Have you ever felt overwhelmed digging through stacks of documents just to find one specific data? McKinsey found that employees spend nearly 20% of their workweek searching for and collecting internal data. Whether it’s an invoice number, a due date, or a vendor contact, finding the right information can quickly become frustrating when it’s still buried inside piles of unstructured documents.
The most straightforward way to overcome this issue is by moving each data into a spreadsheet or other database system to make it searchable. However, it can prove to be effort-heavy and consume a lot more time than expected.
Fortunately, a solution exists to get this process done faster. Key-value pair (KVP) data extraction offers a more efficient way to collect structured information from documents. This way, you can instantly convert them into easily searchable data while preserving the original context.
In this article, you will explore KVP data extraction and what the process looks like. We will also show you the easy way to automate it with Fintelite AI before we wrap up.
The Concept of KYP Extraction
Identifying key fields within a document, capturing their associated values, and organizing them into a structured format is what key-value pair (KVP) data extraction is all about. At the core of this process is Optical Character Recognition (OCR), which plays a key role in transforming information into machine-readable for accurate data extraction. This method does more than just extracting plain text. It retrieves data while recognizing the link between each data point based on its context, ensuring high data consistency and making the information ready for use.
Let’s see what makes KVP data extraction advance beyond basic text extraction.
KVP Extraction vs Text Extraction
| Aspect | Key-Value Pair Data Extraction | Basic Text Extraction |
| Tujuan Keuangan | Extracts structured data in key-value format | Extracts all visible text from a document |
| Context Awareness | Understands the relationship between labels and attributes | Lack of contextual understanding |
| Output | Structured, machine-readable dataset | Raw, plain text format |
| Data consistency | Ensures consistent field-value pairing | Inconsistent data structure |
| Integration Readiness | Ready for direct integration into databases or ERP systems | Requires additional processing before integration |
| Use Case | Ideal for invoice, receipt, form, bank statement processing | Ideal for full-text documents |
Example of KVP Extraction
To help you understand better, we will examine examples of how KVP extraction works in common business documents such as invoices.
An invoice typically contains transaction details and presents them in a table. KVP extraction detects these fields and matches them with their relevant figures based on context and positioning. For instance, the system identifies “Invoice Number” as a key field and links it to the value “#INV02081.”
It then continues to process other details, such as the invoice date, due date, vendor name, and total amount, in the same manner, as shown below:
| Key | Value |
| Company Name | Stanford Plumbing & Heating |
| Company Address | 123 Madison Drive, Seattle, WA 78290 |
| Company Phone | 990-120-4560 |
| Invoice Date | 11/11/18 |
| Due Date | 12/01/18 |
| Balance Due | $2,844.80 |
Use Case of KVP Extraction
By applying KVP extraction to these use cases, businesses can streamline operations and turn document-heavy processes into fully automated digital workflows.
Accounts Payable
KVP extraction can help with processing data from invoices into a format that can be easily transferred straight to the database system. It automatically captures essential fields such as invoice number, vendor name, due date, and total amount with high accuracy.
Sales Order Processing
Managing order requests requires customer name, item descriptions, quantities, and delivery information. With all the important details from sales order documents automatically captured, businesses can accelerate order validation and fulfillment.
Forms Data Collection
Whether it’s application forms, registration forms, or internal request forms, data can be easily extracted from these table-based documents into a structured format ready for database storage or workflow automation, all without manual transcription.
How to Automate KVP Extraction
Fintelite AI is built as a scalable solution to automate structured data extraction for any document with industry-leading accuracy. Its automation offers flexibility in selecting which data to extract, enabling you to create predefined extraction rules that match your unique requirements. Designed for ease of use, here’s how it works step by step:
Step 1: Document Ingestion
Upload your documents, such as invoices, receipts, forms, or statements. Fintelite AI supports document intake in various formats, including PDFs, images, or scans.
Step 2: Data Extraction
The embedded AI OCR technology begins to identify and extract information in the document body. This ensures that both digital and scanned documents can be processed accurately.
Step 3: Key-Value Matching
The extracted data is then organized based on context, positioning, and document structure, resulting in a structured key-value pair format.
Step 4: Structured Data Output
Once the process is complete, the results are available for review and system integration. You can either export them in formats such as XLS or JSON, or push them to your system via API integration.
Ready to Automate Your Business Documents with Fintelite?
Ready to take the next step toward smarter document automation? Jadwalkan Demo with Fintelite AI and discover how it can be tailored to your specific data extraction needs. See firsthand how the platform adapts to your document formats, integrates with your systems, and helps streamline your workflows.
