Why Aren’t Banks Getting More from Digital?

It is a truism in banking that digitalization has the potential to transform the business model, cut costs, and reduce time to market. This assumption is supported by evidence showing that retail banks that digitalize can achieve a 20% increase in revenues and a 30% decline in expenditures. And wholesale-banking digitalization programs can deliver a 12% reduction in cost-to-income ratios. (See “How Digitized Customer Journeys Can Help Banks Win Hearts, Minds, and Profits,” BCG article, June 2016, and “Digital in Corporate Banking Reaches the Tipping Point: Is Everyone Ready?” BCG article, November 2015.)

The numbers are impressive, but they represent the exception rather than the rule. Despite significant investment and effort, many banks struggle to make digitalization pay. To understand why, we surveyed digitalization programs at 12 international institutions. Our research revealed a range of organization structures, technology frameworks, and funding models that support digitalization, but in the majority of cases—at least one in three—implementation results are disappointing. The pace of delivery is slower than initially expected, it is difficult to scale digital solutions across the bank and end-to-end, or the impact on the bottom line is insufficient.

Of the three areas of concern, slow delivery is the problem most widely cited: almost two-thirds of the banks in our survey reported that delivery was not as fast as they’d anticipated, one-quarter said that scaling up is the primary issue, and 15% reported that they are most disappointed in the impact on the bottom line. (See Exhibit 1.)

Our research highlights four key causes of failure to deliver:

• Complex legacy IT that hinders progress
• Data architecture that is inadequate to support digital propositions and journeys
• Key talent gaps in, for example, the digital customer experience, data, and analytics teams 
• Organizational resistance to changes that threaten the status quo 
• In addition, many banks’ strategic approach to digitalization fails to consider potential pitfalls or how to address unexpected problems. Many projects are characterized by initial bursts of enthusiasm that are followed by a long tail of diminishing interest. The result is that their investment delivers limited returns, creating and catching banks in a “digital value trap.”

The Digital Value Trap

The failure of banks to execute their digital rollout in line with their ambitions suggests that they have fallen into a value trap: the upside fails to compensate for the level of their investment. There is also a problem of comprehension. Not even half of the respondents to BCG’s survey said that they have a clear understanding of the benefits of digitalization or that they are achieving those benefits. Respondents said that programs lack delivery speed, the scale expected, or bottom-line impact.

Banks with stretched project timelines cited such challenges as problems attracting appropriate talent to the right locations and the operational difficulties of implementing across multiple locations. Talent deficits are seen as a reason for scaling challenges too—for example, in relation to customer journeys. One bank reported that shareholder pressure for short-term performance militates against effective long-term investment. Another said that patchy resource prioritization had led to a failure to achieve certain financial targets.

In fact, few banks are able to execute digital transformation across all potential use cases, and in many cases, distribution channels take precedence over back-end capabilities such as systems and data architecture. For example, nearly 80% of the banks in our survey acknowledged that their data architecture is relatively immature. 

In assessing the reasons for the digital value trap problem, banks highlight a number of challenges. Exhibit 2 shows those challenges, which include the following:

• Legacy IT Systems. Banks cited existing IT systems as the number one reason for failure to achieve digital transformation. In many cases, this was a result of difficulties in enabling current systems to speak to new digital applications, as well as the faulty business logic embedded in legacy systems. One bank executive said that some 80% of the technology effort in the bank’s digital program was spent on integration, which meant it struggled to execute within the scheduled time frame.
• Organizational Resistance to Change. Inertia is a key reason for a digital program’s low return on investment. And many banks reported that they were unable to break down silos or had failed to identify the right governance structure for projects. Digitalization affects employee tasks and processes, and some employees are naturally concerned about the potential impact on their jobs. (See “Spotlight on Two Banks”.)

• Talent Gaps. The lack of technology expertise was cited as a significant threat to successful delivery because many banks fail to attract and retain tech-savvy employees, who may envisage more exciting opportunities in less formal environments or with startups. Furthermore, because banks are perceived as lacking vision, they may have trouble recruiting highly qualified employees who can help build capabilities over time. 
• Complex Data Architecture. Inefficient data architecture can significantly undermine the speed and progress of a digital rollout. Poor architecture slows systems and perpetuates operational and product silos. Strong data architecture, on the other hand, creates value by supporting data quality and increasing standardization and cross-asset capabilities. It also enables advanced analytics and a range of applications that can boost efficiency and employee effectiveness.

Three Digital Delivery Models

Across the industry, there is considerable variability in the ways that banks have approached the transformation process. Some have chosen to launch new digital solutions alongside existing interfaces, while others have built entirely new lines of business or companies. The following three delivery models stand out:

• Digital as BAU+, or Business-as-Usual Plus. Digitalization is driven by the existing business and management team, and the new approach makes incremental and bite-sized advances.
• Digital as a New Line of Business. The bank creates a new business unit, typically led by a head of digital, to deliver digital transformation with separate support functions, including parts of legacy IT systems. 
• The Digital Native Approach. The organization acquires or builds an entirely new digital bank with its own P&L and technology stack.

The three models can be seen as design blueprints, and many banks run all three approaches in some combination across markets, regions, and business lines. Some digital leaders have other layers to coordinate, for example, in different regions or relating to individual lines of business. Each model offers advantages and disadvantages that are related to the bank’s status, position, and needs.

BAU+

Using the BAU+ model, banks define and execute their digital strategy with the existing processes, organization, and governance, typically adding digitalization to an executive’s responsibilities and assigning new personnel to help. Funding is generally provided through the change budget for the P&L of the specific market or line of business. Existing reporting lines and P&L accountability remain unchanged.

Advantages. Such models launch quickly and offer early progress. Change can be driven through customer journeys, delivering cost savings and higher revenues. 

Disadvantages. In many cases, it is difficult to change the fundamental business model, which may be contained within a particular line of business. This makes it tough to run cross-asset programs and customer journeys, especially in a product-led organization whose P&L sits with the product owner.

Use Case. Large banks with significant digital war chests have adopted this approach. One bank earmarked $2 billion to push its digital agenda, which it developed through existing entities in each market. 

A New Line of Business

Using this model, the bank creates a new digital division, which, in many cases, is led by a head of digital who reports directly to the CEO and has authority in line with other business unit heads. In addition to running the new unit, the head of digital may be responsible for other digital offerings. In the retail context, the head of digital might be responsible for such nonbranch customer concerns as mobile, call center, and online channels. The digital division typically owns digital projects, but it relies on shared services in areas including IT, risk, and HR. The head of digital controls the budget and roadmap for the new business.

Advantages. This model allows for the radical reimagination of customer journeys and propositions. There is more accountability because the head of digital owns the budget and is responsible for delivery. Scale can be achieved by rolling out initiatives across the organization.

Disadvantages. The new line of business may add to organizational complexity and compete with existing businesses for, say, internal IT services. However, it does not resolve legacy IT and data issues. 

Use Case. One large European bank has created a separate business unit for its retail digital initiative. The head of the digital unit sits on the executive committee and has a veto over investments. The unit is organized into a series of labs, each responsible for specific programs, and each lab has a product owner who drives the customer experience roadmap, a delivery director who helps coordinate technology, and design teams who execute the roadmap. The labs enable rapid delivery to the end customer. 

The Digital Native Approach

The digital native approach calls for acquiring or building a digital bank with its own P&L for a given market or line of business. Unlike other approaches, the digital native design is made from scratch, avoiding the complexity associated with existing architecture, and the digital roadmap is heavily focused on acquiring new customers. The digital native is independent of the parent, meaning that they share no services, including IT. The digital targets sit with the CEO of the new digital native bank. 

Advantages. The digital native model allows for fundamentally different economics and capabilities, offers the potential for rapid impact, and obviates the need for legacy IT modernization. Using an approach that is pretty much off-the-shelf, a digital bank can get to market quickly and be highly competitive, because there’s no need to take the time to modernize or improve legacy systems.

Disadvantages. By definition, the model does not change the existing bank, so it’s rarely possible to execute as the only delivery model and difficult to migrate legacy customers and products to the new architecture. 

Use Case. A European retail bank successfully built a digital native bank, which was initially designed to deliver mobile and Internet-based services. The bank was powered by advanced analytics, which permitted it to provide highly tailored offerings, and it was built on top of a new core banking platform with the cooperation of a number of fintech providers. A key reason for its success was that the bank had planned—from the start—for how it would deal with the legacy bank and had developed a realistic five- to seven-year roadmap to manage legacy migration.

Responding to the Digital Challenge

Banks that fail to address the key challenges associated with digital rollout—clunky legacy systems, organizational resistance, talent gaps, and weak data architecture—are liable to find themselves caught in a digital value trap, in which the return on investment does not reflect the cost of transformation.  

To recalibrate the ROI equation, banks must take concerted action that is based on a view of digital as a solution to fundamental business problems and that also meets customer needs. As leaders consider strategies for effective implementation, they should consider several guiding principles that we believe may provide a foundation for progress:

• The customer must be the focus of all digital activity. Banks should prioritize investment on the basis of real business value generation—higher revenues and lower costs—and ROI. In too many cases, this pragmatic commercial lens is missing. 
• Small, empowered, colocated cross-functional teams should straddle organizational silos. As digital maturity evolves, the teams should evolve from project teams to permanent teams.
• Leaders must focus on identifying and resolving resource bottlenecks and conflicts as early as possible, using, for example, individual subject matter experts or system code release schedules on legacy applications.
• Banks should define an end-to-end target state architecture, and digital development must be anchored in the journey toward that target. 
  

Flowing from the general principles, we recommend five key strategic actions:

Identify the right delivery model. Each bank must determine which digital delivery model, or combination of models, is appropriate for its strategic purposes. Banks just embarking on the transformation journey or wishing to test and learn on a small scale are likely to see BAU+ as the preferred approach. BAU+ can energize teams in the early stages of digital transformation and help generate critical early wins and proofs of concept. 

For banks that have already made progress, the new line of business or digital native model is likely to be more appropriate. The new line of business model has the potential to transform the organization, but it requires adaptations of existing processes, policies, and governance—and, inevitably, cultural change. If that approach seems likely to present insurmountable challenges, then the digital native model will probably work better. With either of these models, transformation of the legacy business still needs to be tackled at some point. 

For some banks, a combination of delivery models will work best, with different iterations across locations and types of business.

Update legacy technology. Banks must employ one-speed—rather than two-speed—IT that is grounded in an agile methodology. Core banking replacement is not always (or even often) necessary for modernization. Instead, change leaders should focus on creating an application programming interface (API) architecture that enables legacy applications to communicate with digital applications. One caveat: don’t try to build everything at once; prioritize APIs on the basis of their business benefit and value. 

Foster buy-in across the organization. A key challenge may be a prisoner’s dilemma in which digitalization is the right answer for the bank but not for individual executives, who, for example, might lose headcount or influence. Solving this requires the creation of winning scenarios for those executives, perhaps by expanding their mandate in a new operating model. Bank executives must be aware of the potential for wider internal resistance and should communicate necessary changes early, openly, and transparently.

Nurture and acquire talent. Banks should plan for big shifts in the delivery model and hire digital talent early and with sufficient scale to make a fundamental difference to the proposition. Typically, a bank needs to expand its advanced analytics resources by a factor of ten. Executives must determine what needs to change for the bank to focus intensely on the customer, and they should foster a culture of creativity and entrepreneurialism that encourages talented employees to stay. For example, existing delivery processes, stage gates, and authorizations and sign-offs are unlikely to suit agile project delivery. Digital engineers should be authorized to access the latest open-source tool sets rather than having to use standardized internal versions.

Every bank’s strategy for hiring digital talent should be based on the delivery model it has chosen and the scale of talent ramp-up. With a BAU+ model, the optimal approach involves retraining current staff and adding infusions of external talent. Under the new line of business approach, the hiring strategy is similar—but on a much larger scale—and involves new physical spaces designed to foster innovation and digital delivery. The digital native model creates a blank canvas for hiring individuals and teams, depending on the desired scale and ramp-up speed. Talent retention is generally more successful under the digital native model, because the organizational construct is tailored and uninhibited by existing processes and policies. For example, ten years ago, a credit card provider, seeing how important data and analytics capabilities would become, started to build an analytics team. Over the past decade that team has grown twentyfold. The many banks that lag behind that company must ensure that their recruitment strategy complements a forward-looking digital vision.

Address weakness in the data architecture. Banks must understand the strengths and weaknesses of their current data architecture. For example, data lake projects may struggle to deliver because of a lack of use cases or insufficient planning for operationalization. Analytics, meanwhile, may be boosted with the processing power of the cloud. However, banks may be required to determine how existing processes and procedures inhibit the use of cloud environments. One European bank spent two years improving the quality of its data before it was able to launch a workable digital program.

In managing a digital transformation, bank executives must work to mobilize the entire organization around a strategy tailored to the commercial opportunities available. Ultimately, the banks that adapt their organization to the digital age and replace or integrate legacy IT systems and data repositories by digitalizing end-to-end will be the most likely to both avoid the digital value trap and cut costs and boost revenues.