Mikael Mangs
VP, Network Services

Outsourcing of Accounts Payable (AP) or the centralizing of functions to Shared Service Centers (SSCs), has been a leading trend for decades. This is considered a standard part of almost every company’s corporate financial services menu, but now there’s something new cooking that’s helping businesses get leaner – robotic process automation (RPA) and machine learning.

Why are companies applying RPA and machine learning to AP functions?

Flexibility, standardization, renewal of technology, best practices, reducing manual labor, multi-language support and 24/7 operations – there’s a solid business case for outsourcing AP functions. But companies are now looking to get a leg-up on the competition by further reducing errors and the need for human intervention, while collecting a wealth of financial data and reducing risk through global compliance. This will diminish repetitive tasks and unleash capacity for a digital workforce to focus on more value-add functions, such as strategic sourcing and supplier risk management.

Artificial intelligence – such as machine-learning enabled systems that analyze past data and make decisions based on experience – combined with deep automation of complex processes creates new visibility and unparalleled productivity. With this technology, AP functions become a revenue-generator in the total Source to Pay (S2P) value chain.

What AP activities are best suited for RPA and machine learning?

The biggest factors that motivate organizations to outsource any or all AP functions are process pains/bottlenecks and increased chances for errors that arise in the workflow from receipt to invoice processing, particularly when using manual entries. The desire for efficient and streamlined operational performance, and the need to optimize technology to help strategically improve operations, is increasing motivation for innovation in AP.

Below are some examples of AP functions that can be transformed by RPA and machine learning:

  • Front end mailroom, including scanning and invoice data capture of the elements to collect structured data to perform rule-based activities
  • Invoice and Purchase Order receiving and entry to ERP
  • Invoice and Purchase Order workflow, approval, discrepancy and resolution handling – AP Administration
  • Invoice automation, including automated invoice matching from match to posting
  • Reconciliation of invoice data against payments and further to GL and ERP
  • Payment approval through handling of multiple tasks in multiple sources
  • Supplier master data maintenance
  • Automated reporting for analytics and decision making purposes

What does success look like?

If you are looking to automate all or some of the functions above, you will be consistently required to show assured quality based on the operational KPIs that have been defined by your organization. Governance, cost reductions and results are under continuous scrutiny, so make sure to identify how you will determine and communicate success as you impart new technology on old processes.

Companies that have embraced this new technology look at completion and accuracy rates in areas such as:

  • Templated user management over multiple financial systems
  • New early payment discounts captured, reported and monitored
  • Automated file conversion processes between financial systems
  • Payment and statement reconciliations
  • Automated dispute resolution – such as claims and change requests handling
  • Supplier master data updates and document management through import/export
  • Fraud detection in full S2P supply chain through combined rules and trend monitoring
  • Automated data capture elements combined with machine learning
  • Multi-dimensional data analytics source retrieval and display for decision purposes
  • Shipment notification handling

Why are RPA and machine learning better than traditional outsourcing?

There can be a multitude of downsides to traditional outsourcing – ranging from lack of visibility over cost and operations, an inflexible partner, loss of supplier control and management, complex communication from providers, poor quality, etc.

RPA resolves many of the challenges that outsourcing can pose, including:

  • Lack of visibility and transparency: A business process outsourcing (BPO) partner might become a challenge in purchasing and invoicing processes and have you asking, where is my purchase order or invoice?
  • Late payments and improper treatment of suppliers: Mismanagement of suppliers can lead to increased prices and even loss of key suppliers.
  • Poor quality service and inaccurate data: Inexpensive BPO providers can struggle to provide the quality required for certain volumes and businesses.
  • Incomplete data to properly manage spend: BPO may leave holes in your data, preventing you from getting the full picture of direct and indirect spending.
  • Unforeseen operational costs: These can arise when outsourcing providers price services as all-inclusive – pay attention to topics that are out of scope. Buying a basic service might lead to a huge indirect cost increase with lots of scope and project changes.

How are companies adding RPA and machine learning to AP automation?

Forward-thinking companies are tapping into these tools by partnering with the right AP automation solution provider who has experience in combining the capabilities of solution, automation, RPA and quality. If you want RPA and machine learning, partner with a provider that is committed to innovation and actively exploring new avenues in financial technology to deliver you the latest and greatest solutions. As these providers roll-out new releases, more and more of this technology will become available to you as a customer. Also, look to partner with a provider that offers business consulting to help you best apply innovation at your organization and maximize the value new technology offers.