The integration of Machine Learning (ML) and other intelligent technologies into financial management is revolutionizing the way finance teams operate.
While basic automation has streamlined repetitive tasks, Machine Learning, combined with artificial intelligence (AI) offers a more dynamic and adaptable solution.
In areas such as Accounts Payable (AP) management, ML is transforming complex functions, particularly in invoice matching, verification and cost coding, enabling finance teams to operate more accurately, efficiently and strategically.
Intelligent Financial Management: a step beyond automation
Accounting departments use automation to handle routine financial tasks, reducing manual labor and errors. However, it lacks the flexibility needed in today’s fast-paced business environment.
Intelligent systems, powered by ML and AI, go beyond simple automation by learning from past experiences, adapting to real-time changes, and supporting data-driven decisions.
This shift allows finance teams to move from automating processes to optimizing them and then leveraging intelligent insights.
Machine Learning in AP management: matching, verification and cost coding
In AP management, two critical functions—matching and cost coding—have traditionally been time-consuming and prone to error.
Machine Learning addresses these challenges by improving both speed and accuracy.
Matching invoices with Purchase Orders (POs)
One of the most significant applications of ML in AP management is in invoice matching.
Machine Learning algorithms can automatically compare invoices with corresponding purchase orders and receipts.
This process, which used to require manual participation, can now be handled digitally with high accuracy and speed, limiting the need for human intervention to specific instances requiring resolution, and to general oversight.
By identifying discrepancies, inaccuracies, or missing information in real time, ML allows finance teams to focus on resolving issues and to do so more rapidly and efficiently.
Matching invoices with Delivery Notes (DN)
Machine Learning also plays a crucial role in automating the process of matching invoices with corresponding delivery notes.
Traditionally, this task required manual cross-referencing to ensure that goods received match the billed items.
ML-enabled systems quickly and accurately verify that the invoiced elements match the items, quantities, and other details on the delivery note.
This automation reduces errors, accelerates and simplifies processing, and minimizes the need for manual checks, freeing finance teams to concentrate on resolving discrepancies and focus on value-added tasks.
Verification process
Machine Learning significantly enhances the verification process in AP management. Verifying invoice details such as vendor information, quantities, pricing, and terms typically involves extensive manual review.
Using ML enables these checks to be automated to ensure that invoices comply with contractual agreements and internal policies.
The system can quickly identify discrepancies, flagging issues like price discrepancies, duplicate charges, or incorrect quantities for manual review.
In this way, ML-powered automation reduces processing time and improves accuracy. Finance teams can therefore focus on exception resolution and compliance issues, benefitting from the streamlining of the entire digitalized verification workflow.
Cost coding automation
Another crucial application is in cost coding, where expenses are assigned to specific accounts or departments.
Typically, AP staff review each invoice and assign cost-centre codes according to predefined company rules.
ML allows systems to learn these rules and processes over time and/or through instruction, enabling the correct cost codes to be automatically assigned to each invoice.
Moreover, as the system is exposed to more data, it becomes increasingly accurate — even in handling complex or ambiguously detailed invoices.
Real-time adaptation and strategic benefits
What makes ML particularly valuable in AP management is its ability to adapt in real time. As new data becomes available, the system learns and improves, optimizing financial processes, improving accuracy and facilitating compliance with regard to expenditure and budgeting.
By continually monitoring and adjusting, ML systems help finance teams remain agile and respond swiftly to changing market conditions and business needs.
The use of intelligent systems in AP management saves time and releases short-staffed finance teams from necessary drudgery. Instead of spending valuable time on routine yet indispensible matching, verification and cost coding tasks, finance professionals can devote their energies, qualifications and efforts to oversight, forecasting, scenario planning, budgeting, and decision-making, driving long-term value for the organization.
The future of financial management
Comply has been developed to apply AI and ML to empower companies to easily digitalize their critical accounting functions such as AP management.
When used strategically, these technologies enable critical processes to be reliably simplified and automated and to monitor and control costs and cashflow in real time, helping the company to achieve sustainable competitive advantage.
Until now, many companies have hesitated to go all-in on adopting these technologies to benefit their businesses largely due to the expense, the unavoidably painful change management process, and potential staff churn.
However, accelerated technological and infrastructural developments now allow Software as a Service (SaaS) providers to offer a simple, easy and flexible way for companies to gain all the benefits and competitive advantages they offer without the associated pains of change management — and in so doing also allow those companies to offer more stimulating job prospects to existing and potential finance team members.
