Part 2 of our Mistrust in the Numbers series. Read Part 1 here.
In an age of increasing global competition and unpredictable economies, the pressure to ensure accurate reporting and make informed business decisions is mounting.
BlackLine wanted to find out exactly how this pressure affects the accounting and finance function, so we commissioned a global survey of 1,100 C-suite executives and finance professionals. The survey was designed to gauge confidence in financial data and the perceived impact of errors on the business.
The results revealed a heightened need for accountability, quality data, and trust in those numbers. Here’s how CFOs can help their organization improve in these three critical areas.
Where’s the Accountability?
According to the survey, 41% of respondents are more aware than ever of the risks of incorrect reporting, and ready to see more accountability in the industry. Over three fourths (77%) of respondents went so far as to say that audit committees should have the same accountability as the CFO for reporting inaccurate financials.
These metrics speak to an underlying mistrust in the numbers, specifically by finance professionals who are not in upper management. Moving forward, it’s imperative that CFOs regain this lost trust and make moves to ensure the reliability and accuracy of financial data.
In this vein, 38% of respondents have reviewed internal and external audit processes, with 28% claiming to have changed their company’s reporting processes.
While this type of reform certainly plays a role in renewing trust in the process, accounting automation is the logical next step to ensure accountability when reporting financial data.
BlackLine’s built-in segregation of duties maintains the integrity of accountants who prepare and approve account reconciliations at month-end, and also creates user roles for CFOs and auditors. So, whenever reports are certified or adjustments are made, there is an audit trail to provide complete visibility and accountability.
Ensuring Quality Within the Massive Quantities of Data
In addition to elevating Accounting and Finance’s confidence in the company’s future, automation ensures that important business decisions are made based on the most accurate and relevant information available.
Of those who do not completely trust the accuracy of their financial data, 41% cited manual inputs leading to human error as their top concern. But, the C-suite’s top perceived challenge was disparate data sources and uncertainty that all data is accounted for.
Implementing a digital finance solution addresses both concerns. Manual inputs are enabled but not required, and financial data is stored and updated in a centralized repository that is readily available for all reporting needs.
Automated checks and controls enable vast amounts of data to be collected and processed in real time, and updated technology mitigates the reliance on clunky spreadsheets and outdated processes that lead to inaccurate data and increased risk.
What’s a CFO to Do?
This survey illustrates a desire for more accountability and trust in the numbers by finance leaders, which perfectly positions Accounting and Finance to implement digital automation into their processes.
And while it’s a positive sign that 41% of CFOs surveyed claim that they have implemented technology to mitigate the risk of inaccuracy, this number illustrates room for growth within the industry.
“As business gets more global and complex, the definition of accuracy also becomes more complex,” says Ralph Canter, managing director or financial management at KPMG.
“Whether something is accurate depends on the question being asked. Today’s strategy is challenged by dimensions beyond reporting legal entity. Customer, market, and product line information are required by executives to compete in today’s environment.
“Enhanced data models augmented with new cloud technology can be a key component in the response to accuracy.”
Get your copy of the full survey report to learn how new technologies can enable continuous visibility and accuracy of your financial data.
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