Survey: Most Organizations Fail to Leverage Finance and Credit System Automation
Thursday, August 15, 2019
While 87% of respondents to a Dun & Bradstreet automation survey of finance and credit leaders believe automation will improve the respective function's efficiency in the next three years, most organizations are not leveraging automation to their fullest potential yet.
“Dependable and comprehensive data is at the core of successful automation,” said Andrew Hausman, general manager of Dun & Bradstreet's Finance Solutions. “When fueled by analytics and insight, automation can not only reduce operational costs and increase efficiency, but it can help open new avenues of growth across the business.”
Eighty-three percent of respondents are currently using some form of automation within their team processes and believe it is improving their function’s efficiency by giving employees more time for value-added tasks. However, most companies are not automating their processes to full potential, with 62% of surveyed companies sharing that they are automating less than a quarter of their processes. Billing (43%), credit scoring (36%), reporting (30%) and collections (30%) are listed as the top processes currently being automated in the finance function today.
The report's key findings also include:
- Reliable Data & Integration are Essential: Reliable data is the top success factor of automation efforts, with over 67% of respondents citing this as a top need. Integration with other systems (58%) and time (47%) were also listed as top success factors. The key components currently included in automatic workflows are systems integration (42%), scoring (36%) and use of a customer/supplier master file (29%).
- Operational Efficiency is a Key Driver: Improved speed of processes is the top force driving the need to automate, according to 68% of respondents. This is followed by cost savings (55%).
- Vast Potential Remains within Automation Efforts in Finance: The biggest barriers to automation are integrating multiple systems/tools (32%) funding/budget (26%) and managing disparate data (15%).
Download the study here.