From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation

TL;DR

This paper introduces UNDRESS, a system that leverages NLP, fuzzy regex, and large language models to enable RPA to effectively extract information from unstructured documents, expanding automation capabilities.

Contribution

The study presents a novel system that enhances RPA by integrating advanced NLP and fuzzy matching techniques for unstructured data retrieval.

Findings

UNDRESS improves text extraction accuracy from unstructured documents.

The system significantly enhances RPA's ability to handle unstructured data.

Evaluation shows increased efficiency in automating processes with unstructured data.

Abstract

The growing volume of unstructured data within organizations poses significant challenges for data analysis and process automation. Unstructured data, which lacks a predefined format, encompasses various forms such as emails, reports, and scans. It is estimated to constitute approximately 80% of enterprise data. Despite the valuable insights it can offer, extracting meaningful information from unstructured data is more complex compared to structured data. Robotic Process Automation (RPA) has gained popularity for automating repetitive tasks, improving efficiency, and reducing errors. However, RPA is traditionally reliant on structured data, limiting its application to processes involving unstructured documents. This study addresses this limitation by developing the UNstructured Document REtrieval SyStem (UNDRESS), a system that uses fuzzy regular expressions, techniques for natural language processing, and large language models to enable RPA platforms to effectively retrieve information from unstructured documents. The research involved the design and development of a prototype system, and its subsequent evaluation based on text extraction and information retrieval performance. The results demonstrate the effectiveness of UNDRESS in enhancing RPA capabilities for unstructured data, providing a significant advancement in the field. The findings suggest that this system could facilitate broader RPA adoption across processes traditionally hindered by unstructured data, thereby improving overall business process efficiency.