Automation of related financial document processing operations.

• ChallengeOptimize document workflow with the aid of machine learning
• SolutionData capture and ML consulting services to map out the way to a custom ML-powered financial solution
• Technologies and toolsTesseract, Python data science stack

Client

The Client is a provider of custom software solutions for different business niches. The Client’s company was looking for a reliable machine learning (ML) consulting firm to assist with implementing ML into a custom intelligent document recognition solution.

The system was needed for automatic invoice and tax returns recognition. The solution was expected to enable the automation of related tasks of human employees engaged in working with financial documents and critical data. Likewise, it was expected to facilitate dealing with monotonous tasks.

Challenge: optimize document workflow with the aid of machine learning

The Client was looking for the aid of expert consultants on the right methods of implementing ML into the existing solution. The company needed to effectively recognize and digitize paper documents for further uses, expedite paper document workflow, and processes of data extraction and classification.

The following parts of the Client’s document processing system required an ML boost:

As an automated data capture solutions provider, InData Labs acted as an expert consultant helping to solve some of the most complex tasks. We proposed the Client a method of integrating ML into the existing system to better serve the business needs.

Solution: data capture and ML consulting services to map out the way to a custom ML-powered financial solution

Our consultants revised the existing solution and proposed the following changes and approaches:

• OCR tax analysis. We proposed to use an open-source tool Tesseract or better ABBYY software as an alternative. High-quality OCR software is the core part of the system since it ensures an effective process of recognizing tax forms and invoices and more accurate output.

We offered the development of a combined system to enable the following capabilities after the OCR stage is finished:

• Employ a rule-based approach to extract necessary fields without using ML (for instance, extract data from IBAN)
• In the extracted text, use ML to classify words, keywords, phrases, symbols, and other elements into separate classes, such as total, goods, date, sender, recipient, etc. (we described the data markup scheme, as well).

Also, we suggested replacing an open-source Tesseract with another solution that would yield better recognition output. We offered to combine a template approach used by the Client before with ML algorithms.

Our expert team provided consulting services on improving the existing solution for processing corporate documents of different types. We resulted in providing a viable roadmap to success.

 Result: a viable roadmap to success in using ML for data capture

InData Labs consulted the Client on the possible enhancement of the existing document processing software. Our team offered methodologies for the Client to acquire an OCR software for recognition and processing of invoices and tax returns with the use of machine learning.

The solution can help to automate related financial document processing operations, upgrade customer experience, and promote efficiency growth.

Our consulting on building and OCR tax recognition and processing app provided the Client with necessary assistance in solving complicated ML implementation issues. We have worked out a guide on building an ML-led data capture solution in line with specific needs and expectations.