From Reactive to Proactive: Data-Driven Treasury Management for Strategic Decision-Making

Treasury functions are facing pressure from CFOs and other business units to contribute more of a strategic value and make data-driven decisions that optimise cash flow and take a proactive approach to any financial risks.

There are several factors driving this shift. Market volatility, geopolitical risks, and global disruptions demand a proactive, data-driven approach, and the traditional treasury models are no longer sufficient. For multi-subsidiary businesses as well, managing cash flows across multiple entities, currencies, and jurisdictions has become more challenging.

This change in needs and demands presents an opportunity for treasury professionals to reposition themselves from reactive financial managers to proactive value creators within their organisations.

In this article, we explore how treasury functions can go from being purely reactive to more proactive and how data-driven treasury can support this shift.

Many organisations still struggle to unlock the full potential and reap the benefits of data-driven treasury because of data fragmentation, prevalence of manual processes, inaccurate cash flow forecasting and complex compliance risks. Inconsistent data, fragmented financial ecosystems and lack of real-time visibility are common for multi-subsidiary businesses.

This lack of consolidation impairs the ability to make strategic and agile decisions in the long term, while in the short term it can cause poor capital allocation or missed investment opportunities.

On top of that, despite available automation, a lot of treasury day-to-day tasks still rely on spreadsheets and manual work, making treasurers incapable of responding to the growing pressures and increasing volumes of a growing business. Just as manual work is error-prone, relying on historical data and subjective assumptions cannot produce accurate or reliable forecasts that can withstand volatile markets.

The growing challenges have pushed more and more treasury teams to look into how real-time data and analytics can improve key treasury operations, including intelligently automating key functions.

Investment Decisions: Optimising Capital Allocation

Traditionally, surplus cash needed to be managed by manually reallocating funds, which often resulted in delays, missed opportunities and poor returns. A data-driven approach to capital allocation allows for a more proactive approach thanks to:

Dynamic investment allocation: Gaining real-time visibility into cash positions across all subsidiaries and accounts means that treasurers can, firstly, quickly reallocate funds or even set automatic rules. With that, they can optimise liquidity and efficiency.

Risk-adjusted decision-making: Receiving detailed insights on counterparty risk, market conditions, and internal liquidity allows treasurers to engage in more sophisticated investment strategies that balance return objectives with risk tolerance.

Opportunity identification: Analysis of financial patterns and trends can reveal investment opportunities that could have otherwise been overlooked, such as correlation benefits or more favourable timing.

Borrowing Strategies: Reducing Costs and Optimising Debt

Multi-subsidiary businesses are struggling with fragmented and reactive borrowing decisions that over time amount to unnecessary costs. Data-driven treasury transforms borrowing by:

Optimising the funding mix by analysing costs, terms and implications across subsidiaries to minimise borrowing costs.

Using predictive analytics to identify optimal refinancing windows and reduce interest expenses.

Strategically placing debt within the corporate structure to enhance efficiency and flexibility by taking into account factors such as subsidiary performance, growth prospects, and local market conditions.

Liquidity Management: Enhanced Cash Visibility and Control

Liquidity management and optimisation is undoubtedly where data-driven treasury has had the biggest impact. This is primarily because of the following factors:

API-driven connectivity to banks and financial institutions eliminates the need for manual data collection and consolidation and equips treasury departments with a constant, up-to-date view of cash positions across all accounts, currencies and entities.

Automated cash pooling analyses the cash flows and regulatory landscapes amongst entities to optimise working capital and reduce idle cash. By reallocating funds to where they are most needed, financial stability is protected while operational needs are still uninterrupted.

Additionally, leveraging AI and machine learning has shifted financial analysis and forecasting from descriptive (what happened) to prescriptive (what will happen) and allowed businesses to be more proactive and resilient in the face of volatile market conditions.

Advanced forecasting models can now incorporate, beyond a company's historical financial data and business indicators, also seasonal variations and market conditions to enable scenario-based forecasting for the long and medium term. This can protect businesses from financial risks, funding shortfalls and even market disruptions or FX currency fluctuations.

Mid-sized companies and businesses, unlike large corporations with extensive resources, need to be strategic about their investment in the treasury tech stack. Below, we suggest some tools that will transform the treasury function without a significant IT investment.

1. Treasury Management Systems (TMS)

Modern TMS have evolved in the last decade to offer both transaction processing capabilities and the automation of the day-to-day treasury management tasks as well as analytical capabilities.

The right tool for a mid-size company should offer at least consolidation of financial information from across the organisation, offering both a 360-degree overview as well as granular per-entity insights, as well as key metrics provided in real-time that can be aggregated into customisable dashboards. Some might also have forecasting modules that combine historical data with user inputs to project future cash positions or some scenario analysis tools to model how different business conditions or decisions will impact the cash positions.

The key is to prioritise TMS that are able to accommodate growth and offer robust integration capabilities.

2. API-Driven Bank Connectivity

Companies without extensive resources and IT support can turn to API integrations for real-time access to data exchange. There are a few kinds of API integrations available for treasury:

Banking APIs sync banking data in real time, eliminating manual downloads and consolidation.

ERP integrations extract data around transactions, payables, receivables and expenses and automate reconciliation.

Market data feeds supply current rates and indicators.

Leveraging connections between existing systems can allow mid-sized companies to achieve many of the benefits of real-time treasury without the complexity and cost of full system replacements.

3. AI-Powered Forecasting and Risk Analytics

Modern forecasting and risk analytics tools leverage machine learning and AI to improve the accuracy of predictions, automate risk assessments based on historical data, and incorporate multiple data sources in their analysis and estimate the impact of variances on treasury KPIs. As the data gets more accurate, organisations will benefit from more accurate cash flow projections, faster identification of liquidity gaps and optimisation of working capital. These can be particularly useful for treasury departments that require more strategic support with decision-making across various business entities that have different levels of financial sophistication.

4. Data Visualisation Platforms

The last bit of software worth considering, either as part of the TMS or through an API integration, is a data visualisation tool that can transform complex financial data into a visual format. With that, treasury teams can spot trends and patterns faster and act on them, monitor KPIs in real time and share them with key stakeholders.

Technology is only part of the solution on the route to making your treasury department more data-driven. Before any major tech changes can be implemented, establishing a robust foundation of high-quality financial data and processes is key for growth.

Data Quality and Governance

The strategic value of treasury data depends directly on its quality. Key considerations include:

Standardisation: Establishing consistent formats and definitions.

Validation: Implementing automated checks to identify errors or inconsistencies.

Ownership: Assigning clear responsibility for maintaining different data elements.

Policies: Developing and maintaining guidelines for data collection, storage, and usage.

Process Integration

For data-driven treasury to work across the entire financial and treasury function, it needs to be closely integrated with broader financial processes. The full spectrum of financial activities needed for full treasury insights includes:

Order-to-cash alignment: Ensuring treasury has visibility into sales pipelines and receivables status.

Procure-to-pay connection: Linking treasury forecasts with procurement plans and payment schedules.

Financial planning coordination: Aligning treasury projections with budgeting and planning cycles.

Risk management integration: Connecting treasury data with enterprise risk management frameworks.

Skill Development

Transforming treasury into a strategic, data-driven function requires new capabilities within the team. Beyond the basic familiarity with digital tools, they need to be trained and upskilled in their ability to understand, interpret and work with complex financial data as well as identify patterns, develop recommendations and understand how these fit in the wider business context.

For most organisations, the journey to data-driven treasury begins with using it in specific high-value use cases:

Cash flow forecasting: Developing models that accurately project future cash positions across all subsidiaries.

FX exposure prediction: Anticipating currency impacts based on business activities and market trends.

Working capital optimisation: Identifying opportunities to improve the cash conversion cycle.

Counterparty risk assessment: Evaluating and predicting changes in the risk profile of key financial partners.

The ultimate goal of data-driven treasury management is to move from descriptive reporting (what happened) to predictive analysis (what will happen) and eventually to prescriptive guidance (what should we do) - it's a process of ongoing refinement, but starting with the areas of biggest impact can bring not only the most immediate benefits but also buy-in from the most hesitant stakeholders.