Enhancing treasury performance through data

Data matters. It is a resource, a threat and a responsibility. It is at the heart of the digital transformation every treasurer must undertake.

Data has always been key to treasury. It is instrumental in the basics of the function, in forecasting cashflows, managing liquidity and ensuring correct funding levels.

But there has been a step change in the volume, importance and potential of data. The ability for treasurers to analyse data in a real-time environment can create valuable insights that can be actioned at speed.

Doing so requires the right tools. No longer are spreadsheets sufficient: aside from issues of version control and data quality, spreadsheets lack the deep analytical tooling that will help treasurers understand and interpret the data they have. Antiquated models cannot cope with the overwhelming volume of relevant data, nor the need to adapt and leverage large data sets in real time to make important financial decisions.

Instead, treasurers are turning to more integrated, fit-for-purpose tools to help them manage their increasingly complex operations. Data analytics has become a key pillar of modern treasury management.

Organisations have always had access to data, but only now is it possible to acquire it in real time and to transform it into valuable and actionable insights. This shift has elevated the role of treasurer to that of a strategic partner to the business: more than ever, they can be a guide to what has happened in the past, what is happening now and, crucially, what might happen next.

Once an organisation has arranged its sources of data – for example, from an Enterprise Resource Planning (ERP), Treasury Management System (TMS), Customer Relationship Management (CRM) and bank APIs – there is value to be discovered that has a wide range of benefits for treasury:

• Cashflow forecasting is an area where data can significantly benefit corporate treasurers. By combining the various data components from a variety of internal and external sources, treasurers can deliver a more accurate cash flow forecast.
• Drawing actionable insights from the analysis of payment data can help on several levels – for example, to meet objectives in reducing costs in payables and in improving payment operations. Data analysis can show where inefficient payment methods are being used, or identify important suppliers to help determine appropriate payment terms to drive better working capital performance.
• Fundamental goals within receivables management include evaluating customer credit risk as well as tracking and pursuing payment of invoices. Predictive analytics tools can be used to comb through large volumes of data to identify patterns and trends – for example, in historical payment data to identify customer payment patterns, credit risk, and payment default chances. More advanced financial predictive analytics algorithms could potentially predict the date when a customer can be expected to pay.

Data analytics – used to identify patterns, trends and variations in data using techniques such as artificial intelligence, predictive analytics and machine learning – potentially brings benefits beyond liquidity, payments and collections. It can assist treasurers to monitor and detect fraud, identify potential issues that could help ensure adherence to internal and external policies and regulations, measure financial performance and optimise working capital.

Harnessing the power of data requires a structured approach, starting with clear objectives. Rather than setting about a general ‘big data project’, treasurers are likely to gain better outcomes by homing in on specific inefficient tasks and working out how data can improve efficiency in that task.

It is useful to start by identifying the treasury workflows and the types of data they utilise; a collective understanding within the treasury department makes it easier to identify potential opportunities. Engaging different stakeholders within the organisation will help drive momentum.

Documenting learnings along the way is important: it can be thought of as a first draft of a data strategy which can then be built upon and shared.

Data analytics is essentially a series of logical steps, each of them important. After collecting data, the next step is data cleaning: data is not usually created purely for analysis so must be processed and validated to be ready for analysis. A final important step is data visualisation, for example, through maps, charts or graphs that help make it easier to understand trends or variations and to communicate findings to others.

Some treasury management systems already in place within organisations also allow the automation of certain processes. This helps streamline treasury processes whilst minimising risks. Automation of payments, for example, results in faster processing, fewer manual errors and greater accuracy.

Moving data analytics forward in a treasury function is rewarding but challenging, requiring considerable planning. Several distinct challenges should be expected and addressed.

Data is likely to exist in a multitude of silos around an organisation, and it is important to understand where these silos are, who owns them, and how they can be broken down. A key aim for any treasury data strategy should be the creation of a golden source of data.

But the quality of that data is likely to vary, which makes it important to have comprehensive policies in place around data governance and control.

Then there’s quantity: already overwhelming, and becoming more so by the day. Too many inputs create a recipe for confusion. A key lesson here is not to deviate too far from treasury strategy, and to remember what that strategy is intended to achieve: less can sometimes be more.

Finally, it’s important to have clarity on an organisation’s data resources, both in terms of technology and personnel. Treasurers must understand what they need from technology, so they can work out the tools they need to bring in to achieve their goals; and must upskill existing staff while hiring the right data-centric skills into an organisation.

The challenges are considerable, and the preparation that goes into surmounting them is wide-ranging and onerous. But the potential outcomes more than justify the work that is put in. Data is one of the most powerful resources in the modern treasury world, and the only thing that can be said with certainty about the future is that data’s importance will only increase.