Cash management benchmarks you should be hitting

Introduction

Getting cash management right is table stakes for companies of all sizes and industries. In this article, we’re going to explore key benchmarks Treasury teams should be striving for. We’ll do this by asking ourselves fundamental questions that cash management operations seek to answer. The answers to these questions may vary by company size and industry, so we will consider these nuances as well.

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Is your Treasury team hitting these benchmarks? Let’s find out.

Question: How quickly can we confirm cash balances?

Liquidity decisions are only as good as the data latency. If cash balances are confirmed manually, then borrowing, investing and funding decisions become reactive instead of strategic.

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From a risk mitigation perspective, delayed visibility leads to increased overdraft risk, risk of trapping cash and even missing opportunities for intercompany funding.

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Companies with near real-time visibility consistently demonstrate stronger governance, audit trails and fraud detection.

Key metrics

• Cash visibility (% global cash visible daily)
• Time to daily cash position (# hours to produce cash position)
• Cash position refresh frequency (# times per week)

What does good look like?

For the rest of this article, we’re going to assume that “large cap” companies are companies with >$5bn in annual revenue. We’ll assume that “mid-market” refers to companies with $500m-$5bn in annual revenue and <$500m for smaller companies.

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Large companies should expect to have > 90% of global cash visible on a daily basis. It should be closer to 100%, but acknowledging there are always exceptions for companies spread globally without a Treasury presence in every single country/region. The mid-market has a little more leeway to support flexibility, however should still have upwards of 75-80% of global cash visible every single day.

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Smaller companies also need to keep a close eye on their cash, but it is not always feasible to access and manage every bank account daily and they can lag larger companies when it comes to daily cash visibility.

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Daily cash preparation would ideally be prepared over morning coffee, leaving the rest of the day for more strategic initiatives. Large companies should expect to have their daily cash position highly automated and take a couple of hours or less to prepare. Mid-market companies should still be striving for 2 hours as well, however a range of 2-4 hours is more reasonable factoring in limited resources and less visibility into daily cash.

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Depending on the complexity of a small company’s banking footprint (which we’ll explore a little later), this could take an entire day, especially if the process is highly manual and without dedicated resources.

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Most companies should be striving toward a daily cash position refresh frequency. Smaller companies often don’t have that luxury and prepare on a less frequent cadence e.g. weekly or on an ad hoc basis.

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As we’ll see, the industry that these companies operate in have a material impact on their cash management operations.

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For capital intensive industries, such as manufacturing, telecom and energy, you can expect these metrics to slide (downward) due to the complexity of operations. For example multi-system reconciliations will slow down the ability to prepare a daily cash balance.

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When we think about less capital intensive industries, such as technology and professional services organizations, you should expect to see an upward trend in these metrics. Operations are simpler, by comparison, and a more centralized approach promotes visibility and speed. By leveraging API-driven integrations these companies are achieving close to real-time daily cash positioning.

Question: How reliably can we forecast cash?

Forecast accuracy determines whether the Treasury team is acting as a planner or firefighter. Poor forecasts lead to expensive short-term borrowing and idle cash buffers.

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The ability to allocate capital relies on the ability to forecast accurately. Key events such as M&A, debt repayment and share buy-backs all rely on confidence in the forecast.

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It provides insights into operational maturity across other areas of the business e.g. Accounts Receivable and Accounts Payable. You could argue that it is as much of a broader Finance KPI as it is a Treasury one in this sense.

Key metrics

• Forecast accuracy (30-90 day variance)
• Forecast frequency (# updates per week/month)
• Forecast inputs (% automated forecast inputs)

What does good look like?

Large companies should expect to have the vast majority of their forecast inputs automated (> 75%) and be preparing their forecast updates on a rolling weekly basis, if not daily. With this level of automation and frequent oversight, there should be a low tolerance for forecast variance. When considering a 30-90 day forecast, these larger companies should be managing variances within ± 3-5% i.e. ~3% for a 30-day forecast and ~5% for a 90-day forecast.

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Mid-size companies should be striving for the same margin for error, although forecast inputs may not be as highly automated and forecasts typically prepared on a weekly basis. With that, companies can expect to see a little more variance in their forecast, within ± 5-8%. High performing teams are forecasting within a 5% variance on their 30-day forecast.

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Companies preparing their forecast less frequently and with fewer automated inputs should expect higher levels of volatility in their forecast. Smaller companies will be more impacted by a customer payment that doesn’t clear when expected. This too leads to larger variances. With that, a more reasonable expectation for smaller companies is a variance of ± 8-15%. While that may seem high, it reflects the reality of a lack of automation in forecast inputs and infrequent preparation of the forecast.

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Manual forecast inputs are common for more capital intensive industries - there are a lot of moving parts. Volatile project spend is also a factor that reduces the precision of forecasts. 

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The story looks a little different for our technology and professional services companies. Forecast inputs are automated through tight ERP and TMS integrations, supporting continuous forecasting models vs manual preparation. With the generally predictable inflows, these companies should be striving for the ±3-5% variance range we discussed for large companies and high performing teams are able to consistently forecast within a 3% variance on their 30-day forecast.

Question: How much cash is sitting idle?

Idle cash can impact earnings. Even modest optimization of idle cash can generate a meaningful yield without increasing liquidity risk.

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Excess cash balances often mask inefficiencies built into cash operations e.g. fragmented accounts and weak cash pooling structures.

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Leadership is increasingly viewing idle cash as a return metric and not just a liquidity one, especially for companies holding onto significant cash balances. 

Key metrics

• Idle cash ratio (% idle cash vs total cash)
• Investment deployment lag (# days to invest surplus cash)
• Days cash on hand (average # days cash on hand)

What does good look like?

Large companies are keeping their idle cash balances down by taking advantage of things like advanced cash pooling mechanisms, automated cash sweeps and active investment programs. Large companies will often have less lag in their ability to invest surplus cash (<2 days). By leveraging these tools they should expect to be maintaining idle cash in the 5-10% range, with high performing teams achieving <5%.

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Mid-size companies are also taking advantage of the same tools, however some structural friction can lead to slippage of their idle cash ratio. It can also take a little longer to move surplus cash into investments (2-5 days). With that, these companies can expect a slightly higher range of 7-15%, with high performing teams achieving <7%.

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Smaller companies generally keep more liquidity on-hand to manage day-to-day business operations. They often have less cash to invest, in general and as we just saw, have less overall confidence in their cash forecast. This leads to broader ranges of idle cash balances. Not always a high priority for these smaller companies, high performing teams are achieving <10% idle cash.

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Our manufacturing, energy and telecom companies will often carry higher precautionary balances to support operations e.g. uneven capex timing. This leads to a slightly higher idle cash target of 6-12% compared to larger companies across a range of industries.

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Asset-light companies are managing faster cash cycles with fewer structural holds, enabling them to manage idle cash more tightly. Idle cash can even be synonymous with expected cash forecast variance for technology and professional services companies. With that, they should expect to achieve a lower level of idle cash, in the range of 3-7%. High performing teams are on the lower end of this scale.

Question: How much time does it take to manage cash operations?

As we have seen in other articles, manual effort does not scale well. If cash management processes depend on manual spreadsheets and email communication, growth will be met with additional headcount needs.

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The more time Treasury teams spend collecting data the less time they have to analyze it. High performing Treasury teams shift effort from reconciliation to focus on supporting the business more proactively.

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The more manual (and longer) cash management processes take to execute, the higher the dependency on key personnel within the Treasury team and risk of burnout - a recipe for disaster!

Key metrics

• Time spent on cash management operations (% of team capacity)
• Manual touchpoints per cash cycle (# manual touchpoints and systems to access)
• Manual data ingestion (# manual bank data pulls)

What does good look like?

Earlier we talked about managing daily cash operations over morning coffee and that should be the goal for many companies.

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Large companies are automating their bank data pulls and minimizing manual touchpoints in the process. With this upfront investment, their Treasury teams are spending <20% of their capacity on cash management operations, freeing them up to focus on more strategic, value-add initiatives.

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Mid-size companies are doing the same, although they will generally have some manual bank data ingestion (smaller, international banks primarily) and have manual touch points in their day-to-day operations such as manual release of cash, intercompany transfers and hedging activities. As such, their Treasury teams are spending closer to 20-35% of their capacity in managing day-to-day cash operations.

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This becomes the predominant task of Treasury teams at smaller companies, especially where bank data ingestions starts with manual login to a banking portal, then downloading a statement and where reconciliations are performed in spreadsheets. These Treasury teams are spending >40-50% of their time managing cash operations.

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Capital intensive companies need to layer in some buffer for operational complexity, especially where they still work with legacy banking processes and file formats.

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Asset-light companies are spending less time on day-to-day cash operations by taking advantage of direct bank connectivity, reconciliation automation and transaction processing automation, within predefined limits. These companies are pushing the boundaries of what is possible, allowing them to focus on strategic pursuits vs operational tasks. Maybe cash operations over morning coffee isn’t a pipedream after all!

Question: How costly is our banking footprint?

By not actively managing their banking footprint, companies will be overspending on fees, spending time managing redundant accounts and potentially increasing FX exposure risk.

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Strong banking relationships are important for high quality customer service, credit assistance and can lead to better pricing on banking products. Having a broader banking footprint dilutes some of that strength.

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A broader footprint compounds operating challenges when considering the need for additional bank data feeds, local banking compliance rules to adhere to and general control overhead in managing additional bank accounts across more jurisdictions.

Key metrics

• Bank fees (annual fees ($) per account)
• Bank accounts (# bank accounts per legal entity)
• Banking partners (# banking partners globally)

What does good look like?

For these specific metrics, the ranges are too broad to consider benchmarking, so instead we will explore the strategy employed by high performing teams.

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Large companies have their banking fees optimized by maintaining fewer, deeper banking relationships. They will optimize where they manage their banking activities within their banking network to maximize their pricing leverage. Fees are reduced by consolidating banking integrations/APIs, which is another benefit of working with a reduced network of banks.

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Mid-size companies will often have a banking structure that reflects their history vs their design. Banking relationships may stem from past acquisitions, legacy lenders and specific regional needs vs a unified strategy. With this approach it is a little more challenging to review bank fees on an ongoing basis resulting in one-off, event-driven fee reviews. Even with a robust structure design in mind it often requires significant effort to make the necessary changes, meaning the level of effort often outweighs the perceived savings. Companies need to take a long-term view to get this right.

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Smaller companies generally have less say in their banking relationships. They are driven by local presence, venture debt providers, etc. and costs are absorbed as overhead rather than evaluated. For smaller companies to move with speed they have a higher likelihood of redundant bank accounts and idle cash balances (as we saw earlier). Even so, companies with meaningful fee volume can still negotiate rates and they should be starting there.

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These approaches vary by industry, for example in manufacturing, energy and telecom companies, bank fees are generally tied to lending and project support. With this, there are often more banking relationships to be managed to ensure access to capital and manage risk. Reducing the number of banks/accounts is not as high of a priority and can be structurally necessary, as bigger projects often require multiple lenders (or a “syndicated facility”). 

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Technology and professional services companies typically have fewer structural constraints, making footprint optimization much more achievable. These companies can support centralized operating models e.g. shared-service structure to support global operations through fewer banks. They also leverage TMS platforms to reduce the friction of changing bank providers. High performing teams handle the majority of cash inflows and outflows with a small number of global banks, with strong pricing discipline.

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It is worth noting that regardless of company size and industry, when operating globally you will find that some countries require you to bank locally, as a requirement to pay local employees, collect customer payments, and remit taxes. Etc. This is one of the reasons we have some flexibility in the metrics our Treasury teams should be hitting!

Final Thoughts

Every company is different. The metrics discussed in this article are a guide toward how high performing teams are operating today. At the end of this article you will find a “Cash Management Benchmark Cheat Sheet” that summarizes these metrics. Here are a few considerations as you think about your own cash management options.

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Treasury teams that can confirm balances quickly and consistently make better funding, investment and risk decisions, while those without timely visibility are forced to hold onto excess buffers of cash and operate reactively. Focus on cash visibility first - speed to a reliable cash position is the foundation for everything else!

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Reliable forecasts reduce unnecessary liquidity, lower borrowing costs and enable Treasury teams to shift from daily reconciliation toward more strategic initiatives. Forecast accuracy is the best indicator of cash management maturity!

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Companies that automate their data flows, streamline bank connectivity and reduce manual touchpoints frees up Treasury team capacity and in turn, liquidity. This results in improved returns without increasing risk. Idle cash and manual effort are often systems of the same program - friction in cash management processes!

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Regardless of a company’s size or industry, Treasury teams should measure success by how efficiently cash moves from visibility to predictability, ensuring that every bank account, banking relationship and ultimately every dollar serves a purpose. The right benchmark isn’t about having few banks or less cash on hand, it’s about aligning the structure to meet your specific business needs!

Is your team ready for the next evolution of treasury operations? Ask yourself these 10 questions before choosing an AI treasury platform!