|AP Invoice & Document
Fraud Detection

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Systemise anti-fraud controls: A major challenge for financial departments.

Digital uses are now the norm and the benefits to be gained by organisations are considerable: acceleration of transactions, automation or do-it-yourself approaches favour a rapid ROI. However, the digitalisation of documents and their attachments simplifies their falsification.

As a result setting standardised digital processes for fraud, is becoming a strategic challenge for companies seeking to simplify supplier relations while avoiding the malicious behavior that can be costly for companies (both financially and concerning brand image).

Having a platform integrating both the omnichannel capture of all your documents, and the powerful automation of processes, plus the detection of document or invoice fraud is the only way to systemise financial anti-fraud checks and catch them in the early stages, even before they are integrated into any business process. You gain in efficiency and in serenity.

fraud detection white paper

Document and invoice verification: To detect Accounts Payment risks

Document fraud is an intentional act designed to obtain a financial advantage or an undue service. There are several types of approaches: modifying authentic documents to misrepresent information (by withdrawal, addition, modification of content) or creating false documents.

To be as efficient as possible, accounts payable fraud detection must combine several complementary analysis methods, which together allow an optimal appreciation of the authenticity of the elements.

Graphometric image analysis is an innovative technology that enables the detection of copy-paste, deletion or addition of content in an image. On the basis of structural and frequency analysis, it reveals ballistic traces: suspicious zones that are too perfect or too identical or different from the rest of the content.

The metadata analysis of a PDF file, for example, detects the presence or absence of certain elements in the information or structure. The content itself, obtained by OCR (optical character recognition) is compared with repositories.

By automating a multitude of controls, an objective measure of the level of reliability is obtained, as well as the addition of rich information (verified elements, suspicious or anomalies) that provide valuable support for decisions and operations.