December 2, 2024
The processing of incoming invoices is a key financial management process for enterprises, requiring significant time investment and being vulnerable to fraud. Issues such as duplicate invoices, deliberate or accidental errors, and inflated costs of goods and services are among the most common ways of illegally obtaining funds.
One way to optimize this process is through the automation of payment document processing, which helps reduce document processing costs and minimize risks associated with fraud.
What Tools Can Optimize the Process?
1. Invoice Processing Using OCR
Optical Character Recognition (OCR) allows computers to “read” text from images or scanned documents, converting printed and then scanned characters into editable and processable text on a computer. The main advantage of OCR is the elimination of manual data entry.
The OCR processing workflow includes:
- Scanning and uploading. Documents are scanned and uploaded digitally into the software.
- Image processing, including enhancing the quality of the scanned image, such as removing noise, correcting rotation, and more.
- Text recognition – converting characters in the document into machine-readable text.
However, OCR has several limitations:
- Missed or misread letters and numbers, especially symbols;
- Confusion between similar-looking letters or numbers, e.g., O and 0, I and 1, or B and 8;
- Incorrect recognition due to low resolution, lighting, or contrast quality.
While OCR offers new opportunities for simplifying invoice processing, it does not fully automate the process, including data interpretation, document verification, and identifying potential errors and anomalies. Artificial Intelligence can complement OCR in analyzing the information contained in invoices.
2. Invoice Processing Using Artificial Intelligence
The combination of OCR and Artificial Intelligence (AI) can significantly enhance the processing workflow by adding a layer of analysis and verification. AI-based systems can analyze content and detect anomalies using machine learning algorithms to recognize patterns and behaviors. For example, AI can:
- Interpret the data recognized on documents. For instance, identifying:
- Dates;
- Bank details and their components;
- Amounts;
- Counterparty data;
- Numbers, etc.
- Analyze data and identify:
- Errors;
- Anomalies;
- Signs of fraud and illegal fund acquisition.
The combination of OCR and AI creates new opportunities for automating invoice processing, reducing manual labor, and minimizing the risk of erroneous payments. This allows for less time spent on processing, thereby speeding up the workflow while improving control over financial flows, ensuring confidence in the accuracy and reliability of payments.
TEpol Soft team has developed a solution that combines OCR and AI. More details about the program can be found at request.