Artificial Intelligence

Pros and Cons of Using AI for Accounts Payable Data Extraction

A few years back, when AI made its presence, we were not aware that it would drastically leave a significant impression in all sectors. As its demand rises significantly in the accounting sector (at a CAGR of 33.5% during the forecast period from 2024 to 2029 [Source: Mordor Intelligence]), it is also transforming the way accounts payable departments operate. Advanced AI systems have become an extra hand, allowing businesses to automate tedious and repetitive tasks like data extraction, data input, etc., freeing up human resources for higher-level analysis and decision-making.

But how exactly is AI simplifying data extraction for accounts payable, and what are the implications? How businesses can responsibly use it to achieve better efficiency and growth. Let’s understand these aspects in detail in this blog.

How AI & automation is transforming accounts payable data extraction?

Optical character recognition (OCR) with Machine Learning

Instead of the traditional approach, now organizations utilize OCR with machine learning and advanced algorithms to refine the capabilities of accounts payable data extraction. Cutting-edge AI-powered OCR systems utilize a vast amount of training data to learn and handle variations in invoice data better than traditional OCR systems to collect information more accurately.

Intelligent document processing (IDP)

For complex or varied formats, IDP combines OCR with machine learning models to understand the document type (purchase order, invoice, etc.), structure, and context. This allows extracting data from unstructured documents like free-form invoices, PDFs, and even hand-written ones. After extraction, IDP systems convert the data into structured format, like spreadsheets or databases, making it readily usable for further processing and analysis.

Robotic process automation (RPA)

RPA bots are ideal for automating repetitive and time-consuming tasks such as data collection, data entry, and more. Utilizing rule-based algorithms, these bots can collect required data from specific data points, reducing manual intervention and the chances of errors.

Natural language processing (NLP)

NLP models work beyond basic text recognition to extract more complex data by understanding the context and meaning of terms. They enable you to review documents and identify critical data points (date, vendor name, order amount, etc.) to collect specific information.

Need of AI in data extraction for accounts payable & its benefits

We all know manual accounts payable data extraction is challenging and tedious for growing organizations, which is why more and more people are adopting AI for the rescue. Some of the significant reasons or pros for using AI in data extraction for accounts payable are:

Speed and efficiency

AI models improve the speed and efficiency of the accounts payable department by quickly processing large volumes of documents in various formats. Unlike traditional data collection methods, AI models don’t struggle with unstructured data like free-form descriptions or handwritten notes. They can easily understand the context and extract relevant information even from such data for seamless processing.

Scalability and flexibility

Modern AI systems can be trained on vast data to handle diverse invoice formats, layouts, and even handwritten documents. When you train and integrate such an AI model into your existing AP systems, it can adapt to all the invoice formats you recieve, making the workflow faster and more efficient.

You can further train such an AI model on your data, so they can learn to identify and extract key data points from various invoice formats (PDFs, emails, images). With such custom functionality, you can seamlessly integrate the AI model into an existing AP systems like Xero, Sage, and Zapier. This will help streamline your data extraction workflows for growing volumes, offering seamless scalability & flexibility.

Faster processing time

By automating data collection steps and handling various formats, AI can create streamlined workflows, reducing processing time and delays. Leveraging natural language processing (NLP) and computer vision, AI models can accurately extract data with minimal errors, reducing manual verification and rework. As a result, the risk of duplicate or delayed payments gets significantly reduced. Also, the accounts payable teams get more time to focus on other critical tasks, such as financial analysis, payment processing, managing supplier relationships, and more.

Challenges involved in using AI models for accounts payable data extraction (Cons)

While the pros of implementing AI for AP data extraction are quite evident, here are some challenges or cons associated with it that businesses can’t ignore.

Overhead cost

Advanced AI systems indeed offer promising advancements in Accounts Payable data extraction and automation. However, their hefty price tag and complex integration with existing AP systems can pose a significant challenge for budget-conscious organizations. Evaluating the long-term return on investment (ROI) becomes crucial, but the future benefits may not outweigh the immediate financial constraints.

Additionally, utilizing advanced robotic process automation (RPA) systems for data extraction demands substantial investments in high-end infrastructure, skilled personnel, and extensive training, significantly enhancing the overhead cost.

Data accuracy issues

While the AI models are now utilizing NLP & machine learning capabilities to minimize errors in the collected data, their reliability is still questionable. Data accuracy has always been a primary concern with the adoption of AI, prompting businesses to invest not only in advanced AI systems but also in dedicated human expert teams. These experts can utilize their subject expertise & vast experience to manually review the collected data to identify & fix errors that machines might have missed somehow.

Data security

Data security is another foremost concern associated with the adoption of AI models. Since AP data extraction involves collecting & handling financial information that is sensitive & confidential, businesses can’t rely on systems that don’t have stringent security measures in place. 

Unauthorized access or data breaches are prevalent with AI models, posing a major threat. Additionally, when utilizing AI systems for AP data extraction, it becomes even more critical for organizations to comply with regulations like GDPR, CCPA, etc. to demonstrate responsible use and secure handling of sensitive information.

Lack of required knowledge/skills

To implement and utilize modern AI solutions for AP data extraction, businesses need skilled resources who have prior experience in operating such systems. Without experienced and dedicated resources, businesses can’t harness the true potential of AI models.

Lack of transparency and explainability

AI models often operate as “black boxes,” meaning their decision-making process is opaque and difficult to understand. Without explainability, it’s difficult to trust the results and hold the model accountable for potential biases or errors. This is especially critical in data extraction, where inaccurate or misleading information can have significant consequences.

What are the possible ways for businesses to leverage AI for accounts payable data extraction?

To overcome the above challenges and harness the potential of AI for automated data extraction, here are some cost-effective approaches businesses can try:

Use SaaS (Software as a Service) solutions

Cloud-based platforms: Numerous SaaS platforms utilize AI and Machine Learning for automated invoice processing. These solutions typically charge a monthly subscription fee based on the data volume or features used. Some of the popular cloud-based platforms for AP data extraction are Yooz, Kofax, Tipalti, and MineralTree.

Embedded AI in existing software: Some accounting and enterprise resource planning (ERP) software packages, like SAP and Oracle, now offer built-in AI modules for data extraction. These functionalities can be easily availed by upgrading the existing subscription to premium.

Outsource data extraction services

Several third-party service providers specialize in extracting data from invoices and documents using AI in a blend of human-in-loop approach. They typically charge per document or have pay-as-you-go models. Outsourcing data extraction services to reliable companies can help businesses to:

  • Reducing overhead cost

Hiring skilled data experts for large-scale data extraction and training them can cost organizations more. Outsourcing can offer lower labor costs, especially if you choose providers in regions with lower labor rates like India.

  • Getting more time to focus on core operations

The third-party provider understands your data extraction goals & requirements and dedicatedly handles them to provide you with ready-to-use datasets. This way, the accounts payable team gets more time to invest in other crucial operations such as payment processing, follow-up, financial analysis, and so on.

  • Accessing skilled resources and advanced tools

When you partner with a reliable data extraction company, you get access to a team of experienced data professionals who have hands-on experience with the latest tools. This saves you from investing in advanced infrastructure or resources in-house and proves to be a more cost-effective option.

  • Scaling seamlessly

When you outsource AP data extraction, you have the liberty to scale the team up or down according to your changing business requirements or workload. This is particularly helpful for businesses with fluctuating invoice volumes.

  • Ensuring data accuracy and security

Reliable data extraction service providers leverage advanced AI systems alongside a robust human-in-the-loop approach and stringent security measures. This ensures the accuracy and compliance of the collected data.

Utilize free and open-source platforms

If your data collection requirements are simple, you can consider using open-source libraries and tools like Tesseract OCR that offer basic text extraction capabilities. However, they might require technical expertise for setup and customization and lack advanced AI features. You can get assistance from their active online community, but you cannot expect much from open-source platforms in terms of data security.

Key takeaway

While the AI system holds the potential to automate tedious data extraction tasks in accounts payable, responsible implementation is key. It’s crucial to understand both the benefits and challenges associated with AI and leverage best practices to ensure secure and seamless data capture.

If it is not feasible to invest in advanced AI systems in-house, it is better to consider cost-effective cloud-based solutions. Alternatively, you can choose to outsource data extraction services to a reliable third-party provider who has the required expertise and hands-on experience to work on the latest tools for accurate and efficient data collection. When partnering with a data collection company, consider factors like their proven experience, client reviews, ISO certifications, and client portfolio to make the right choice.