Treasury System Selection

Treasury is full of improvement ideas. Better systems, cleaner data, automated processes, more control, more visibility.

The problem is not identifying what needs to change. The problem is getting people to actually change it.

Change management in treasury is about turning good ideas into real, working improvements without breaking the day-to-day operation. Which, considering treasury runs payments, liquidity, and risk, is a bit like renovating a house while still living in it.

Why Change in Treasury Is Hard

Treasury sits in the middle of multiple dependencies:

• Banks 
• ERP systems 
• Internal stakeholders 
• External providers 
• Regulations and controls 

Changing one part often impacts several others. That creates hesitation.

Add to that:

• Limited resources 
• Competing priorities 
• Fear of operational disruption 

And suddenly, even obvious improvements get delayed.

Not because they’re wrong. Because they’re inconvenient.

What Drives Change in Treasury

Change usually comes from a few triggers:

• System limitations or legacy setups 
• Growth and increasing complexity 
• Regulatory requirements 
• Cost pressure 
• Need for better visibility and control 
• Digital transformation initiatives 

Sometimes change is proactive. More often, it’s reactive. Something breaks, becomes inefficient, or too risky to ignore.

Typical Treasury Change Projects

You’ll see recurring themes:

• Implementing or upgrading a TMS 
• Centralising cash through pooling or in-house banking 
• Improving cash flow forecasting 
• Automating payments and bank connectivity 
• Standardising processes across entities 
• Enhancing controls and compliance frameworks 

All of these sound logical. None of them are trivial.

The Gap Between Idea and Execution

Most treasury teams know what “good” looks like.

The gap is execution.

Projects fail or stall because:

• Scope is unclear or too ambitious 
• Data is inconsistent or incomplete 
• Stakeholders are not aligned 
• Responsibilities are not defined 
• Change impact is underestimated 

And then there’s the classic:
“We’ll fix it in the next phase.”

There is always a next phase.

The Human Factor

This is where most change efforts quietly collapse.

People are used to:

• Existing processes 
• Known workarounds 
• Personal ways of doing things 

Even if those processes are inefficient, they are familiar.

Change introduces:

• New systems 
• New responsibilities 
• Temporary disruption 
• Learning curves 

Without proper communication and involvement, resistance builds. Not openly. Subtly.

And subtle resistance is the hardest to manage.

Communication and Buy-In

Successful change requires:

• Clear explanation of why change is needed 
• Practical benefits, not abstract improvements 
• Early involvement of key users 
• Visible support from leadership 

People don’t resist change. They resist unclear or imposed change.

Treasury needs to translate technical improvements into business impact:

• Less manual work 
• Fewer errors 
• Better visibility 
• Faster decision-making 

Make it real, or it won’t stick.

Balancing Change and Continuity

Treasury cannot pause operations.

Payments need to go out
Cash needs to be monitored
Risks need to be managed

So change has to be phased:

• Parallel runs 
• Controlled rollouts 
• Testing and validation 
• Fallback options 

Rushing change increases risk. Moving too slowly reduces impact.

Finding the balance is part of the job.

Technology Is Not the Solution

This is where expectations often go wrong.

Buying a new system does not solve:

• Poor data quality 
• Unclear processes 
• Lack of ownership 

Technology enables improvement. It doesn’t create it.

Without process clarity and discipline, new systems simply replicate old problems in a more expensive environment.

Where It Goes Wrong

Some familiar patterns:

• Overestimating what technology will fix 
• Underestimating data and integration complexity 
• Lack of stakeholder engagement 
• No clear ownership of the project 
• Trying to change everything at once 

Most failed projects don’t fail technically. They fail organisationally.

Treasury’s Role in Change

Treasury often leads or heavily contributes to change initiatives.

It brings:

• Process understanding 
• Awareness of risks and dependencies 
• Practical constraints 
• Focus on control and efficiency 

A strong treasury function doesn’t just identify improvements. It ensures they are implemented in a way that actually works.

Because in treasury, a “partially working solution” is just another problem waiting to happen.

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Treasury doesn’t operate in a single system. It sits in the middle of a network of systems, each with its own logic, data structure, and occasional refusal to cooperate.

ERP systems hold transactions
Banks hold cash
TMS manages liquidity and risk
Reporting tools try to make sense of it all

Integration is what connects these pieces into something usable.

Without it, treasury becomes a manual data-processing function. With it, treasury can actually focus on managing cash and risk instead of chasing numbers.

What Integration Actually Means

Integration is about ensuring that data flows automatically, consistently, and accurately between systems.

Typical integrations include:

• ERP → TMS (transactions, forecasts, accounting data) 
• Banks → TMS (balances, statements, payments) 
• TMS → ERP (accounting entries, confirmations) 
• TMS → reporting tools (analytics and dashboards) 

The goal is simple:

• Enter data once 
• Use it everywhere 

The reality is slightly more complex.

Why Integration Matters

Without integration:

• Data is manually extracted and uploaded 
• Errors increase 
• Timelines slow down 
• Multiple versions of the truth appear 

With integration:

• Data is consistent 
• Processes are faster 
• Visibility improves 
• Decision-making becomes more reliable 

In other words, integration reduces friction. And treasury has enough of that already.

Types of Integration

There are different ways to connect systems:

• File-based integration
  Using standard files (e.g. CSV, XML) transferred between systems
  Simple, widely used, but not real-time 
• Host-to-host connections
  Direct connections between systems and banks
  More automated, but requires setup and maintenance 
• SWIFT connectivity
  Standardised messaging for bank communication
  Reliable and secure, but comes with cost and complexity 
• API integration
  Real-time data exchange
  Flexible and increasingly popular, but dependent on bank and system capabilities 

Most companies use a mix. Because consistency across providers would be too easy.

Data Standardisation

Integration only works if data is structured consistently.

This includes:

• Standard formats (e.g. ISO20022) 
• Consistent naming conventions 
• Aligned data fields across systems 

Without standardisation:

• Data mapping becomes complex 
• Errors increase 
• Maintenance becomes ongoing work 

Standardisation is not exciting. It is essential.

The Challenge of Data Mapping

Different systems speak different “languages.”

Integration requires:

• Mapping fields between systems 
• Defining how data is translated 
• Handling exceptions and edge cases 

For example:

• One system may define a transaction differently than another 
• Currency formats may vary 
• Timing of updates may not align 

This is where most integration projects become more complicated than expected.

Real-Time vs Batch Processing

Not all data needs to be real-time.

• Real-time (API-based)
  Useful for payments, balances, and time-sensitive decisions 
• Batch processing
  Suitable for daily reporting, forecasting inputs, and reconciliation 

Treasury needs to decide:

• Where real-time adds value 
• Where batch processing is sufficient 

Chasing real-time everywhere often increases complexity without proportional benefit.

Maintenance and Ownership

Integration is not a one-time project.

It requires:

• Ongoing monitoring 
• Updates when systems change 
• Handling of errors and exceptions 

Without clear ownership:

• Issues go unnoticed 
• Data becomes unreliable 
• Trust in systems decreases 

Which leads people back to manual processes. Again.

Where It Goes Wrong

Some familiar issues:

• Underestimating integration complexity 
• Poor data quality undermining connections 
• Lack of standardisation 
• No clear ownership of integration maintenance 
• Overcomplicated architecture 

Integration doesn’t fail because it’s impossible. It fails because it’s treated as a one-off task instead of an ongoing capability.

Treasury’s Role

Treasury defines:

• What data is needed 
• How frequently it should be updated 
• How systems should interact 

It ensures:

• Data supports decision-making 
• Processes remain efficient 
• Integration delivers practical value 

Because in treasury, having data is not enough.

It needs to be connected, consistent, and usable.

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Treasury runs on data. Not opinions, not assumptions, not “it should be fine.” Actual data.

Cash balances, exposures, forecasts, payments, positions. Every decision treasury makes depends on having the right data at the right time.

The problem is not a lack of data. It’s having too much of it, in too many places, with just enough inconsistency to make everything slightly unreliable.

Why Data Matters in Treasury

Treasury decisions are time-sensitive and financially impactful.

Without reliable data:

• Cash positions are unclear 
• Risks are miscalculated 
• Forecasts are inaccurate 
• Decisions are delayed or wrong 

With reliable data:

• Visibility improves 
• Control increases 
• Decisions are faster and more confident 

It’s not complicated. It’s just difficult to get right.

Types of Treasury Data

Treasury works with several key data sets:

• Bank data
  Balances, transactions, intraday movements 
• ERP data
  Payables, receivables, accounting entries 
• Forecast data
  Expected inflows and outflows 
• Market data
  FX rates, interest rates, pricing information 
• Master data
  Bank accounts, counterparties, payment details 

Each has its own source, structure, and timing. Bringing them together is where the challenge begins.

Data Quality: The Real Issue

Data quality is the foundation.

Good data is:

• Accurate 
• Complete 
• Timely 
• Consistent 

Poor data is:

• Incomplete 
• Duplicated 
• Outdated 
• Inconsistent across systems 

And poor data leads to:

• Incorrect reporting 
• Misleading forecasts 
• Loss of trust in systems 

Once trust is lost, people stop using the system and go back to manual workarounds.

Which defeats the entire purpose of having systems in the first place.

Reporting: Turning Data into Insight

Data on its own is not useful. It needs to be translated into insight.

Treasury reporting includes:

• Cash position reports 
• Liquidity forecasts 
• Exposure and risk reports 
• Working capital metrics 
• Investment and debt positions 

Good reporting:

• Is clear and consistent 
• Focuses on what matters 
• Supports decision-making 

Bad reporting:

• Overloads with information 
• Lacks clarity 
• Creates confusion 

There is a fine line between “comprehensive” and “unusable.” Many reports cross it.

Dashboards and Visualisation

Modern treasury increasingly uses dashboards.

These provide:

• Real-time or near real-time insights 
• Visual representation of key metrics 
• Easy access for stakeholders 

Dashboards can improve:

• Speed of decision-making 
• Accessibility of information 

But only if:

• The underlying data is reliable 
• The metrics are clearly defined 

Otherwise, you just get better-looking confusion.

Single Source of Truth

One of the main goals in treasury data management is creating a single source of truth.

This means:

• One consistent version of key data 
• Aligned definitions across systems 
• Reduced duplication 

Without it:

• Different reports show different numbers 
• Time is spent reconciling instead of analysing 
• Confidence in outputs decreases 

Achieving a single source of truth is harder than it sounds. It requires alignment across systems and teams.

Data Governance and Ownership

Data needs ownership.

This includes:

• Who maintains master data 
• Who validates inputs 
• Who ensures data quality 

Without clear ownership:

• Errors persist 
• Data becomes unreliable 
• Responsibility is unclear 

“Shared ownership” often leads to no ownership.

Frequency and Timeliness

Not all data needs to be real-time, but it does need to be timely.

Treasury decides:

• Which data needs real-time updates 
• Which can be daily or periodic 

Delays in data:

• Reduce relevance 
• Impact decision-making 

Too much real-time data without structure can also overwhelm.

Balance matters.

Where It Goes Wrong

Some familiar issues:

• Poor data quality across systems 
• Multiple versions of the truth 
• Overcomplicated reporting 
• Lack of ownership 
• Misaligned definitions 

These are not technology problems. They are organisational and process issues.

Treasury’s Role

Treasury defines:

• What data is needed 
• How it should be structured 
• How it is used in decision-making 

It ensures:

• Data supports operations and strategy 
• Reporting is meaningful and actionable 
• Systems are trusted 

Because in treasury, decisions are only as good as the data behind them.

And if the data is wrong, everything built on top of it is just confidently incorrect.

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Cash flow forecasting is the process of estimating how much cash will come in and go out of the business over a given period.

That sounds simple. It isn’t.

Because forecasting depends on assumptions. And assumptions depend on people. And people are… let’s say, optimistic.

Treasury’s job is to take all those assumptions and turn them into something that resembles reality.

Why Cash Flow Forecasting Matters

Cash flow forecasting allows companies to:

• Anticipate liquidity shortages or surpluses 
• Plan funding or investment decisions 
• Support strategic initiatives 
• Avoid last-minute surprises 

Without a forecast, treasury is reactive. With a forecast, it can act ahead of time.

The difference is usually measured in cost, stress, and how often someone says “we didn’t see this coming.”

Different Forecast Horizons

Not all forecasts are the same.

• Short-term (daily to weekly)
  Focus on cash positioning and immediate liquidity needs 
• Medium-term (monthly to quarterly)
  Support planning, funding, and working capital management 
• Long-term (annual and beyond)
  Linked to strategic planning and capital structure decisions 

Each serves a different purpose and requires a different level of detail.

Short-term forecasts need accuracy.
Long-term forecasts need direction.

Confusing the two is a common mistake.

Sources of Forecast Data

Forecasts are built from multiple inputs:

• Sales projections 
• Accounts receivable and payable data 
• Payroll and operational expenses 
• Capex plans 
• Tax payments 
• Financing activities 

Treasury consolidates these inputs into a single view.

The challenge is not collecting data. It’s ensuring that data is:

• Complete 
• Consistent 
• Timely 

Which is where things usually start to fall apart.

The Reality of Forecast Accuracy

Everyone wants a “highly accurate” forecast.

Reality check: perfect accuracy doesn’t exist.

Forecasting is influenced by:

• Changing business conditions 
• Delays in payments 
• Unexpected expenses 
• Human assumptions 

Instead of chasing perfection, treasury focuses on:

• Improving accuracy over time 
• Understanding variances 
• Building confidence in the forecast 

A forecast that is directionally correct and consistently improved is far more valuable than one that looks precise but isn’t trusted.

Direct vs Indirect Forecasting

There are two main approaches:

• Direct forecasting
  Based on known cash flows, such as invoices and payments 
• Indirect forecasting
  Derived from P&L and balance sheet projections, typically through FP&A 

Direct forecasting is more accurate in the short term.
Indirect forecasting is useful for longer-term planning.

Most companies use a combination of both.

Rolling Forecasts

Static forecasts quickly become outdated.

Rolling forecasts are continuously updated, typically:

• Weekly for short-term views 
• Monthly for longer horizons 

This keeps the forecast relevant and allows treasury to adapt to changes.

It also creates more work. But useful work.

The Role of Technology

Forecasting can be supported by:

• ERP systems 
• TMS platforms 
• Data aggregation tools 
• Increasingly, AI and machine learning 

Technology helps:

• Consolidate data 
• Identify patterns 
• Reduce manual effort 

But it does not fix:

• Poor input data 
• Lack of ownership 
• Weak processes 

If the inputs are unreliable, the output will be too. Just faster.

Ownership and Accountability

One of the biggest challenges in forecasting is ownership.

Who is responsible for:

• Providing inputs 
• Validating assumptions 
• Updating data 

Without clear ownership:

• Inputs arrive late or incomplete 
• Forecasts lose credibility 
• Treasury spends more time chasing data than analysing it 

Clear roles and accountability improve both efficiency and accuracy.

Variance Analysis

Forecasting is not just about predicting. It’s about learning.

Treasury compares:

• Forecast vs actual 
• Identifies deviations 
• Understands root causes 

This feedback loop improves future forecasts and highlights structural issues.

Without it, forecasting becomes a repetitive exercise with limited value.

Where It Goes Wrong

Some familiar issues:

• Overly optimistic assumptions 
• Lack of input from key stakeholders 
• Fragmented data sources 
• No regular updates 
• No analysis of variances 

The result is a forecast that exists, but isn’t trusted.

Which defeats the purpose entirely.

Treasury’s Role in Forecasting

Treasury brings structure, discipline, and realism to forecasting.

It ensures:

• Cash flows are understood and projected 
• Liquidity risks are identified early 
• Decisions are based on forward-looking insight 

It doesn’t predict the future. It reduces uncertainty around it.

And in treasury, that’s about as close as you get to control.

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KYC, AML, and sanctions compliance form a critical part of treasury’s interaction with banks and financial institutions.

They are not optional, not occasional, and definitely not quick.

These frameworks exist to prevent financial crime, ensure transparency, and protect the integrity of the financial system. For treasury, they translate into ongoing obligations that affect daily operations.

What KYC, AML, and Sanctions Mean

• KYC (Know Your Customer)
  Banks need to understand who they are dealing with. This includes ownership structures, business activities, and key stakeholders. 
• AML (Anti-Money Laundering)
  Ensures that financial systems are not used to move illicit funds. 
• Sanctions compliance
  Prevents transactions with restricted countries, entities, or individuals. 

Together, they create a framework of checks that companies must comply with when working with financial institutions.

Why This Matters for Treasury

Treasury sits at the centre of:

• Bank account management 
• Payments and collections 
• Counterparty interactions 

Which means it is directly impacted by KYC, AML, and sanctions requirements.

Without proper compliance:

• Bank accounts cannot be opened or maintained 
• Payments may be delayed or blocked 
• Relationships with banks can deteriorate 

In extreme cases, access to banking services can be restricted.

KYC: The Ongoing Process

KYC is not a one-time onboarding exercise.

Banks require:

• Corporate structure documentation 
• Ownership details (often up to ultimate beneficial owners) 
• Identification documents 
• Business activity descriptions 

And they require updates:

• Periodically 
• When company structures change 
• When new entities are added 

Treasury often manages this process, coordinating with legal and compliance teams.

It’s time-consuming, repetitive, and unavoidable.

AML Controls and Monitoring

AML frameworks focus on detecting suspicious activity.

Banks monitor:

• Transaction patterns 
• Unusual payment flows 
• Counterparty behaviour 

Treasury needs to ensure:

• Transactions are consistent with business activity 
• Documentation supports payments 
• Processes are transparent 

Unexpected or unclear transactions can trigger:

• Payment delays 
• Requests for additional information 
• Increased scrutiny 

Which slows down operations.

Sanctions Screening

Sanctions compliance involves checking:

• Payment beneficiaries 
• Counterparties 
• Countries involved in transactions 

Against official sanctions lists.

This is often automated by banks and systems, but treasury still needs to:

• Ensure accurate data 
• Validate counterparties 
• Manage exceptions 

A flagged transaction can:

• Be delayed 
• Be rejected 
• Require manual review 

Timing becomes unpredictable when sanctions checks are triggered.

Impact on Payments and Operations

KYC, AML, and sanctions directly impact:

• Payment execution times 
• Onboarding of new suppliers or customers 
• Opening new bank accounts 
• Expanding into new markets 

What looks like a simple operational step can become a multi-week process due to compliance checks.

This is where treasury needs to plan ahead.

Data and Documentation

Compliance relies heavily on documentation.

Treasury needs to maintain:

• Up-to-date corporate records 
• Ownership structures 
• Counterparty information 
• Supporting documents for transactions 

Incomplete or outdated data leads to:

• Delays 
• Repeated requests 
• Increased friction with banks 

Where It Goes Wrong

Some common issues:

• Underestimating the time required for KYC processes 
• Incomplete or inconsistent documentation 
• Poor coordination between departments 
• Lack of central ownership 
• Treating compliance as a one-off task 

These issues create delays and frustration. Usually at the worst possible moment.

Treasury’s Role

Treasury acts as the coordinator.

It ensures:

• Required documentation is available and maintained 
• Banks receive timely and accurate information 
• Transactions comply with AML and sanctions requirements 

It works closely with:

• Legal 
• Compliance 
• Operations 

Because while KYC, AML, and sanctions may not add visible value, they enable everything else to function.

Without them, treasury doesn’t have access to the financial system.

Which makes the rest of the job somewhat difficult.

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Treasury automation is transforming how treasury teams operate. Less manual work, fewer errors, more visibility. In theory, it sounds like a dream. In practice, it mostly means replacing spreadsheets with systems and then figuring out why the data still doesn’t match.

At its core, automation is about removing repetitive tasks so treasury can focus on actual decision-making instead of copying numbers between files.

What is Treasury Automation?

Treasury automation is the use of technology such as:

• Treasury Management Systems (TMS) 
• Robotic Process Automation (RPA) 
• Artificial Intelligence (AI) 
• Data and analytics tools 

To streamline treasury processes.

It reduces manual intervention, improves accuracy, and allows treasury to focus on liquidity, risk, and strategy instead of operations.

Why Automate Treasury Processes?

Manual treasury setups tend to be:

• Slow 
• Error-prone 
• Dependent on individuals 

Automation improves this by:

• Increasing efficiency through streamlined workflows 
• Improving accuracy by reducing manual input 
• Providing real-time visibility into cash and risk 
• Reducing operational cost 
• Supporting better risk management 

In short, less firefighting, more control.

Key Areas of Treasury Automation

Automation typically focuses on:

Cash Forecasting and Liquidity Management

• Automated forecasts based on historical and real-time data 
• Improved visibility into cash positions 

Payment Processing

• Straight-through processing (STP) 
• Reduced manual approvals and intervention 
• Built-in fraud controls 

FX and Interest Rate Risk Management

• Automated exposure tracking 
• Hedging support and execution tools 
• Real-time monitoring dashboards 

Bank Account Management

• Centralised bank connectivity 
• Automated reconciliations 
• Identification of redundant accounts 

Regulatory Compliance and Reporting

• Automated reporting 
• Audit trails 
• Reduced manual compliance effort 

Implementation Best Practices

Automation is not just about tools.

To make it work:

• Define clear objectives before starting 
• Focus on high-impact processes first 
• Involve stakeholders early 
• Train users properly 
• Continuously monitor and improve 

Automating chaos doesn’t create efficiency. It just creates faster chaos.

The Role of AI

AI is increasingly used for:

• Forecasting improvements 
• Pattern recognition 
• Fraud detection 

It adds value, but only if data quality is strong.

Otherwise, it just produces more confident mistakes.

Conclusion

Treasury automation improves efficiency, accuracy, and control. It allows treasury to move from operational execution to strategic contribution.

But it only works if processes and data are in order first.

Otherwise, you’re just upgrading your problems.

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Modern treasury doesn’t run on spreadsheets alone anymore. It runs on systems, integrations, data flows, and a growing pile of tools that all claim to make life easier.

Sometimes they do. Sometimes they just make the same problems faster and more expensive.

Technology in treasury is not about having the latest tools. It’s about creating a setup where data is reliable, processes are efficient, and decisions can be made with confidence.

Everything else is noise.

Why Technology Matters in Treasury

Treasury operates in a complex environment:

• Multiple bank accounts and entities 
• Different currencies and jurisdictions 
• High transaction volumes 
• Increasing regulatory requirements 

Trying to manage this manually doesn’t scale.

Technology enables:

• Automation of repetitive tasks 
• Real-time or near real-time visibility 
• Integration between systems and banks 
• Better control and auditability 

In short, it allows treasury to move from reactive to proactive.

The Core Treasury Technology Stack

A typical treasury setup includes:

• ERP systems
  The source of financial transactions and accounting data 
• Treasury Management System (TMS)
  The central platform for cash, risk, and payments management 
• Bank connectivity solutions
  SWIFT, APIs, host-to-host connections 
• Data and reporting tools
  Dashboards, analytics platforms, forecasting tools 

Each component plays a role. The challenge is making them work together.

Because a great system in isolation doesn’t create value. Integration does.

Data: The Real Foundation

Everyone talks about systems. The real issue is data.

Treasury relies on:

• Bank data 
• ERP data 
• Forecast inputs 
• Market data 

If this data is:

• Incomplete 
• Inconsistent 
• Delayed 

Then even the best technology won’t help.

Clean, structured, and reliable data is what makes technology useful. Without it, you just get faster confusion.

Automation: The Real Efficiency Driver

Automation is one of the biggest benefits of treasury technology.

It can reduce:

• Manual data entry 
• Reconciliation effort 
• Payment processing time 
• Reporting delays 

Common areas for automation:

• Bank statement processing 
• Payment execution 
• Cash positioning 
• Reconciliation 

The result:

• Fewer errors 
• Faster processes 
• More time for analysis 

At least, that’s the goal. Provided the automation is set up correctly.

Integration: Where Projects Get Interesting

Systems need to talk to each other.

ERP ↔ TMS
TMS ↔ Banks
Data tools ↔ Everything

This requires:

• Data mapping 
• Standardisation 
• Ongoing maintenance 

Integration is often the most complex part of any treasury tech project.

It’s also the part that determines whether the setup actually works.

Digital Transformation in Treasury

Digital transformation is a popular term. In practice, it means:

• Moving away from manual processes 
• Standardising workflows 
• Increasing data availability 
• Improving decision-making speed 

It’s less about “innovation” and more about fixing inefficiencies.

The real transformation happens when:

• Processes are redesigned 
• Data is structured 
• People actually use the tools 

Without that, transformation remains a PowerPoint concept.

AI and Advanced Analytics

AI is the latest addition to the treasury conversation.

Use cases include:

• Cash flow forecasting improvements 
• Pattern recognition in payments 
• Fraud detection 
• Data cleansing and classification 

It has potential. But it depends heavily on data quality and process maturity.

AI on top of poor data just gives you more sophisticated mistakes.

The Build vs Buy Question

Treasury often faces a choice:

• Buy standard solutions 
• Build custom tools 
• Combine both 

Buying is faster and less resource-intensive.
Building offers flexibility but requires maintenance.

Most companies end up with a mix.

And then spend time managing the complexity that comes with it.

Where It Goes Wrong

Some familiar issues:

• Investing in tools without fixing underlying processes 
• Poor data quality undermining system value 
• Overcomplicated system landscapes 
• Lack of user adoption 
• Underestimating integration complexity 

Technology rarely fails on its own. It fails because expectations and execution don’t match.

Treasury’s Role in Technology

Treasury defines the requirements.

It ensures:

• Systems support actual processes 
• Data is usable and reliable 
• Automation adds real value 
• Technology aligns with business needs 

IT supports. Vendors provide. Treasury owns the outcome.

Because at the end of the day, if the numbers don’t make sense, no one is calling the software vendor first.

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Once risks are identified, treasury has to decide what to actually do about them. That’s where hedging comes in.

Hedging is the use of financial instruments or structures to reduce or stabilise the impact of market movements. It doesn’t eliminate risk. It changes how and when that risk shows up.

The objective is not to “win” against the market. It’s to create predictability in cash flows and financial results.

Which sounds reasonable, until someone compares the hedge result to what would have happened without it.

Why Companies Hedge

Companies hedge for a few key reasons:

• To protect margins from FX or interest rate movements 
• To stabilise cash flows and improve planning 
• To reduce earnings volatility 
• To align with internal risk appetite and policies 

In short, hedging reduces uncertainty. It trades potential upside for reduced downside.

That trade-off is where most debates start.

Types of Hedging Approaches

There is no single hedging strategy. Companies typically choose between:

• No hedging (natural exposure)
  Accepting market movements and absorbing the impact 
• Natural hedging
  Structuring operations so inflows and outflows offset each other, for example matching revenue and costs in the same currency 
• Financial hedging
  Using derivatives or financial instruments to manage exposure 

Most companies use a mix, depending on the type and size of exposure.

Common Hedging Instruments

Treasury has a toolbox of instruments. The most common ones include:

• Forwards
  Lock in an exchange rate or interest rate for a future transaction
  Simple, predictable, widely used 
• Options
  Provide protection against adverse movements while keeping upside potential
  More flexible, but come with a premium 
• Swaps
  Exchange cash flows, often used for interest rate or currency exposures
  Useful for longer-term structures 
• Money market hedges
  Using borrowing and investing to synthetically lock in rates 

Each instrument has different implications in terms of cost, flexibility, and accounting treatment.

Hedging Strategy: How Much and When

The real challenge is not the instrument. It’s the strategy.

Treasury needs to decide:

• What percentage of exposure to hedge 
• Over what time horizon 
• At what frequency (layering hedges over time or all at once) 

For example:

• Hedge 100% immediately 
• Hedge gradually over time 
• Hedge only a portion and leave the rest open 

There is no universally correct answer. It depends on:

• Risk appetite 
• Predictability of exposures 
• Market conditions 
• Business priorities 

And, inevitably, hindsight.

The Cost of Hedging

Hedging is not free.

Costs include:

• Bid-ask spreads 
• Option premiums 
• Credit charges from banks 
• Operational and administrative effort 

Treasury constantly evaluates whether the cost of hedging is justified by the reduction in risk.

Sometimes the answer is yes. Sometimes it’s not. Sometimes it only becomes clear afterwards.

Hedge Accounting: The Technical Layer

This is where things get less exciting and more restrictive.

Hedge accounting determines how hedging results are reflected in financial statements. Without it, hedges can introduce volatility rather than reduce it.

To qualify, companies need:

• Clear documentation 
• Demonstrated effectiveness 
• Consistent application 

Failing hedge accounting doesn’t mean the hedge is wrong. It just means the accounting impact may not match the economic reality.

Which tends to confuse people who only look at reported numbers.

Timing and Forecast Accuracy

Hedging relies on forecasted exposures.

If forecasts are inaccurate:

• You hedge too much 
• You hedge too little 
• You hedge at the wrong time 

All three happen regularly.

This links hedging directly to forecasting quality. Weak forecasts lead to weak hedging decisions.

Where It Goes Wrong

Some classic issues:

• Over-hedging or under-hedging due to poor forecasts 
• Using complex instruments without fully understanding them 
• Focusing on market timing instead of consistency 
• Lack of clear policy or inconsistent application 
• Evaluating hedges based on outcomes instead of objectives 

The last one is particularly common.

A hedge that “loses money” may have done exactly what it was supposed to do.

Treasury’s Role in Hedging

Treasury doesn’t try to beat the market. It creates structure around uncertainty.

It ensures:

• Risks are managed consistently 
• Decisions align with policy and risk appetite 
• Financial impact is stabilised where needed 
• The company avoids unpleasant surprises 

Because in the end, hedging is not about being right.

It’s about being prepared.

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