Finance teams are facing tremendous pressure to reduce their operating costs, while stepping up to become a strategic partner to various parts of the organization. The expectations are clear: reduce the effort spent on routine tasks and use high value efforts to help the company navigate through a fast-paced, highly competitive environment.
In fact, in a recent Oxford Economics study “How Finance Leadership Pays Off: Efficiency Helps CFOs Stay Ahead of the Pack,” 73% of surveyed finance executives agree that automation is improving efficiency within their organization and throughout the company, freeing up bandwidth for more strategic tasks.
Finance organizations have already consolidated repetitive, resource-intensive tasks in shared services centers to gain scale and efficiencies while benefitting from lower labor costs. Now the opportunities for further labor arbitrage are leveling off, as wages in these locations are rising as well. Nevertheless, expectations of further cost reductions and efficiency gains remain high.
Advances in computing power are enabling machine learning applications that can drive the next level of efficiency gains. Adding intelligence to ERP systems will allow for the automation of processes that cannot be handled through rules-based approaches.
Take the example of receivables management: matching incoming payments with open receivables is a very labor-intensive task. A customer of mine noted that at their organization, 400 shared services agents spend 80% of their time on this clearing process. Through classic approaches, automatic matching rates from 30% to 90% are achievable, depending on customer and country. The remaining payments currently have to be cleared manually, because details are missing or multiple invoices are paid together. Even to maintain these levels, substantial implementation and maintenance efforts are required.
Machine learning services can dramatically increase these automation rates by learning from a large number of past accountants’ decisions and applying them to future payments. This enables substantial reduction of labor cost per invoice (and faster clearing) while increasing consistency and service quality.
Factoring in information from remittance advices and payment advices provides further potential for efficiency gains. Payment advices are the basis and precondition for further processing. Currently, these advices are often not entered into the system due to technical barriers and effort and are used for a manual clearing of payments only. Automatically extracting information about payments from unstructured advices (e-mail, PDF, paper) and using them in the clearing process will further increase automation rates. In addition to the cost reductions, the faster processing time will also reduce the number of days of sales outstanding, allowing for a reduction in working capital.
Every added degree of automation will bring finance organizations closer to the vision of lights-out finance operations, where transactional tasks will be handled through an intelligent, self-driving ERP system. Freeing up Finance from tedious manual tasks will allow them to focus their attention on strategically partnering with other board areas to jointly guide their businesses towards growth and innovation with decisions made based on more current, complete and reliable information.
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