Leveraging AI & ML for Freight Audit

Freight audit is the process of reviewing transportation costs to ensure that they are accurate and in line with an organization's expectations. Traditionally, freight audit has been a labor-intensive process, requiring manual review of invoices and other documents. However, the use of machine learning (ML), optical character recognition (OCR), and named entity recognition (NER) can greatly facilitate this process and improve its accuracy.

In this white paper, we will explore how these technologies can be used to improve freight audit processes, and discuss the potential benefits and challenges of implementing such a system. OCR for Data Extraction.

OCR technology allows organizations to convert scanned documents, such as invoices and contracts, into digital text that can be easily processed and analyzed. This makes it possible for organizations to quickly and easily access and analyze large amounts of data from transportation documents, without the need for manual data entry.

To use OCR for data extraction, organizations can first scan their transportation documents using a suitable scanner or mobile device. The scanned documents can then be passed through an OCR engine, which will convert the images into digital text. In some cases, additional pre-processing may be necessary to improve the quality of the scanned images and increase the accuracy of the OCR process.

Once the scanned documents have been converted into digital text, they can be processed using natural language processing (NLP) techniques to extract relevant information from the text. This information can then be stored in a database or other data repository for further analysis.

NER for Data Classification

Named entity recognition (NER) algorithms can be used to identify specific entities, such as names, dates, and monetary amounts, in the text of transportation documents. This allows organizations to quickly and easily extract relevant information from these documents, such as the names of carriers, the dates of shipments, and the amounts invoiced.

To use NER for data classification, organizations can first pre-process their transportation documents to improve the quality of the text and increase the accuracy of the NER process. This may involve tokenizing the text, applying part-of-speech tags, and using other NLP techniques to prepare the data for analysis. Once the text has been pre-processed, it can be passed through an NER algorithm, which will identify and classify the named entities in the text. The identified entities can then be stored in a database or other data repository, along with their corresponding classification labels.

ML for Anomaly Detection

Machine learning (ML) algorithms can be trained on large amounts of transportation data to identify trends and patterns in the data, and to automatically detect anomalies that may indicate fraudulent activity or other issues. For example, an ML system could be trained to recognize sudden spikes in transportation costs, or discrepancies between expected and actual invoiced data.

Overall, the use of OCR, NER, and ML can help organizations efficiently manage and analyze their contract data, enabling them to make data-driven decisions and improve their operations.