Big Data and analytics in treasury management

Corporate treasury is under increased scrutiny today. In particular, as more and more shareholders ask companies to demonstrate how they manage financial resources and financial risks, a global trend toward centralisation of treasury activities has emerged. In addition, treasurers need to cope with increasingly complex financial instruments, volatile financial markets and the introduction of new regulations and accounting practices. For the treasury department, this means there is a need to continually update know-how in order to bring about a reduction of costs and volatility, while delivering value and short but expansive lines of communication to the company.

Against such demands, how can corporate treasury obtain the necessary information to deliver the right insights for members that deliver increased shareholder value, improved business operations and performance for the organisation?

Creating an analytics-driven organisation

In just a few years, the terms “big data” and “analytics” have become hot topics within company boardrooms. The treasury and finance functions are clear beneficiaries of analytics, which provides greater insight into customers, competitors, profitability and processes. Analytics can also strengthen the chief financial officer’s (CFO) ability to drive strategic decision-making and investment planning. Thus, creating an analytics-driven organisation has also become the top driver of collaboration between the CFO and chief information officer (CIO).

Many executives understand that embracing big data and analytics is crucial to keeping their organization nimble, competitive and profitable. Yet the challenge is how to invest and implement data analytics that can derive useful insights for business improvement. For a start, it is important to get the buy-in from the board of directors. Board members need to understand the complexities and have a grasp of the issues surrounding these technology trends. Equally important, they should be prepared to ask the right questions of the executives in charge of big data and analytics initiatives.

With the large and disparate volumes of data being created within the organisation, innovative and scalable technology is needed to collect, host and analytically process it.

Big data includes information garnered from social media, internet-enabled, machine, video and voice recordings, and is typically characterised by the four Vs:

• Volume: the amount of data being created is vast, compared to traditional sources.
• Variety: data comes from different sources and is being created by machines and people.
• Velocity: data is being generated extremely fast – a process that never stops.
• Veracity: big data is sourced from many different places; as a result, treasurers need to test the veracity and quality of the data

While big data is often defined by the volume, the value is equally important. For the CFO and treasurer, data analytics can offer significant value across a variety of financial and non-financial activities. For example, in forecasting, organisations can use data from various sources, such as unstructured data from embedded sensors and social media feeds to understand market signals. Incorporating real-time market signals and analysing their impact on revenue creates a new level of forecasting accuracy with real-time availability.

Leveraging big data and analytics in treasury functions

Big data and analytics can support treasury management activities. There are many areas where the treasury function can use data analytics to its advantage, such as asset and liability management; hedging of interest rate risk and foreign exchange (FX) risk; cash management; and compliance.

In asset and liability management, treasurers can leverage data such as foreign exchange; market value assumptions; mark-to-market (M2M); bank data; rates and spread to conduct analytics for insights into stress testing; interest rate risk management and fund transfer pricing; balance sheet strategy; and multi-factor behaviour models for consolidation.

In hedging of interest rate risk and FX risk, treasurers can turn to economic fundamentals for each country, and analyse currency to determine the necessary time to hedge. Analytics allows a more efficient process to run simulations to test the effectiveness of hedges and to price complex derivatives

Additionally, in cash management treasurers can run detailed transaction analysis to institute cash culture programmes; compress payment terms; accelerate initial customer contact for collections; and consolidate the number of collection paths.
On the same note, data analytics can be applied to reconcile the fixed asset book to tax differences and review source data to reconstruct accurate tax fixed asset records for compliance.

Big data-aligned treasury agenda

To implement big data and analytics into the treasury function, organisations can look into developing a big data-aligned treasury change management agenda, covering the management model, treasury operating model and the IT architecture. This includes designing a big data-aligned treasury transformation strategy; updating the treasury model and organisation management model; designing a big data-aligned treasury architecture; optimising the treasury operating model; and implementing big data aligned to the treasury management system (TMS).

Thereafter, the next generation treasury operating model can focus on management activities, including separating operative treasury (trading) and strategic treasury (holistic organisation management, optimisation of business performance and shareholder value). The added insights from data analytics also mean that the treasury function can allow innovative and new services, as well as playing a key bridging role between the lines of business.

Indeed, with the insights to be gleaned, data and analytics looks to be the way forward for the treasury function.


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