Refining data into advanced data with the power of analytics
Current corporate slogans aside, data is not the new oil. It might have been a good analogy when the deluge of consumer transactional and behavioral data showed up in terabytes on the digital doorstep. Data, like oil, was valuable fuel for driving decisions from operations to marketing to sales. As long as the oil kept coming, customers continued to evolve from a grainy snapshot to a clear digital image.
Times change. Oil is a fossil fuel. A finite resource. Data has evolved and the ability to aggregate and analyze it has gained sophistication. For smart corporates, it has become more like green energy: renewable, efficient and unlimited in its potential. In the latest Payments Podcast, I discuss how data, as seen from a green energy perspective, can be used by corporates to power higher performance across payables, treasury and receivables. As such, new technologies and better analytics can make these units truly interconnected. Data is not new. But its ability to drive decisions in disparate parts of a corporate, but in a synchronized and connected way, is its logical evolution.
Advanced data analytics and emerging usage of data science such as AI and predictive analytics are becoming essential tools for treasurers and other financial decision-makers. They are becoming more adept at accessing data from inside a business such as the ERP, treasury management system and CRM systems in real-time.
The real value lies in combining this enterprise data with banking data and getting a single, summary view of all this data using dashboards, which show a business’s key performance indicators. Financial decision-makers can greatly benefit from analytical tools which enable them not only to measure their KPIs (like days sales outstanding, days payables outstanding, cash conversion cycle etc.), but also to improve these metrics and therefore have a positive effect on the organization’s overall financial performance.
In the podcast we take a deep dive into treasury data and its “green energy” applications. The combination of data within the treasury management system, ERP and bank accounts can create more accurate cash flow forecasts for treasury executives, which is at the heart of their job. There are three requirements in which data can play a more urgent and active role:
Variance analytics: By analyzing the variance of previous historic cash flow forecasts predictive analytics can be used to improve the accuracy of future forecasting. Some of the inbound and outbound flows are highly predicable (e.g. loan repayments on fixed maturity dates), while others are less easy to predict, such as when a customer will pay an invoice. If an item is not paid or received on the anticipated date it needs to be rolled forward in the cash flow forecast to a suitable future date and re-assessed automatically to see whether it has been paid or remains unpaid.
Efficient liquidity management: It could be argued that this is the essential treasury function. A treasurer must balance the basics of liquidity management with the need to maximize returns on surplus cash and minimize interest charges on loans and overdrafts. Working in conjunction with cash flow forecasting, an efficient treasury management system allows treasury teams to set up automated sweeps and cash concentration pools. By associating an account hierarchy with rules establishing minimum and maximum balance thresholds, an efficient sweeping system can manage the optimum set of bank transfers for a corporate’s efficient liquidity management.
Efficient reconciliation: It is essential to constantly reconcile bank statements against ERP ledgers to allocate cash correctly. Assembling the necessary data and analyzing it in real-time is easier in systems that span the full cash lifecycle, in particular payables, treasury and receivables. It is important to combine data transformation with reconciliation, as data can often arrive in disparate formats and needs to be normalised.
Of course, corporates have to make data work across the three areas mentioned earlier: treasury, payables and receivables. Data and advanced analytics can help integrate these three units to create a more focused and efficient operation.
Intelligent use of data analytics transforms how finance professionals work by providing unprecedented real-time control and visibility of payables, treasury and receivables through a single platform connected to multiple banks. The objective here is to aggregate enterprise and banking data in real-time to provide a single view of all cash management activities in multiple currencies, presented on personalized and informative dashboards, which identify trends and highlight risks relating to fraud and financial crime, regulatory compliance, as well as foreign exchange and interest rate movements.
Looking to the future, with several new payment initiatives and regulations in process, the ISO 20022 messaging scheme will allow payments to carry more structured data to add to that integration of treasury, payables and receivables. It will also reduce the risk of errors, as users will be able to include additional payment details and references. Using this more structured and richer data will also help companies receiving payments to achieve higher levels of automated reconciliation. This in turn will enable a much deeper level of data analytics and insights about why payments are made and to whom.
It’s also important to note that achieving more effective data analysis across treasury, payables and receivables will be enhanced by the growth of e-invoicing.
The e-invoicing market is growing rapidly and it involves the secure exchange of large quantities of data. The important thing about e-invoicing today is that there are more and more countries.
In Europe, Asia and the Americas which are mandating the use of e-invoicing to accelerate the cashflow of small and medium enterprises (SMEs) and to raise the efficiency of tax receipts. It’s these new government-mandated rules which are driving much of the current growth in e-invoicing at more than 20 percent a year.
Listen to the full Payments Podcast, Data Analytics in Treasury: The bedrock of better financial performance
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