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Automated invoice data extraction – ML

Real Estate

Machine Learning

Automated extraction of accounting data from invoices using machine learning

2 K

accounting documents processed monthly

30 K

accounting fields automatically extracted per month

2 FTE

reduction in manual processing operations

Challenge

Hundreds of invoices daily, thousands of fields – and fully manual processing

The client’s back office processed over 2 000 accounting documents per month (invoices and credit notes), each requiring manual data extraction and entry into an EDM system. The documents varied in structure, scan quality, and field layout, which ruled out a simple template-based approach.

Key Requirement

Algorithms capable of autonomous adaptation to new invoice patterns – without requiring manual model retraining for each new supplier.

Project

Two parallel streams: ML + EDM integration

The project started with a detailed analysis of processed documents, including invoice types, data structures, scan quality, and input channels. Based on this, two parallel workstreams were launched:

ML model selection and implementation – testing models for autonomous extraction of accounting fields (issue date, sale date, payment date, contractor data, net/gross amounts, VAT rates, currency, bank account numbers, and more).
EDM system integration – adapting the enterprise document management system interface to enable automatic population of fields using extracted data.

The effectiveness of the solution was validated over a two-month production period under expert supervision from designated client-side employees.

Solution

INVOICE Reader – over 85% accuracy from the start

The proprietary INVOICE Reader module developed by Finture achieved a stable accuracy rate of over 85% across all document types and extracted accounting fields, exceeding the client’s baseline requirements already during the validation phase.

Key Decision

The use of ML models based on neural networks and NLP libraries enabled a solution that adapts to new invoice patterns without any engineering intervention – a critical capability given the continuously growing supplier base of the organization.

Results

2 K

accounting documents processed monthly

30 K

accounting fields automatically extracted per month

2 FTE

reduction in manual processing operations

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Client

Capital group in the real estate sector in Poland

Sector

Real Estate

Service

Machine Learning

Technologies

NLP Libraries, Neural Networks

Competencies

Hyperautomation, Digital Transformation

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