AI in account payable: changing the conventional way of extracting data from invoices
In today’s digital era, the convergence of AI and data extraction has revolutionized account payables, offering innovative solutions for streamlined operations. Manual methods are inefficient and prone to errors, but AI technology has empowered organizations to unlock tremendous potential.
AI’s ability to analyze vast data, recognize patterns, and learn from experience has transformed data extraction. Through sophisticated algorithms, machine learning, and natural language processing, AI solutions swiftly and accurately extract crucial information from invoices, receipts, and other documents.
The impact of AI-powered data extraction in account payables is significant:
- Enhances operational efficiency by automating repetitive tasks, reducing errors, and increasing productivity.
- Provides valuable insights for informed decision-making, identifying trends, cost savings, and process optimization.
- Seamlessly integrates with existing workflows, such as ERP systems and OCR technologies, for minimal disruption and maximum efficiency.
As data volume grows, the importance of AI in account payables extraction intensifies. AI automation and accuracy save time and resources, enabling human capital to focus on critical thinking and creativity. Stay ahead by leveraging AI to drive growth, enhance financial performance, and harness the power of data.
If is not AI what is it?
Manual data extraction
Despite advancements in technology, some companies continue to rely on manual data extraction methods when processing invoices received from suppliers. In this panorama, once the invoice-to-pay is received, the relevant information is manually identified and extracted from the invoice. This involves reading the invoice and manually inputting the data into the appropriate fields in the accounting system or accounts payable software. It may require navigating through various screens or forms to accurately record the information.
On the flip side, numerous companies have embraced the OCR system as a means of extracting data from their invoices.
Optical Character Recognition (OCR) is a technology that enables the conversion of scanned or photographed documents into editable and searchable text. It works by analyzing the visual patterns of characters in an image and translating them into machine-readable text.
In the context of account payables and invoices management, OCR enables data extraction from invoices. When an invoice is received, OCR software is used to scan and extract relevant information such as vendor name, invoice number, date, line items, and amounts. This extracted data is then converted into a structured format that can be easily processed and integrated into accounting systems or accounts payable software.
OCR technology uses advanced algorithms to recognize characters and patterns within the image. It can handle different fonts, sizes, and languages, making it versatile for invoice processing in multilingual and multinational environments. OCR eliminates the need for manual data entry, significantly reducing human error and saving time in the accounts payable process.
But, what are the drawbacks of this technology?
Here are the four main drawbacks of OCR technology:
- Accuracy Issues: OCR systems may encounter challenges in accurately recognizing and interpreting characters, leading to errors in data extraction. Poor-quality scans, complex document layouts, or handwritten text can contribute to reduced accuracy.
- Formatting Challenges: OCR may struggle with preserving the original formatting of the document during the extraction process. This can result in the loss of structural elements or the misinterpretation of data, particularly in documents with complex layouts or non-standard formats.
- Language Limitations: OCR systems may have difficulty recognizing and extracting text from languages or scripts that are not well-supported by the software. Multilingual environments or documents with non-standard characters can pose challenges for OCR technology.
- Dependency on Document Quality: OCR performance relies heavily on the quality of the input documents. Faded or smudged text, distorted images, or poor-quality scans can significantly impact the accuracy and reliability of OCR results.
Is AI able to overcome these limitations?
Yes, of course!… Let’s see how:
Data extraction with AI
Data extraction with AI refers to the process of automatically retrieving and interpreting information from various sources using artificial intelligence techniques. It involves extracting structured or unstructured data from different formats such as text documents, images, websites, or databases, and converting it into a usable and organized format for further analysis.
In the field of accounts payable, data extraction with AI plays a vital role in automating and streamlining invoice processing, payment reconciliation, and overall financial operations. AI can be used to automatically extract key information from invoices, such as vendor details, invoice numbers, dates, line items, and amounts: AI-based technologies make use of machine learning algorithms for accurately extracting data from scanned or digital invoices, eliminating the need for manual data entry.
So, What’s the difference between AI and OCR data extraction?
Here’s a detailed differentiation (Yes, we already talked about this!) between vendor invoice data extraction using OCR (Optical Character Recognition) and AI (Artificial Intelligence):
OCR technology relies on pattern recognition and optical scanning to extract text from invoices. While it can achieve reasonably accurate results under controlled conditions, OCR may struggle with variations in invoice layouts, fonts, or the quality of scanned documents. AI, on the other hand, leverages advanced techniques like machine learning and natural language processing, allowing it to adapt and improve accuracy over time. AI models can learn from vast amounts of data, handle diverse invoice formats, and handle complex information extraction tasks more effectively.
OCR primarily focuses on extracting text characters from images or scanned documents. It provides basic text recognition capabilities but lacks a deeper understanding of the content. In contrast, AI models can employ NLP algorithms to comprehend the context, meaning, and relationships between different data elements within invoices. AI can extract structured information such as vendor names, invoice numbers, dates, line items, and amounts, as well as interpret unstructured data like item descriptions or terms and conditions.
OCR-based extraction may produce errors or inaccuracies due to limitations in recognizing and validating extracted data. AI-based approaches can incorporate validation checks and sophisticated algorithms to verify the extracted information against existing databases, historical data, or predefined rules. AI can automatically flag and handle exceptions, such as missing or mismatched data, reducing manual intervention and improving overall data quality.
OCR systems typically require manual configuration and setup to accommodate changes in invoice templates or formats. When faced with new or modified invoice layouts, OCR may struggle to accurately extract data without reconfiguration. In contrast, AI models can be trained to adapt and generalize to new invoice layouts and variations, reducing the need for constant manual intervention and allowing for more flexibility as businesses encounter evolving invoice formats.
OCR-based systems often require manual data verification and correction, resulting in time-consuming and error-prone manual interventions. AI-powered data extraction offers higher levels of automation, reducing manual effort and streamlining invoice processing workflows. By automating data extraction and validation, AI significantly improves processing speed, efficiency, and scalability, enabling organizations to handle larger volumes of invoices with fewer resources.
AI-based data extraction not only provides accurate and structured invoice data but also unlocks the potential for advanced analytics and reporting. With AI, organizations can analyze invoice data in real time, identify patterns, track key performance indicators, detect trends, and generate actionable insights. These capabilities enable businesses to make data-driven decisions, optimize financial operations, and drive continuous process improvement.
Overall, while OCR is a valuable technology for basic text recognition, AI-based data extraction offers superior accuracy, flexibility, adaptability, automation, error detection, and advanced analytics capabilities. It overcomes OCR’s limitations by providing more comprehensive invoice data extraction and interpretation, resulting in improved efficiency, accuracy, and operational effectiveness in managing accounts payable processes.
If you are interested in learning more about how Dost leverages AI for extracting and processing information from vendors’ invoices, check out our academy showing how the Dost solution works!
Moreover, in this video, you can see how Dost extracts and processes data!