Data Extraction: Comparison of traditional and ML methods

Traditional methods for data extraction include collection of materials, correction of errors, and manual data entry. Because of differing formats, languages, and texts combined with the complex, repetitive nature of each document, traditional methods have produced high personnel turnover from pressure to perform in an efficient, timely, and accurate manner.

With Machine Learning enhanced OCR, data is scanned and indexed before being audited for common mistakes like double-billing, over or under-billing, and verification that contract terms have been met.

Classification and Categorization

Manual processes can be hampered due to the variety of format structures and condensed handwritten characters. Unusual or complicated charges often appear, and questions arise about where and how to categorize them. Answers often mean a slowdown in the entry process.

Machine learning collects data from millions of invoices over time and learns to recognize various types of language, format, and text so that reading and indexing become faster and more accurate while condensing entry time.

Anomaly Detection

High volume requires intense scrutiny of performance within this high-pressure environment. Finding and correcting mistakes from simple human error and anomalies in the BOL, contract, or standards and regulations produce a need to look more deeply into the computations and written agreements. This necessary scrutiny can be both time-consuming and costly.

However, machine learning algorithms designed to search for information use predictive analytics to flag such data so management can decide how and why the anomaly exists.

Fraud Protection

With any invoice processing system, the need for protection from fraud poses a costly but necessary challenge. A safeguard for sensitive financial information must be in place and updated to maintain high client trust.

Machine learning algorithms allow for the incorporation of advanced security measures. Beyond security, advanced analytics provide insight into negative patterns and vendor performance, along with other metrics that can be used for strategic decision-making. Adjustments along the entire supply chain can close potential security gaps.

Automated Approval Workflows

When workflow approval of information or systems becomes necessary, automated systems can be programmed to find and notify the person responsible for accepting changes or corrections. Instead of manually looking up and contacting personnel who need those notifications, algorithms flag the information and inform the attendant personnel. They can then return the approval as quickly as it was sent, saving time and money with faster turn-around.

Continuous Improvement

Continuing growth in the tech landscape produced improvements that require monitoring. The need in the industry for quicker process-and-demand fulfillment has induced the search for answers to issues and fixes in technology across the business spectrum. New products, designs, and faster technology now require continuous training, monitoring, and apprehension to stay even with industry growth. The inability to keep up with industry standards inhibits opportunities to scale.

Cost Savings and Efficiency

The following list includes some of the cost savings mentioned in just this short review:

• Personnel needed for data capture and correction
• Outsourcing  and automated processes eliminate the cost of space, equipment, training, and auditing
• High security at superior levels to avoid fraud and breach are used by reputable outsourcers worldwide
• The cost of time and materials needed to train and onboard new personnel shrinks
• Any approved personnel can access pertinent information when needed
• Fewer discrepancies arise since machine learning finds and flags far more errors than are usually found with human entry and auditing.
• New opportunities to scale the business rise as expenses come under control through high-speed processing
• The ability to compete is enhanced through efficiency and the reduced cost of automated processing. Expenses and growth no longer rise at the same rate.

Machine learning has contributed options to grow and succeed in any industry that comprehends the value of automating invoice processing and auditing with ML technology. iTech offers free proof of concept and the credentials and experience to create a highly efficient and cost-effective means of improving invoice processing for your industry.