that would otherwise be missed. With the right insights, you can instead create a BCJ that supersedes competition.

Not sure where to start? Get in touch with our experts and discover just how Sogolytics can add value for you!