From Ok Google, Alexa to chatbots, Artificial Intelligence (AI) is simplifying our lives, making everything smarter, better, and quicker. But, has AI disrupted your accounts payable processes? Yes, definitely !! Automated Invoice processing using AI is a very fascinating area in the accounts payable process with significant benefits. This is because invoice processing automation would free up back-office finance/procurement teams to focus on higher value-added tasks. Let’s understand how artificial intelligence can help automate invoice processing for your vendors on the ServiceNow platform as well as how AI-enabled cognitive invoice data extraction can transform Accounts payable processes for your organization.
Current Challenges in Invoice Processing
Multiple invoice data points leading to errors: Large organizations receive invoices from multiple suppliers through multiple channels as structured XML documents from Electronic Data Interchange (EDI), PDFs, and image files via email, and increasingly rarely as hard copy documents. It takes a lot of time and manual effort to have all these different types of invoices into the unified system. The error-prone data entry happening at the start of the invoice processing workflow can further lead to possible mistakes such as paying the same invoice twice, paying incorrect amounts, duplication of invoices, etc.
Data mismatch: The accounts payable team has to perform two-way or three-way invoice data matching with a purchase order (PO), goods received note (GRN), and contracts data using “stare and compare”. Now imagine when the same action is repeated hundreds of times per day, how much your team wastes time and money. Also, even the most careful human eye cannot guarantee accuracy, as the team might miss dates, values, or formulas that can ultimately slow down the entire department, lead to overpayments and expose the organization to a variety of risks.
Cost per invoice processing: The ongoing invoice processing process is largely manual with various costs involved like: manual hours, paper, postage, and others putting the real price of manual invoice processing between $12 and $30 per invoice. Apart from this, there are also other hidden costs, like missed early payment discounts, late fees, and accounting errors.
Types of Invoice Data Capture Solutions
Template-based invoice OCR (Optical Character Recognition) software: OCR-based invoice data capture solution helps you convert images into text that the accounts team can work with. However, OCR alone is not capable of processing invoices on its own. AP team needs to feed invoice templates and rules for every new invoice type your company receives.
AI-enabled cognitive invoice capture software: Invoice capture solutions capture key-value pairs and tables which are required to auto process invoices and it mimics the human mind when detecting and capturing document data. It then gives human operators intuitive assistance, enabling them to validate and correct captured data in seconds. This solution can be deployed on the cloud, ensuring best-in-class security and scalability, as well as full 24/7 access from any device, giving greater operational efficiency to global accounts payable teams.
AI-enabled Cognitive Invoice Processing in Action
AI can automate the invoice capture and processing using the following steps :
- Extract key-values (e.g. bank account, ordered item)from the hard copy or image invoice. If the extraction process does not achieve the required confidence-level in the results, it is sent to employees for a manual check.
- Cross-check the invoice information with PO, GRNs, and contract data using 2-way/3-way validation.
- Automatically inserts invoice information into the unified system and classifies the invoices by the cost centers, business units or product categories.
- Automated approvals using advanced workflow conditions to settle the invoice.
Benefits of AI-powered Automated Invoice Processing
- Faster Invoice Processing: Consider that, for one invoice, manual data extraction took over three and a half minutes, while AI-enabled extraction took just under 27 seconds. Future invoices from the same supplier will require even less time to process, as the platform now recognizes it and no longer requires human validation. The solution gets smarter with every new invoice it processes; therefore, our example company will eventually have the option of fully automated invoice processing.
- Enhanced Data Accuracy: AI and Machine Learning detects and captures invoice data using neural networks to increase its understanding and capabilities with every document it processes. Unlike a human data entry clerk, smart invoice processing software does not come with the risk of making errors due to, for instance, the fatigue of carrying out a dull repetitive task.
- Increased Productivity: With AI & Machine Learning taking care of the manual tasks, your Accounts Payable team can shift their focus on value-generating activities, such as
- Financial planning
- Tracking company spend
- Deriving actionable insights from analytics
- Collaborating with other corporate functions, such as Procurement
- Strengthening vendor-customer relationships
- Improving Bottom-Line: Greater financial impact is seen with on-time invoice approval leading to early payment discounts from suppliers, increasing profit margins.
AI-enabled invoice data capture on ServiceNow
Many enterprises have started their digital transformation journey using the ServiceNow platform, which offers out-of-the-box solutions to digitize many enterprise processes. For invoice processing, your organization can leverage Invoiceflow which provides a unique zero-touch invoice processing solution covering intelligent invoice data extraction, AI-based 2-way/3-way invoice validation with flexible invoice processing workflows.
Aavenir’s Invoiceflow delivers faster invoice processing with fewer touchpoints, fewer errors, and more early payment discounts, thus improving the vendor invoice processing. Get your free trial of Invoiceflow accounts payable automation from the ServiceNow App Store
