During these unprecedented times, corporate treasurers must deal with a difficult and uncertain new normal.
Drawing relevance of data analytics to the treasury function, Börries Többens – senior manager, finance advisory, KPMG writes: “Analytics serves to derive specific measures/decisions from findings and is upstream from the decision itself. After a decision, however, it comes back into play, since it helps to determine whether or not the decision/measure has led to the desired outcome and if this was effective and efficient. A cash position thus does not come under analytics, and nor does liquidity planning, derived from a mix of posted receivables and liabilities and corporate planning.”
According to Sam Wong of EY, there are many areas where the treasury function can use data analytics.
He adds: “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.”
Craig Davis of KPMG sums up the significance of data analysis for a treasurer: “Corporate treasury is increasingly becoming critical for the financial health, growth and successes of all organisations. Most corporate treasury groups rely on multiple data sources and solutions to address business needs.
“This approach only brings increased operational difficulties and introduces further risk, rather than addressing actual challenges. At a time when many companies are looking to reduce their IT expenditure, data analytics can offer clear benefits in terms of reduced costs, better forecasts and improved decision-making for critical treasury functions.”
Data analytics 101 for treasurers
Here is a mix of traditional and contemporary data analytics techniques that help treasurers to visualise flows and discover hidden patterns, from which the business can draw benefits, ranging from significant savings to strengthened internal controls, and monitoring.
Trend analysis as a data management technique helps treasurers in spotting actionable patterns in the presented information. Corporate treasurers can use this technique to analyse the trend of the company by comparing its financial statements, either with the trend of market or results of the company’s past performance.
Revenue and cost analysis of a company's income statement can be arranged on a trend line for multiple reporting periods and examined for trends and inconsistencies. For example, a sudden spike in expense in one period followed by a sharp decline in the next period can indicate to the treasurer that an expense was booked twice in the first month.
When used internally, trend analysis could be one of the most useful management tools available to the corporate treasurer. Trend analysis could be used as a data analytics technique in the following ways:
- Examining revenue patterns to see if sales are declining for certain products, customers, or sales regions.
- Examining expense report claims for evidence of fraudulent claims.
- Examining expense line items to see if there are any unusual expenditures in a reporting period that require additional investigation.
- Extending revenue and expense line items into the future for budgeting purposes, to estimate future results.
Horizontal and vertical analysis
EY’s 2016 research, The DNA of the CFO, found that finance leaders are under intense pressure to balance their responsibility for financial stewardship with their role in developing the organisation’s future strategy in an age of increased regulation and widespread digital disruption.
Horizontal and vertical data analysis can provide a corporate treasurer with forward-looking reporting since these techniques aim at ascertaining the trend and changes in the financial performance of the company over time and the impact of a particular factor in an accounting year.
In horizontal financial analysis, the comparison is made between an item of financial statement, with that of the base year’s corresponding item. On the other hand, in vertical financial analysis, an item of the financial statement is compared with the common item of the same accounting period. While horizontal analysis could be helpful in intra-company comparison, vertical analysis can help in both intra and inter-company comparison.
Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. It can be utilised to assess the strength of the relationship between variables and for modelling the future relationship between them.
According to Treasury Today, regression calculations could be used in the treasury function to determine the extent to which specific variable factors, such as interest rates or the price of a commodity, influence movements in the price of an asset. In receivables management, for example, the impact of early-payment discounts offered to customers might also lend itself to regression analysis.
One application of regression analysis for treasuries is explained in this article by Darren Greway. He says: “Regression analysis is flexible and may be tailored to the hedging program and risk management objective. Whatever test you decide to use, you must ensure that it accurately reflects the risk you are hedging and that it is applied consistently. Reliability, consistency and flexibility come at the cost of complexity and time. A small hedge program might be effectively managed within a spreadsheet, but for a larger program or multiple hedge programs, a dedicated software solution is the most cost-effective and audit-proof way to implement regression.”
Summary data analysis
Summary data analysis is used to count key core indicators, such as the company’s annual operating income, annual consumption costs, and annual net profit, which are often the data that decision-makers are most concerned about. Such analysis will certainly count as business intelligence instead of reports.
Bob Stark, head of product and market strategy at Kyriba, says with such business intelligence, treasurers can help their CFOs sleep more soundly: “Quality reporting is a core requirement of treasury, as treasurers are asked to deliver a wide range of reports for management and oversight. Business intelligence helps solve this problem by relying on data visualization instead of displaying rows and columns of data. Dashboards enable business conclusions and outlying information to (figuratively) pop out of the screen, drawing attention to the KPIs and explanatory information that the CFO really wants to see. Instead of forcing executives to scan through reports, business intelligence tools leverage visual features in addition to offering a dynamic “on the fly” interactivity that static reports can never offer.”
Treasurers could represent the analysis for this data analytics technique by combining it with a great dashboard for the CFO and the board’s perusal towards decision making.
Data comparison analysis
Data has no significance without comparison, and the same is true in financial data analysis. Through the comparison of various indicators, the treasurer can reveal the financial status, operating conditions and cash flow of the company.
One such application is discussed by Ingo Schorn, the head of treasury and Katja Lehner part of the treasury front office at Andreas Stihl, where comparison allows access to a wide range of data and helps to base detailed analysis.
The reference standards for comparative analysis to a corporate treasurer could be:
- Time comparison: Comparing with the actual data of the previous period and the same period of last year
- Entity comparison: Comparing with the data of peers in the same industry
- Result comparison: Comparing with the plan or budget data
- Structural comparison: Based on the composition analysis, compare the structure of the two data and analyse the changes in financial indicators
Data analytics to the rescue
The challenges faced by businesses have become a new front in the battle over the coronavirus. The rapidly changing situation around the coronavirus spread can be taken as an opportunity to combat the worrying outlook.
Though no one has a playbook for this, the measures that corporate treasurer can take in response by adding data analytics to their arsenal might help them over the long term.