Banks served as an important source of support for their customers and communities during the first waves of the COVID-19 crisis. Now, however, institutions must see to their own profitability. The pandemic has exacerbated preexisting structural and market pressures and injected new steering complexity.
BCG’s sixth biennial Treasury Benchmarking Survey, conducted over the course of 2020, captured insights from 39 banks across Asia, Europe, and North America, including 13 global systemically important banks (G-SIBs). The findings reveal three core challenges:
These challenges are shaping the treasury agenda. Respondents globally cited three main aspirations for the next few years:
Our 2020 Treasury Benchmarking Survey revealed that insight gaps and manual-intensive processes still plague many banks, with treasuries globally struggling to gather needed information and perform computations at the speed required. (See Exhibit 1.) Two-thirds of respondents (and 90% of those at G-SIBs) said that they lack accurate data to support balance sheet management. And 42% of participants said that the data needed to complete their work is not available at all.
Even when treasury employees succeed in amassing the right data, respondents said that teams must then spend time reconciling inputs—often manually—since the calculations used by the risk, finance, and treasury functions often vary. Frustration is mounting, with one-third of respondents reporting that they cannot generate calculations fast enough.
Head count data shows that staffing levels vary widely among treasuries that are of a comparable size when measured by assets, suggesting that productivity also varies widely. Treasuries in the top quartile based on head count have nearly twice the number of full-time equivalents for every €100 billion in assets than do the leanest institutions in the bottom quartile (43 FTEs and 24 FTEs, respectively) with the median coming in at about 31 FTEs. (See Exhibit 2.)
To improve efficiency and performance, most respondents have ranked operating model improvements and data and IT modernization among their top agenda items. More than half (54%) indicated that having a cloud-based application environment is a key aspiration. Only 6% of treasury respondents work in organizations that have reached this level of modernization today. Participants from regional banks were also keen to improve their modeling and stress-testing abilities. To enable this, many hope to develop a microservices-based architecture that uses application programming interfaces (APIs) and employs a central data warehouse and data lakes.
Liquidity buffers have been growing in size even before the pandemic. Since 2019, they have grown by 3 percentage points and now represent roughly 20%, on average, of banks’ balance sheets (23% for G-SIBs). Liquidity coverage ratios (LCRs) have also grown, rising from 139% in fiscal year 2017 to 146% in fiscal year 2019, far above the regulatory minimum of 100%. And while large buffer sizes and high LCRs are often correlated, that was not true at several banks in 2020, suggesting some might be overly conservative in their financial resource management.
Addressing the size and composition of the balance sheet has taken on increased importance in light of declining NII. In recent years, central bank quantitative easing programs, weaker maturity transformation returns, and regulations—such as minimum requirement for own funds and eligible liabilities (MREL) and total loss absorbing capacity (TLAC)—have eroded NII performance, driving average treasury contributions down, from 12% in 2016 to about 9% in 2020.
To optimize the balance sheet, treasurers are placing heightened emphasis on financial resource management. Most have increased their risk appetite across the P&L. (See Exhibit 3.) In some cases, the shifts have been dramatic. Nearly three times the number of institutions elevated their risk appetite for interest rate transformation than lowered it, with many treasurers using that expanded appetite to increase the average maturity of modeled products. And nearly four times the number of treasurers raised their risk appetite in investment portfolio and capital investment than lowered it. Treasurers are increasingly looking at asset classes and securities that have longer horizons for returns and the potential for higher spreads.
The one area where the appetite for risk contracted was liquidity transformation, likely because of concerns about the depth and severity of the COVID-19 crisis when data for our survey was being gathered. Benchmark data shows that the average bank had a net stable funding ratio of 118% for fiscal year 2019 and that TLAC and MREL levels were also above the regulatory minimums by an average of 5 percentage points and 11 percentage points, respectively.
Looking ahead, treasurers will need to consider how to adjust their framework for steering financial resources in order to manage ESG. For example, 97% of respondents indicated that they expect ESG funding to increase. But while 56% said that their banks have launched an ESG issuance program, only one-quarter said that their institutions had set a minimum target for these bonds.
Although treasuries have been experimenting with new technologies at a slightly higher rate than they were in 2018, they are not transforming fast enough. (See Exhibit 4.) Less than half of the respondents said that their functions employ key aids, such as robotics or machine learning (ML), in a material way.
As a result, many forecasting methods remain rudimentary. Eight in ten respondents said that their organization relies on manual inputs from specialists and simple statistical methods, such as ordinary least squares regression. Looking ahead, treasurers are broadly agreed that their target state should enable the use of advanced statistical methods (such as autoregressive integrated moving average with exogenous input) and deploy ML capabilities more fully. (See Exhibit 5.)
The benefits of reaching that target state are significant. Employing ML-driven algorithms can help treasury teams reduce manual labor, optimize the use of collateral, automate buy-or-sell recommendations, and predict client behavior, such as prepayment rates.
Embracing agile working practices will be important to help treasury teams avail themselves of these capabilities. Creating interdisciplinary teams, breaking large projects into smaller sprints, and establishing rapid review-and-release cycles are established ways to speed digitization and adoption—especially in ML use-case development. But while 50% of treasury respondents believe agile ways of working deliver higher-quality outputs and cost savings, less than half say their treasury functions use these methods regularly in their projects.
Leaders that commit to adapting the treasury function will find their efforts well rewarded. On the basis of our client experience, acting decisively in the following areas can boost bank NII 2% to 5%, and in some cases by as much as 10%.
Increase operating model efficiency. The following steps can give treasurers the authority and insights they need to improve productivity and cost performance significantly:
Optimize financial resource management. To improve the treasury’s P&L contribution and balance sheet health, treasurers should increase board engagement in risk appetite discussions. Treasurers should also systematically review each balance sheet component to identify untapped potential and optimize each layer. (See Exhibit 6.)
Accelerate digitization. To pick up the pace of modernization, we suggest the following actions:
Treasuries have an opportunity to enter the postpandemic period on a much stronger footing. Although the interest rate environment remains challenging, a combination of strong bank liquidity, low market volatility, and a willingness to expand traditional risk tolerances create ideal conditions for treasury leaders to press for change. These changes are hard, but persistence will pay off by allowing treasurers to contribute demonstrably higher NII.
This article and the underlying BCG Treasury Benchmarking Survey would not have been possible without the cooperation of the participating banks. We are also grateful for the contributions of our BCG colleagues Michael Buser, Kirill Katsov, Anand Kumar, Chi Lai, and Tobias Strauch.
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