Commercial and corporate banking have been the standout growth stories in North American banking of late, with about 10 percent CAGR over the past three years (Exhibit 1). These years have also seen increasing variance between the leaders and the rest of the pack in revenue and growth. As we head into the “late cycle,” and with a clear slowdown in 2019, we expect this variance to increase as commercial-bank leaders focus on engines that will drive performance through the cycle.
Over the past several years, North American commercial banks have benefited from significant economic tailwinds. On the volume side, deposits and balances grew by approximately 5 percent CAGR as companies translated higher profits into cash holdings. Across loans and deposits, net-interest margins widened significantly, with 10 to 20 percent increases common across banks. As a result, growth has been broad based, with most North American commercial banks recording strong results in recent years.
However, though this growth has led banks outside commercial banking to consider increasing their presence in the sector, this interest has not been matched by performance. The past three years have seen increasing variance across the industry in revenue growth: some banks have posted 15 to 20 percent or more CAGR, while others are below 2 percent CAGR while maintaining their revenue/asset ratios (Exhibit 2). These variances are likely to grow as the economic cycle matures and, potentially, moves into a downturn, with interest margins compressing and deposit and loan volumes peaking.
Products that add value at “moments of truth,” rather than just higher prices and volumes on products led by relationship managers, will support margins in any economic environment. New, agile technology models allow commercial banks to launch digital portals and new services at a fraction of their cost ten years ago, creating cost flexibility. Granular data sets and advanced analytics will lead to double-digit improvements in share of wallet, pricing, and reduced churn—delivering growth while also increasing resiliency in the event of a downturn. Leading North American commercial banks are already starting to fire these engines.
Grounded in recent extensive work with leading commercial banks in North America and around the world, we outline below six engines we expect to fuel longer-term profitability through the cycle. The insights in this paper are based on proprietary commercial-banking-performance benchmarking, more than five million data points in McKinsey’s GCI Analytics from 32 of the world’s top banks, and 150 voice-of-customer interviews.
Engine 1: Providing specialized knowledge and coverage for industry verticals and ecosystems
Our research shows that commercial clients value a relationship manager (RM) who understands their industries and credit needs, has specific product knowledge, and can provide help beyond simple loans and transaction services. About 70 percent of commercial customers we surveyed consider these characteristics to be top priorities. In fact, for commercial businesses, specific industry expertise is far more important than attributes such as close ties to the local business community and industry networks.
It makes sense, then, that banks organized around industry verticals (such as healthcare, energy, technology, hotels, and private-equity sponsors) often achieve outsized growth through those coverage models. Their specialized RMs focus on narrow client lists and deliver distinctive knowledge, and these banks tailor the experience for the segment (for example, by offering the ability to decide on complex credits quickly). In the healthcare vertical, for example, many banks have subverticals offering services to specialist medical practices and skilled-care homes. Focused-coverage bankers cater to companies with complex cash-flow patterns, bespoke payment formats, healthcare enterprise-resource-planning (ERP) integration, and other specific needs.
Despite the clear advantages of the vertical-coverage model, it does have some limitations: specialized knowledge is harder to sustain, bespoke services are costly, and clients’ business models are becoming harder to classify in any one vertical. US medical practices, for example, now range from traditional office-based groups of fewer than five physicians to larger specialty groups with more than 5,000 physician affiliations. Diagnostics labs include some with traditional pay-per-test business models, as well as labs with at- or below-cost testing to build deep electronic-medical-record databases. Many health start-ups consider themselves technology companies first, preferring to be served by digitally enabled banks with connections to early-stage venture-capital funds and investor groups.
To be sustainable, industry-coverage models must extend to serve ecosystems, orchestrating connections across relationships and adding value at critical moments of truth for multiple stakeholders: for example, start-ups, VC funds, and large institutional investors in those funds form an ecosystem for financing early-stage technology companies. Banks that build a position in the middle of such an ecosystem can gain a sustainable competitive advantage.
Leading banks are also experimenting with flexible models to achieve ecosystem coverage. One model has remote RMs providing deep sector-specific coverage for smaller corporates, while local bankers or regular roadshows maintain in-person coverage where required. A second model has regionally based cross-sector RMs covering corporates locally, aided by digital support platforms that provide client- or ecosystem-specific knowledge on needs and banking solutions.
When commercial-banking executives ask us—as they do frequently—what we think a wholesale redesign would look like, we ask them to consider how they can serve clients more holistically and deliver value throughout the client life cycle and across business cycles. Could one coverage team build relationships with doctors at medical schools through student loans, finance these doctors’ first office practices, and then help negotiate their acquisition by a larger managed-care group? Could multiple coverage teams collaborate to serve a fast-growing digital healthcare-app company, blending healthcare, technology, and start-up expertise? Could companies be served without coverage at all, leveraging self-service tools and API libraries? The rewards for banks that grasp these opportunities to deliver holistic value will be significant. Focus groups often tell us that the banker who helps them through their first loan or first transaction will often become their “banker for life.”
Engine 2: Deepening share of wallet through fee-based products, such as treasury management and cross-border services
Fee income is the greatest differentiator between the commercial banks that generate returns significantly higher than the cost of capital and those with average returns. Controlling for product type and risk, loan margins are typically consistent among banks, reflecting close-to-perfect competition. High-performing commercial banks earn more than twice the average fee income per client, reflecting the value of treasury products in transaction banking and of other services, such as foreign exchange and cross-border services.
Commercial and regional banks often have limited treasury products, as a result of the high setup costs of traditional treasury banking systems and the need for scale in legacy payments rails and infrastructure. But this barrier to entry has fallen dramatically. We estimate that by using an “out of box” core banking platform and expanding functionality through robust microservices at the application-programming-interface layer and rented or built best-of-breed channel applications, a bank can launch a comprehensive set of treasury products with an initial investment of $20 million to $40 million. This approach has enabled some banks to target segment cost-to-income ratios of 45 to 60 percent in less than four years, solely through fee income—before the additional benefits from attracting deposits and increasing retention through expanded product usage.
There is also significant potential to raise fee income through services such as foreign exchange and cross-border services. Our client interviews consistently cite multiple pain points in this domain, including hard-to-use digital portals, high and opaque fee structures, unpredictable settlement times, and complex documentation requirements for compliance. Some regional banks have grabbed this opportunity, redesigning foreign-exchange journeys from end to end to generate more than $100 million in fee income, at an incremental cost-to-income ratio of less than 10 percent.
Engine 3: Embedding data and analytics in decision making
Compared with the sophisticated scoring and multimillion population sets common in consumer banking, commercial banking can sometimes resemble an artisanal craft: successful RMs and credit officers rely on intuition and relationships built over decades-long careers. While commercial-banking data sets will probably always be small relative to retail (as retail clients far outnumber midsize companies), commercial banks can now achieve step changes in performance by applying machine learning to a range of internal and third-party data.
The improvements can come in three ways. First, banks can generate practical models from large data sets, capturing a range of opportunities using advanced analytics. Examples include 10 to 15 percent incremental growth through next product to buy, 5 to 15 percent growth through granular microsegment pricing, and 15 percent growth through adaptive retention treatment and the lower risk costs resulting from improved early-warning signals. Second, banks can build intuitive tools (such as a workbench) to guide an RM’s interactions with individual clients. A workbench, for example, could flag cross-sell opportunities and automatically generate client-pitch documents and discussion guides, enabling RMs to spend all their time with clients. Third, banks can reimagine the overall sales process, integrating the latest in analytics and technology.
Our work with commercial banks has helped us identify six high-impact analytics use cases:
- deep, granular value-pool mapping of the commercial-banking market, targeting the verticals, geographies, and clients that coverage teams and prospecting should focus on
- structured wallet sizing to identify products and services with the highest take-up at target clients, followed by the development of a structured, regularly updated fact base for planning individual client accounts
- advanced analytics models based on the full range of internal and external data to determine the propensity to buy, so RMs can identify the next product to buy
- a pricing architecture tailored to microsegments for both individual products (as opposed to across-the-board increases) and multiple products in an overall relationship
- establishing indicators of potential churn behavior and designing cure treatment for microsegments to improve retention by up to one-third
- building early-warning indicators for credit risk (for instance, tracked payments and transactions, market-trading levels, and news) to improve overall risk costs by 8 to 12 percent, as well as focusing analysts’ time on higher-risk, complicated credits to improve overall decision making
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An example in treasury management illustrates how to get the benefits of advanced analytics. The typical bank has a straightforward but cumbersome repricing process, starting with an across-the-board price increase (such as 3 percent annually) and then negotiating bespoke discounts and waivers, based on qualitative input from RMs and coverage bankers. Results are mixed. At one commercial bank, more than 60 percent of price increases for treasury-management services resulted in revenue declines within one year as price-sensitive clients steered volume to other providers or moved their relationships altogether. Combining McKinsey’s GCI Analytics data with data from this bank helped it identify 45 percent more price-insensitive clients than existing models did, so it could be more targeted in its repricing. That led to fee-income growth of 12 percent year on year.
This experience is typical across the entire corporate relationship. We often see initial lending decisions based on elaborate client-profitability models and a complex “discount” architecture with multiple criteria. Just as often, those same reasons justify persistently low client profitability, with little or no feedback of data to improve the original decision logic.
Another example in credit early-warning indicators illustrates the through-cycle potential of analytics. The typical bank has a standard, regimented credit-review process, starting with annual spreading and quarterly reviews across the full portfolio. Significant time is spent on low-risk credit exposure. Risk indicators are reviewed when the credit review is due rather than when they are identified. At one commercial bank, the use of early-warning indicators to focus more quickly on the most relevant risks reduced risk costs by 8 to 12 percent and focused credit and management attention on emerging at-risk credits, increasing the bank’s resiliency in a potential downturn.
Engine 4: Reengineering and digitizing processes to meet clients’ expectations
Industry expertise and the ability to access credit continue to be crucial elements in serving commercial clients. Yet our recent survey of more than 150 companies across industries showed that quick service is as important as competitive credit pricing. Transparency, effortless processes, paperless interactions, and personal support throughout the process are emerging as the attributes that most influence perceptions of service quality (Exhibit 3). Many of these efforts are supported by at-scale investments on core replatforming to ensure that banks can stay ahead of clients’ expectations. As decision makers adapt to the omnipresence of digital solutions in their personal lives, they carry these expectations to their roles in their businesses. The ability to perform simple tasks through digital channels is already one of the main criteria that clients consider when adding a new banking relationship.
Two journeys stand out as major satisfaction and share-of-wallet determinants, largely because banks rarely meet the expectations of their clients. First, onboarding customers not only continues to be one of the most painful processes for them but also consumes both costs and organizational attention for banks (Exhibit 4). Our research suggests that RMs and their assistants spend up to 30 percent of their time dealing with onboarding-related administrative tasks. Banks can reengineer their processes from a “client back” perspective to minimize the effort required on both sides. Within six to nine months of developing a new end-to-end servicing process, changing the operating model, and making small but targeted technology investments, a leading US bank recently achieved double-digit improvements in the end-to-end time required.
The second banking pain point for commercial clients is the slow and cumbersome credit process. Many banks have taken notice, investing significantly to improve and digitize their credit process, with the goal of delivering six- to seven-day turnarounds for their clients and improving pull-through by double digits.
However, only a minority of banks have digitized the credit process successfully. Almost all have upgraded their loan-origination technology or are doing so. Many declare partial success after multiyear overruns and investments far exceeding those planned. One bank recently saw a cost overrun of $40 million and a three-year extension of the timeline with a technology vendor.
Successful efforts to digitize credit processes in banking usually share three attributes. First, these banks start by redesigning the process before investing in a digital solution. We find that within six months—before any technology interventions—an average bank can achieve a three-day credit-approval timeline for the lower end of middle-market credits (and a two-week approval timeline for core middle-market credits) by changing and clarifying roles and responsibilities, simplifying applications and credit-approval memos, and employing visual management. Second, in successful digital-credit efforts, use cases and solutions are sponsored by the business rather than designed by the technology group and pushed out to users. Third, successful efforts launch quickly for wide adoption. In our experience, a solution that is “live” to users within six to nine months and supported by an active adoption campaign has a chance of adoption that is an order of magnitude higher than a solution developed on a 12-month or longer timeline.
Engine 5: Developing and protecting a reliable source of funding
The typical commercial-client portfolio requires a reliable, low-cost source of deposits to fund its asset growth. For many banks with a great commercial sales force, access to competitive funding has become the constraint on growth. Fully unlocking it requires a deposit engine that can at least keep pace with asset growth.
Wholesale funding and brokered deposits help in the short term but create a cost disadvantage. During downturns, they pose a liquidity risk that needs to be counterweighted by broader balance-sheet strength. This simple idea isn’t lost on banks—an intense war for deposits is underway on both the commercial and retail sides: growing marketing expenditures, aggressive offers, and digital attacker banks (such as Marcus and Purepoint) are chipping away at deposits from established franchises.
Banks have several options for building and protecting a reliable source of funding in today’s market. One underutilized source of direct funding is commercial clients. The average loan-to-deposit ratio for a commercial portfolio can range from under 50 percent to almost 90 percent (with some franchises at over 100 percent). But this ratio is significantly influenced by the focus on industries, such as deposit-rich professional services, financial institutions, and nonprofits. Commercial banks could target their clients in these industries as sources of funding for their balance sheets. The funding profile of deposits differs significantly across industry verticals, but they offer an attractive profile overall, with behavioral maturity of more than three years in many cases.
Capturing share from such deposit-rich industries requires RMs dedicated to them, cash-management products that meet their payable and receivable needs, and an industry-leading client experience largely based on optimized onboarding and the seamless execution of transactions.
Engine 6: Investing in talent and performance management
The heart of commercial banking, and the prime differentiator among firms, is talent. Our research and work with leading banks show that this area has enormous potential for improvement.
In part because of the stability of commercial banking, the current sales cohort skews older than it does in most other areas of banking. This represents a significant reservoir of talent but is also a caution concerning the future talent pipeline. Commercial banking will continue to be delivered through people and built on relationships, and the sales tactics and practices of superior RMs will remain a differentiator. However, many banks still have rudimentary talent practices for commercial bankers: ad hoc recruiting pipelines, fragmented on-the-job training, and limited or no structured career-development paths. Improving these talent practices will be critical in coming years as senior commercial bankers decide whether to continue their careers and start to apprentice the next cohort of bankers. Given the typical starting point, even small interventions can have an outsized impact. In a recent example, a bank identified four new indicators of sales outperformance after a six-week effort to mine HR data, so its senior bankers could focus on the most promising juniors.
We have also noticed that RMs receive a lot of focus, while other roles that create significant value receive little of it. Work with a leading US regional bank showed that only one-third of the commercial-banking unit’s value going forward will be derived from traditional front-office roles—and the rest from “enabling” roles, such as those of the people who lead the digitization of client journeys to improve the customer experience and increase revenue, as well as the procurement staffers who optimize relationships with a bank’s myriad suppliers.
For banks in North America, the commercial sector is full of promise—and many banks have already fired up their engines. Those that accelerate growth may end up leaving others behind.
