Digital Auditor: A Framework for Matching Duplicate Invoices Duplicate invoice payment is one of the most prominent challenges encountered by accounts payable operations, and whenever it occurs, it costs to the company. Due to large volume and variety of invoices across multiple suppliers, it is not pragmatic to manually examine every invoice to check if it is legitimate and has not been previously financed. This paper presents Digital Auditor (DA), an automated framework for detecting duplicate invoices. It is based on two principles 1) converting invoices into structured templates by extracting relevant information from the invoices and organizing it as keyvalue pairs and 2) machine learning based duplicate detection algorithm which compares corresponding fields between two invoices and identifies duplicate invoice pairs. Digital Auditor efficiently identifies duplicate pairs, and thus alleviates laborious manual efforts and time in inspecting the invoices against the previously paid invoices. To demonstrate the efficacy of Digital Auditor, this paper presents comprehensive experimental results and key observations from usertrials by business professionals on a large sample of invoices from a nonproduction environment.

Association Rule Mining from large datasets of clinical invoices document The concept of massive data generation nowadays affects several domains such as marketing including electronic invoices of large retailers, web access log files, healthcare, life sciences and so on. All these web activities introduced a new way to pay through the concept of electronic invoices (eInvoice), replacing the paper invoices. For these reasons, eInvoicing can be thought of as an innovative digital infrastructure for the issue, transmission, and storage of invoices. The availability of large volumes of eInvoices allows the discovery of new knowledge through data mining in these domains. Thus, users by using data mining can extract knowledge from large invoices documents. In this paper, we present a software tool for mining association rules from invoices produced in healthcare centers. In particular, the tool adopt a novel preprocessing methodology that provides merging, cleaning, formatting and summarization of eInvocies. The methodology can improve the quality of a huge amount of clinical invoices reducing the quantity of irrelevant data, making the remaining data suitable to mine information in form of association rules. The core of the tool allows to extract association rules from eInvoices; as a case study, we discuss the mined rules, highlighting the relationships among the purchased goods.