This blog outlines best practices in matching policies for vendor invoice processing, considering various factors like vendor characteristics, purchase value, and GL account specifics.
Matching policies are controls put in place to ensure that payments made to vendors are accurate, authorized, and for received goods or services. The most common types of matching include:
Implementing vendor-specific matching policies can streamline AI-led automation and mitigate vendor risks. Below is a table illustrating different scenarios and suggested policies:
VENDOR TYPE | EXAMPLE SCENARIO | SUGGESTED MATCHING POLICY |
Trusted Vendor | Long-term supplier with a consistent delivery record | 2-way matching or manual approvals for transactions under a certain threshold |
New Vendor | Supplier without an established relationship | 3-way matching for all transactions, regardless of size |
High-Risk Vendor | Supplier with previous discrepancies in deliveries | 3-way matching with additional audits for the first few transactions |
Frequent Small Purchases Vendor | Supplier for minor, recurring operational needs | Manual approvals or simplified 2-way matching for efficiency |
The value of the transaction should directly influence the level of scrutiny applied. Here are examples:
TRANSACTION VALUE | EXAMPLE SCENARIO | SUGGESTED MATCHING POLICY |
High-Value | Capital equipment or large service contract | 3-way matching to ensure accuracy and prevent financial discrepancies |
Medium-Value | Office furniture, mid-size projects | Marketing Manager |
Low-Value | Office supplies, minor services | 2-way matching or manual approvals, prioritizing efficiency. This could be Invoice & GRN or Invoice & PO. |
Micro-Transactions | Snacks for office, minor app subscriptions | Manual approvals with periodic review for patterns or policy adjustments. Manual approval authority matrics for such purchases typically can be just 1 or 2 levels. |
The nature of the expense also dictates the appropriate matching policy, as demonstrated in the table below:
GL ACCOUNT TYPE | EXAMPLE SCENARIO | SUGGESTED MATCHING POLICY |
Capital Expenditures | Purchasing new machinery or buildings | 3-way matching to ensure accuracy, given the long-term impact |
Operating Expenses | Monthly utility bills, rent payments Monthly utility bills, rent payments | 2-way matching or manual approvals for regular, expected expenses |
Research and Development | New project development costs | 3-way matching to closely monitor and control investment in innovation |
Marketing and Advertising | Campaigns, promotional materials | 2-way matching, considering the varying scales and flexibility needed |
AI algorithms will automate the extraction of relevant data from purchase orders, invoices, and receipts, regardless of format. AI can match these documents at scale, identifying discrepancies or mismatches between purchase orders, delivery notes, and invoices, thus enforcing the chosen matching policy without manual intervention.
AI systems can learn from historical transactions and adapt to the company’s purchasing patterns over time. This means that the system can identify which vendors or transaction types are more prone to errors and adjust the matching policy level accordingly. For instance, if a certain vendor frequently has discrepancies in invoices, the AI system can flag transactions with this vendor for more detailed reviews.
AI systems offer a high degree of customization, allowing companies to tailor matching policies based on specific criteria, such as vendor category, transaction size, or expense type. This flexibility ensures that the matching process is both efficient and aligned with the company’s risk management strategies.
In conclusion, adopting a strategic approach to matching policies in vendor invoice processing can significantly enhance financial accuracy, improve vendor relationships, and optimize operational efficiency. By considering vendor characteristics, transaction values, and the nature of expenses, businesses can implement a balanced and effective invoice processing system that safeguards against errors while maintaining efficiency in operations.