How Machine Learning Enhances Data Extraction Accuracy

Extracting data from documents can be time-consuming if left entirely in human hands. For example, in accounting departments, much of an employee’s day may revolve solely around extracting data from invoices and manually entering it into other business software systems. However, this approach is costly due to the turnaround times involved and is prone to errors. Machine learning changes that.

With Tungsten Automation solutions such as AP Essentials, companies can harness the power of Machine Learning (ML) to accelerate and improve the data extraction process. How does ML make such a difference? Multiple elements make this technology a potent force for workflow automation—here’s what makes the difference.

Enhanced Outcomes for Optical Character Recognition

With machine learning, OCR outcomes can improve dramatically. Automated noise reduction in the image helps OCR tools extract better, higher-quality data. Pattern recognition and continuous improvements over time also ensure that machine learning tools can “learn” and adapt to interpret difficult OCR results. The result is enhanced accuracy and a reduced need for human intervention to determine what OCR cannot classify.

Adaptive Learning Offers In-the-Moment Improvements

Adaptive learning enables ML algorithms to continuously update their models with new information without extensive retraining. As the algorithm ingests additional data and makes more classifications, the model continues learning to differentiate between similar but different characters extracted from documents. The result: the more you feed through the system, the better it becomes over time. These continuous improvements are fundamentally central to the advantages of ML in extraction.

Contextual Understanding Helps Sort Edge Cases

Some data can seem very similar on the page while being dramatically different. Consider how a human might easily confuse the letter “o” with zero or “i” with the number one. Data extraction tools aren’t immune to making mistakes in this category. However, machine learning tools can analyze data in context—that is, they can “understand” the information surrounding an uncertain item and determine the most likely character.

Automated Validation Reduces Manual Reviews

Low-quality extraction can pollute a data set if not caught early. Machine learning tools can automatically identify when extracted information does not conform to the quality standards set by your business. It can then automatically route the data to a human for review and approval. The system picks up where it left off once the human corrects or confirms the extracted information. There’s no need for employees to hover over the process or meticulously examine every line for errors when automated validation makes it easy.

Anomaly Detection Keeps Humans in the Loop

Anomaly detection is similar to automated validation but focuses on data that seems “out of family” for a given type of information. Rather than filtering low-quality data, anomaly detection flags data that might be in error. For example, an account number with too many or too few digits might be flagged as an anomaly. Again, this information can rapidly reach the appropriate stakeholder in the process for verification or correction, ensuring high-quality outputs.

Access Advanced Data Extraction Capabilities Today

Companies can dramatically reshape how they approach critical processes through solutions empowered with machine learning. Accounts payable teams can enjoy faster results, higher quality data, and better outcomes. The Tungsten Automation Solution Marketplace feature many advanced capabilities that deliver exceptional ML outcomes for data extraction. See what’s possible when you learn more about these opportunities today.