The bedrock of financial validation processes is accurate matching, and intelligent automation and AI are making this process almost painless.
Imagine a bustling restaurant on a Friday night. The kitchen is a flurry of activity, with chefs expertly crafting dishes at lightning speed.
What happens if discrepancies arise between the restaurant’s orders and the supplier’s invoices? For instance, if the restaurant receives a higher quantity of products than what was originally ordered but pays the invoice based on the initial order, it could result in payment discrepancies, ultimately leading to strained vendor relationships. Conversely, if the restaurant receives fewer products than was invoiced, they may end up with stock shortages, affecting their ability to meet customer demand and leading to financial implications, operational chaos, and even business disruption.
The key to averting these basic transactional problems in Accounts Payable (AP) is ‘accurate matching’ of transaction documents. Traditionally, a good system involves a three-way matching process, which compares an invoice to the purchase order, and then ensures that the purchased product was actually delivered, via an order receipt or packaging slip. However, the process is labor-intensive, error-prone, complex, not always practicable, and could involve fraud or disruptions.
To that end, AI has made the ‘accurate matching’ process more efficient than ever. Jaya Mahajanam, Director (Solutions), E42, speaks with DigiconAsia.net to gather some of her industry insights.
DigiconAsia: In today’s digitalization urgency for the financial industry, what are the strengths of automation matching in AP?
Jaya Mahajanam (JM): In the world of AP, accurate matching of documents is the bedrock, as it ensures that every financial transaction aligns with the corresponding documentation. The consequences of mismatches are far-reaching, leading to overpayments, underpayments, strained vendor relationships, and draining resources. These errors can disrupt the supply chain and erode the financial health of an organization.
AI can be used to ensure the accurate matching of documents. Automated matching, the technical backbone of AP, operates on a multifaceted process that involves data extraction, comparison, and automated alerts:
- Data extraction: At the heart of the process is the AI-based extraction of data from invoices, purchase orders, and receipts. Automated systems employ advanced Intelligent Character Recognition (ICR) and Natural Language Processing technologies (NLP). ICR converts text from both scanned documents and handwritten documents into machine-readable text, while NLP discerns the context of this text. Together, they form a powerful duo that extracts critical data with high precision.
- Comparison: Once data is extracted, automated systems employ complex algorithms for comparison. These algorithms are designed to meticulously scrutinize the data and identify matches and discrepancies. The level of precision offered by these algorithms ensures that even the subtlest differences are detected.
- Automated alerts: In the event of a discrepancy, the system generates automated alerts. These alerts are immediate and precise, allowing for swift resolution. The beauty of this automated process is that errors are intercepted before they lead to erroneous payments, saving time and resources.
In automated matching, data is meticulously cross-referenced against predefined criteria, including business rules, accounting codes, and internal policies. This meticulous financial validation process ensures the accuracy and compliance of all financial transactions with the organization’s specific requirements, at machine speed.
Automated matching systems go beyond simple two-way matches, and can conduct complex n-way matches involving invoices, purchase orders and receipts across multiple documents, enhancing transaction accountability.
Furthermore, these systems facilitate communication with vendors, automatically generating reconfirmation requests when discrepancies are detected, expediting issue resolution, and nurturing transparent vendor relationships.
In today’s regulatory landscape, automated matching extends to cross-referencing transaction data with government portals, ensuring adherence to tax regulations and legal requirements, ultimately safeguarding against penalties, and upholding regulatory compliance.
DigiconAsia: How is AI impacting automated matching systems?
JM: AI has been used in Cognitive Process Automation (CPA) in automated matching within AP workflows. By integrating advanced technologies like machine learning and natural language processing, CPA enhances the accuracy and efficiency of data extraction and comparison.
CPA also empowers automated systems to not only recognize patterns and match data with high precision but also adapt and improve performance over time. With CPA, organizations can achieve a level of sophistication in data matching that was previously unattainable, ensuring that financial transactions align seamlessly with the organization’s specific criteria.
In the financial heartbeat of various industries, the role of accurate data matching in AP is paramount, and automation and CPA are solving AP issues in many sectors:
- In retail chains, it is the watchdog ensuring that quantity and quality of received products tally with instructions on orders and invoices, eliminating overpayments and payment delays.
- For manufacturers, it is the precision tool verifying the alignment of raw materials with orders, thereby maintaining production accuracy.
- In the hospitality sector, it is the quality check confirming that received supplies meet the set standards, averting financial losses.
- For tech firms, it is the critical eye that confirms whether the developed code meets contract stipulations.
The magic of automated matching in AP processing lies in its function to facilitate error prevention, improved accuracy, and streamlined financial operations. Its data extraction, comparison, and automated alert features serve as a technical marvel that optimizes the financial health of organizations.
Moreover, automated matching is not a solitary process; it serves as the precursor to validation, conducts two-way and n-way matches, reconfirms with vendors, and ensures compliance with government regulations.
As businesses navigate the complex landscape of modern finance, automated matching — especially when supercharged with AI — stands as a beacon of precision and efficiency, paving the way for a future where financial operations are conducted with unprecedented accuracy and ease.
DigiconAsia thanks Jaya for sharing her AP automation insights with readers.
