In an increasingly data-centric world, it’s only a matter of time before finance organisations fully rely on data-driven business operations. Unsurprisingly, this approach is part of a multi-year plan for two-thirds of finance professionals, according to findings from Onguard’s recent FinTech Barometer – but only 7% believe their own organisation is already data-driven.
Despite the current low adoption rate within the finance industry, it’s clear there are significant benefits to embracing the data-driven operational model of the future – particularly when it comes to treasury management. 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 risk. This wealth of data, from tracking payments to receipt flows and account balances, can then be used to visualise flows and uncover hidden patterns, improving internal controls and resulting in significant savings.
A combined approach
But companies wanting to become data-driven face limitations. Combining data from a variety of internal and external sources can be a huge challenge, although this is essential for accurately predicting credit scoring, payment behaviour and cash flow. While finance organisations usually have sufficient data, they can’t gain optimum value from it if they can’t combine it.
This is where 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; cash flow and liquidity monitoring; mitigating interest rate, counterparty and foreign exchange (FX) risk; and compliance. It can also be used as a tool to combat fraud and money laundering and is vital in managing financial sanctions.
Therefore, as treasurers strive to strengthen working capital, secure liquidity, manage risk and simplify processes, big data can be integrated with other customer relationship management (CRM) systems, external data sources and credit management systems to support more efficient and effective decision-making.
Skills, expertise and the right tools for the job
A further challenge to organisations becoming data-driven is the lack of expertise in data processes and analysis. The role of the finance professional is evolving in response to the growing demand for different skill sets within the world of finance. According to the finance professionals, organisations need analytical ability and skills in communication and programming in order to become data-driven. In addition, the knowledge and skills traditionally associated with treasurers and finance professionals also remain crucial to interpret figures.
However, it’s also essential to ensure treasurers are equipped with the right tools and technologies to help them accurately interpret these figures. By providing access to data visualisation tools, which often have open application programme interfaces (APIs) directly linked to treasury management systems for quick and effective results, treasurers can view and draw insights from transaction data quickly and at scale.
Similarly, this can enable treasurers to run detailed transaction analysis to institute cash culture programmes within their cash management processes. These include compressing payment terms, accelerating initial customer contact for collections, and consolidating the number of collection paths for a more streamlined approach. Data analytics can also be engaged to reconcile the fixed asset book to tax differences, and review source data to reconstruct accurate fixed asset tax records for compliance.
As such, it’s clear that, once again, a combined approach is needed. By training staff and recruiting new talent to specialise in data analysis, as well as providing the latest Big Data and analytics technologies, organisations can capitalise on the wealth of skills, experience and tools they have at their disposal to create data-driven treasury departments.
The future of data-driven treasury management
By embracing a data-driven approach, finance organisations and treasurers will be better equipped to make more informed decisions, offer superior customer service and gain a competitive edge in the marketplace. By using the insights gained from data, inefficiencies or risks can be easily identified, and treasurers can then collaborate with the right internal teams to act on these findings. For example, by engaging business support teams to assist with changing supplier payment terms and implementing strategies to improve working capital.
Above all, this will dramatically improve the customer experience; with data-driven insights paving the way for true innovation, personalisation and efficiency, lowering days sales outstanding (DSO) and improving cash flow.