J.P. Morgan’s view: Unlocking the value of data in treasury

Thanks to emerging technologies and data analytics, treasuries can leverage transaction data from past events and use it to deliver meaningful insights and prescribe future actions, in real time. While this can cut costs and reduce risks, it is also changing the way treasuries function. 

Q. How is data changing the treasurer’s role?

The role of the treasurer has become increasingly strategic. This widening scope of responsibilities requires that they have ready access to business intelligence.

Treasurers recognise that data analytics can identify inconsistencies in processes, detect anomalies and extract insights via benchmarking exercises and pattern recognition. In addition, the use of predictive analytics can optimise liquidity, and mitigate risks.

In the past, the real challenge was around the ability to gather and manage huge volumes of data in order to successfully distil insights.

However, advances in machine learning and artificial intelligence are a game-changer.

Today, presentation formats, such as data visualisation tools – which often have open application programme interfaces directly linked to treasury management systems for quick and effective results – mean treasurers can view and draw insights from transaction data quickly, and at scale.

This wealth of data, from tracking payments to receipt flows and account balances, especially when accumulated over time, can be used to visualise flows and uncover hidden patterns, from which treasurers can draw benefits, ranging from significant savings, to strengthened internal controls, and monitoring.

Real benefits of putting data to work

Q. How do I incorporate more data analytics into my treasury function?

Incorporating more data means weighing up considerations. These include:

• Determining the amount of data to be generated
• Evaluating data already on hand
• Assessing how to pool information into databases to run analytics
• Establishing if data can be warehoused offshore (due to regulatory constraints in different markets).

Having the right resources can be challenging. Corporates in the early stages of digitalising treasury processes may need to invest in training and reskilling staff.

If inefficiencies or risks are identified, internal teams need to act on these findings. Often, this requires collaboration with parties outside the treasury function. For example, changing payment terms with suppliers and customers requires business support to implement strategies to improve working capital. 

Establishing a data analytics function from scratch needs significant investment. Increasingly, corporates are relying on their banks to help support the integration of data – one of the reasons J.P. Morgan’s new interface, DataLab, is designed to convey business intelligence that’s digestible and easy to access – so clients can focus on their strategic objectives. 

Q. How does DataLab analytics help treasuries?

J.P. Morgan maintains a data lake of transaction information which together with DataLab technology, is able to take a bird’s eye view of a client’s transaction flows. This can then be used to generate sophisticated analytics using large amounts of payment and balance data.

Treasuries can use this information to drive better cash and liquidity management, in their decision-making process, to validate business models and to establish short and long term visions with quantifiable business benefits.

As DataLab does not need configuration or vendor customisation – which can be expensive to implement and maintain – it is easy to adopt without additional resources. 

Q. Where has DataLab been used by clients?

Clients have used DataLab to harness data analytics in corporate treasuries to creating efficiencies in payment.

For example, one of the bank’s technology clients discovered that by using DataLab analytics, it was making multiple payments daily to the same supplier. By moving payments to weekly runs, the client reduced the number of payments from 483 to 52 – an 89% improvement in efficiency, in addition to making further saving from lower fees related to fewer transactions.

DataLab also identified inefficiencies in an aerospace company’s intercompany funding. The firm had residual balances in accounts across many of its Asia-Pacific markets to make local payments which weren’t fully utilised. By implementing another of the bank’s tools – J.P. Morgan’s Just-in-Time Funding solution – the corporate made significant cost savings by funding local currency payments from a central account, as needed, without leaving residual balances in those markets. 

This technology can also identify risks. One oil and gas sector client used DataLab to detect a number of non-core currency accounts, mostly in exotic currencies, which exposed the firm to FX risk. This led to account rationalisation. Having fewer bank accounts also translates to lower bank fees and maintenance costs.

Q. How will data analytics evolve in the future?

J.P. Morgan sees data analytics playing a key role in the evolution of the real-time treasury where transactions are conducted and reported instantly and automatically.

As more investment flows into data analytics platforms and user interfaces, corporates will benefit from greater access to information, and the ability to customise data as needed. We also expect the use of big data to uncover hidden patterns and previously unknown correlations – which will continue to expand treasury’s knowledge and decision-making processes. 

To learn more, please contact:

Amy Eckhoff Head of Commercialization & Innovation, Liquidity solutions, Treasury Services, Asia Pacific, J.P. Morgan [email protected]	May Leen Wee Head of Payments Product Management, Treasury Services, Asia Pacific, J.P. Morgan [email protected]