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Fraud has become a real threat in the P2P process: according to the DFCG 2020 - Euler Hermes study, 1 out of 3 companies suffered more than 5 fraud attempts in 2019 and in 56% of the cases of fraud observed, it is the document (invoice, BIC, etc…) that is implicated. 

Therefore, making a success of your Procure-to-Pay (P2P) digitalisation and automation project requires carefully considering the risk of fraud. No company is exempt: from SMEs, such as Moore and Moore Design losing nearly £900,000 because of false bank transactions done by a former finance manager... to large accounts, like Facebook and Google, victims of a scam of falsified invoices amounting 122 million dollars. And the health crisis only reinforces these risks: the pharmaceutical distributor CERP, for example, victim of a fraud of a phantom supplier for more than 6 million pounds, in an order of hydroalcoholic gel and masks.

So, is it possible to fight fraud effectively? Yes, on the condition that you understand the situation well, adopt the right approaches, and equip yourself with the right tools.

7 out of 10 companies Victims of fraud attempts Euler Hermès Study – DFCG 2020

The document and its data, the entry point for fraudsters

While computer systems intrusions like false president fraud and fake customer fraud are recurrent, it is fake supplier fraud that leads in most of the cases. Imagine an accounting department receiving a letter (email or letter) from a supplier, appearing to be a legitimate document, asking for a change of bank details. When paying an invoice, the money will therefore be transferred to this new account and diverted. An essential point to understand and better fight fraud: today, fraud is most often hidden in document exchanges, with fraudsters mainly using methods such as the creation and alteration of paper or digital documents. Hence, the importance of having tools to analyse documents and identify possible forgeries (amounts, IBAN number, etc.).

Thinking of 'invoice data capture', regardless of fraud, is no longer a reality. Fraud is a real topic.

Elodie Papet, Head of Inter Mutual Assistance Operational Flow Department

The limits of traditional approaches

While attitudes are changing in the face of the threat, change remains slow. Thus, 6 out of 10 companies have still not allocated a specific budget to fight against fraud. Due to lack of resources and time, controls are most often carried out on samples and manually. Human-based processes, therefore fallible. The result: cases of fraud identified too late, and only in part. Sometimes attempts are made to identify fraud patterns by implementing statistical data analysis, an approach that is unfortunately long and expensive.

While businesses have never been so vulnerable, these traditional approaches to fighting fraud clearly show their limits.

The risk is that the crisis we are going through will lead to less vigilance (…). Cyber fraudsters can take advantage of this to exploit any flaw in the prevention and control system and intensify their attacks.

Christian Laveau, President of the Cyberfraude Working Group, DFCG

Systematise document and data controls

To respond to this growing threat, finance departments must now have tools capable of detecting fraud not only on data, but also on documents and processes. And to be perfectly effective, this detection must no longer be carried out “a posteriori” and on the basis of a test sample, but systematically on all invoices and “a priori”, meaning before payment. For all these reasons, these controls therefore have every legitimacy to be integrated directly within the supplier invoice capture and automation solution. This is precisely what ITESOFT offers with Streamline Invoices, the only Procure-to-Pay digitalisation solution including a robotic detection service for fraud attempts using unique technologies developed with research laboratories:

  • Systematic checks on documents and data
  • Controls carried out a priori i.e. upon receipt of the document, therefore before payment
  • Combination of exclusive image analysis algorithms (graphometry, AI, etc.) and internal and external data consistency checks (amount, VAT number, bank name, etc.)