By now, most finance teams understand the power of automation. However, many have yet to realize the extent to which artificial intelligence and machine learning can benefit processes.
These financial tools are designed to build value through continuous improvement and focused process optimization, eliminating both fraud and human errors from critical business workflows.
In this article, we’ll take a closer look at the rise of AI and just how artificial intelligence is transforming the end-to-end AP process, including how it’s used, key benefits, and the best software available for automating finance today.
AI is Transforming Accounts Payable
Automation is finance’s greatest asset, and artificial intelligence plays an important role in the digital transformation of accounts payable processes. It introduces automation, machine learning, and complex algorithms to help a business improve accuracy, efficiency, and informed decision-making.
Here are some of the main ways in which AI is impacting accounts payable right now:
Implementing artificial intelligence in accounts payable processes leads to a multitude of savings across the board. The AP department can now take advantage of more early payment discounts, as AI will identify key areas for evaluating cash flow and discount terms.
The rise of robotic process automation (RPA) also works to mechanize labor-intensive systems like document management, filing, data entry, etc. These tools reduce the need for physical storage, manual processes, and human intervention.
AI invoice processing involves advanced technology like Optical Character Recognition (OCR). This is a smart scanner that will automatically extract all relevant data from an invoice, which can include (but is not limited to):
- Invoice number
- Vendor details
- Invoice data
- Line item descriptions
Invoice automation reduces the need for manual data entry, minimizing mistakes, and speeding up processing times.
Automated invoice processing also includes AI-powered workflows for approvals. Invoice AI can route invoices (and other documents) instantly to the appropriate approval channels, based on predefined rules and policies.
Not only does this streamline the entire approval process, but it also reduces bottlenecks and ensures compliance with company policies.
Fraud and Discrepancies
The use cases for AP automation solutions involve fraud detection, prevention, and data validation. AI can validate and reconcile invoice data quickly and easily, against purchase orders and contracts. This helps to identify discrepancies early on in the process, preventing under/overpayments, late payments, and other costly mistakes.
Financial automation software also helps a business detect anomalies in invoice patterns and behaviors. The system will go on to flag any potentially fraudulent activities, and send notifications accordingly.
This helps businesses prevent fake invoices from being processed through fast, automated systems.
Reporting and Analytics
What would the AP function be without advanced reporting and analytics? AI enhances the financial reporting process for a company and helps teams identify inefficiencies and areas for improvement.
Artificial intelligence will also analyze historical invoice data to predict things like:
- Potential cash flow issues
- Vendor behaviors
- Payment trends
This enables a business to optimize payment strategies and make more informed decisions.
Vendor Management and Compliance
AI can assess vendor performance by analyzing key data points, like the quality of goods/services, payment history, and on-time delivery stats. This analysis can then be used to better inform vendor selection and the negotiation process.
Regarding compliance, AI streamlines the entire process and eliminates the headache of noncompliance. The technology can ensure tax compliance with laws and internal financial policies by automatically checking invoices for accuracy and adherence to regulations.
Advancements in finance AI have enabled companies to handle larger volumes of invoices without the need to significantly increase staffing levels. This is a particularly valuable tool for companies looking to scale or grow quickly.
AI-powered dashboards also integrate seamlessly with key enterprise resource planning (ERP) systems, creating a unified ecosystem for financial management.
How Can AI Be Used in Accounts Payable?
Recent studies reveal that almost 70% of AP teams have not automated the most pressing issues in their AP business processes. This includes manual tasks, where it’s acknowledged that someone still needs to look over the invoice and confirm it is correct.
As such, automating these tasks may feel a little disempowering, since you’re essentially trusting a robot to have the same level of discretion as a trained human.
However, manually keying into ERP systems is an underwhelming task at best. Teams should focus on more value-based, innovative jobs.
Here’s how AI can be used in accounts payable:
Coding Invoices and GL Mapping
Some of the most challenging AP tasks to automate are coding and mapping. That’s because multiple GL codes can apply to the same expense, split by line items or individual product codes. This makes assigning these codes a manual job and must be done after consulting with business teams and/or the CFO.
Additionally, assigning codes can be subjective. This can lead to contractual expenses being incorrectly tagged. Artificial intelligence and machine learning are incredible tools for invoice processing, particularly when it comes to assigning the right category and GL code to invoices.
This technology facilitates General Ledger (GL) mapping by remembering a selected code once a user chooses it, then automating the process next time, for the same vendor. The system is technically “learning” your AP team’s preferences when processing invoices.
A business can automate invoice coding based on LLM processing. This means the artificial intelligence tells you which GL the invoice should be categorized in. It can then be customized to offer multiple suggestions.
Fraud and Anomaly Detection
Artificial intelligence is used as a catch-all for fraud detection and reducing errors. It helps to eliminate duplicate invoices and prevents most problems before they happen.
The system will automatically identify anomalies in invoice data and payment patterns. It will then flag any potential errors or fraud for further investigation.
Advanced AI tools now carry out fully-automated data validation processes to ensure data hygiene and exercise smarter duplicate detection. It goes a bit further than simply verifying if two files are the same. It will also check if the contents of two files are suspiciously similar.
AI algorithms will also validate extracted invoice data against a variety of business documents, purchase orders, contracts, and historical data. This ensures the invoice data matches all quantities, pricing, and expected terms.
Learning Patterns and Trends
The real value of AI is realized when it can recognize trends and learn from patterns. Accounting automation can manage the small tasks, performed multiple times, for various invoices and receipts.
Some of these smaller tasks include:
- Sending a specific vendor’s invoice to a certain approver, every time
- Assigning an invoice to the right category/project/class in the ERP
- Changing the GL mapping for one specific line item on an invoice
- Detecting duplicates by comparing invoices to existing records
The first step is finding the tasks ideally suited for iterated re-learning. This means the activities your business performs on a daily basis, over and over again. Some examples where AP automation machine learning works best include GL code assignments and category/class/project classifications.
In-Depth Reporting and Predictive Analytics
Artificial intelligence can determine vendor performance by analyzing delivery times, payment history, and qualitative reports. AI-generated reports provide greater insights into the accounts payable process, helping companies identify top trends, potential areas for cost savings, and opportunities for improvement.
AI will also optimize cash flow by suggesting optimal payment schedules based on available cash flow. This ensures bills are always paid on time, while maintaining healthy cash reserves.
Automation technology also assists with predictive analytics, analyzing historical data to better identify cash flow trends, payment patterns, and potential gaps. This leads to better financial planning and more efficient growth strategies.
Machine Learning in Accounts Payable
Machine learning is a subset of artificial intelligence that involves processing massive amounts of data and understanding the important patterns behind it. Machine learning invoice processing is just one way in which a business benefits from the technology.
Here are some of the most significant advantages, broken into simple concepts:
Accounts payable automation paired with machine learning largely eliminates humans from the equation, leading to fewer human errors and a higher rate of accuracy.
Sophisticated software platforms offer far greater precision, with features like:
- Data Validation and Matching: Machine learning quickly validates invoice data against important documents, automatically flagging discrepancies for review.
- Invoice Data Extraction: Algorithms working with OCR technology accurately extract invoice data and categorize it accordingly.
- Anomaly Detection: AI models easily identify anomalies in invoice data and payment patterns, helping to detect potential errors, duplicates, and fraud.
Automation working in conjunction with machine learning, can process invoices at a rapid pace (much faster than a human). There is no risk of slowing down or tiring, with these machines capable of operating 24/7. This translates into shorter turnaround times and faster approvals.
Machine learning can automatically assign expense categories and GL codes to invoices based on pre-defined rules and historical patterns. This optimizes the coding process and reduces manual effort.
Systems can also take it a step further and analyze past invoice approval patterns to predict the appropriate routing for future invoices. This reduces processing delays and ensures invoices reach the right approvers quickly.
Machine learning brings in major cost savings by increasing productivity, with a low cost for implementation. This type of software also gradually improves efficiency over time, which cuts costs even further.
AI tools with machine learning will also analyze historical payment data and vendor terms to recommend the best payment schedules for early payment discounts, while simultaneously managing cash flow.
Machine learning in AP leads to more control. Models can analyze historical data to predict cash flow, payment patterns, and potential gaps, enabling better financial management and planning.
ML-powered accounting software will collect and generate useful data, centralizing information in a way that makes it easier to manage. This means greater control over cash flow and the ability to leverage data in more meaningful ways.
Machine learning empowers finance teams to automate repetitive tasks, reduce errors, and make more accurate, data-driven decisions. As ML models learn from more data, they increase in accuracy and efficiency, contributing to improved financial management and streamlined operations.
ML is a tool for future-proofing, with the ability to continuously learn and adapt to new patterns. AI creates a cohesive environment for all of your accounts payable processes, integrating seamlessly with ERP systems. It also helps to assess vendor performance by analyzing delivery times, payment history, and the quality of products or services.
How to Embrace AI in Your Accounts Payable
AI-powered AP automation involves careful planning, monitoring, and implementation. Here are a few steps to consider when embracing AI in your accounts payable:
Step #1) Planning
This first phase is to assess business needs, determining the specific challenges and bottlenecks in your accounts payable process. This can include tasks like invoice validation, manual data entry, approval delays, etc.
Clearly define objectives, whether that’s improving accuracy, reducing processing times, or enhancing decision-making. Make sure you research AI and ML-powered solutions that align with these goals, considering factors like integration, scalability, and user-friendliness.
Step #2) Prepare the Data
The next step is to gather all the data needed to make the most informed decision. Collect historical data that includes invoices, payment records, purchase orders, and vendor information. Just remember, the quality of the data is absolutely crucial for training AI models effectively.
Data must be accurate and consistent. Avoid issues by cleansing and organizing information (including removing duplicates) that may negatively impact AI performance.
Step #3) Choosing the Software
The next step is to select a reputable AI and ML solution that offers tools tailored to your AP processes. The vendor should have a track record of successful implementations and should be able to customize features according to your business needs and workflows.
Step #4) Implementing the Solution
Now it’s time to integrate the AI solution with your existing ERP and/or other financial systems. This process should be seamless, with data flowing smoothly. Otherwise, the software may not be a good fit. It could end up costing a lot more if you have to replace legacy systems. Thus the importance of due diligence upfront.
Test the AI solution thoroughly. Use a subset of test data to ensure it’s functioning as expected. Prior to scaling up, address any discrepancies or issues. This will help you avoid costly mistakes.
Step #5) Training and Optimization
If the AI solution you choose involves machine learning, the next step is to train algorithms using your prepared data.
This may involve training systems to recognize vendor patterns, invoice formats, approval workflows, etc. Continuously monitor the AI’s performance and fine-tune algorithms as needed.
Step #6) Change Management
The last phase is employee training and change management. Training should be provided to all finance teams, reviewing how to use the AI tools effectively.
This is the time to address any concerns, issues, or misconceptions staff may have about implementing the new software. Always emphasize that AI is not meant to replace anyone but to enhance existing operations.
After implementation, it’s important to set some key performance indicators (KPIs) to continuously monitor the success of the software. Metrics might include processing times, cost savings, and accuracy improvement.
An organization should always remain open to adapting processes based on insights. As technology evolves, so should the way in which we do business.
Software with AI and ML Capabilities
Tipalti PI is one software solution that offers both artificial intelligence and machine learning capabilities. The next-generation, AI-powered payables intelligence engine is designed to revolutionize payables while reducing manual workloads and increasing productivity.
Expect these types of features from software that offers both AI and ML capabilities:
Auto Coding and Intelligent Invoice Management
The Tipalti Pi Auto Coding feature leverages AI and machine learning to predict the correct coding on purchase orders and invoices. Not only does this replace the need for manual coding, it speeds up processing and improves spend visibility.
Instant Spend Analytics with a Digital Assistant
Ask Pi is Tipalti’s AI-powered digital assistant that can provide instant access to critical business data. The Ask Pi bot answers your complex questions quickly, without the need to navigate through layers of interfaces or conduct lengthy analysis. This helps an organization make more informed decisions instantly.
Optimize Payables Workflow
Tipalti PI helps to support employees through digital transformation by ensuring efficient payables processing. It automates complex tasks, allowing your finance team to focus on more strategic initiatives.
Tipalti offers accounts payable machine learning tools that are built to grow and adapt to your business. As a company evolves, so will Tiaplti Pi, continuously utilizing procurement and payables data to further automate workflows and streamline financial processes.
The Future of AI and ML in Accounts Payable
So, what’s next in the ever-evolving landscape of financial technology? Artificial intelligence and machine learning in accounts payable open the door to endless opportunities for automation. This finance automation guide is just the tip of the iceberg.
The future promises greater accuracy, transparency, and strategic value for businesses. Here are just some of the trends and possibilities shaping the future of AI and ML in accounts payable:
While advanced AI and ML will continue to drive tasks for invoice processing, data extraction, coding, and approvals, the technology will evolve into thinking more for itself.
AI solutions handle complex, time-consuming tasks, like understanding and processing unstructured data, handling exceptions, and making nuanced decisions that are based on context.
This enables real-time data processing, improving responsiveness and analyzing a wider range of data sets.
Natural Language Processing (NLP)
NLP enables AI systems to better understand and process human language, making communication with AP systems more intuitive. This enhances collaboration and engagement between users and their fintech tools.
AI and ML models continue to become more sophisticated in predicting payment patterns, cash flow trends, and vendor behavior. This type of insight enables businesses to better optimize their payment strategies and budgeting.
Blockchain and Smart Contracts
AI and blockchain technology can enhance security and transparency in accounts payable processes. Smart contracts automate certain parts of the payment workflow, and blockchain provides a secure, tamper-proof ledger of financial transactions.
AI and ML tools now incorporate external data from the Internet of Things (IoT) and supply chain systems. This marrying of datasets provides a holistic view of the entire payment process, allowing for more accurate planning and forecasting.
Environmental, Social, and Governance (ESG) considerations are becoming an integrated part of financial processes. There is a growing need to address ethical considerations, data privacy, and compliance with financial regulations.
Summing it Up
The root of success for AI and ML-based systems is continuous learning and adaptation. The best AI software functions independently, learning from new data and adapting to changing patterns to improve performance, over time, without significant human intervention.
The future of artificial intelligence in accounts payable promises a more efficient, intelligent, and strategic approach to financial management, allowing organizations to focus on higher-value decisions while leaving the routine processes to robots.
The data housed in your finance department is incredibly valuable. Learn more about how to build a robust finance tech stack today.