Fraud between companies can interrupt the flow of business and destroy reputations. Moreover, it is becoming increasingly difficult to identify and prevent. According to PYMNTS, global markets lost $4.2 trillion in 2019 alone due to B2B fraud. However, machine learning can identify fraud accurately before it has occurred.
Fraud detection is one of the most useful applications of artificial intelligence. Specifically, the fraud model reveals attackers’ patterns of behavior so you can identify them in real time. As a result, you can avoid the fallout of a missing or delayed B2B payment.
In order to build a good fraud model, you should have a secure client list that includes company name, address, and financial information. Additional data can include a list of payment methods and encoding formats as well as contact details of anyone who made a transaction with the company. The easiest and most important step in prevention of B2B payment fraud is to verify the information provided by new potential clients.
A good B2B anti-fraud model must first have a large database of partners. Information on anyone who has previously visited the business website also helps.
A black list of known scammers, an archive of B2B payment fraud incidents, and access to a watch list of international organizations like the Interpol or the FBI.
The biggest challenge is to find system weaknesses during transactions while ensuring that it is still flexible and won’t harm casual visitors.
$8.5 million was stolen in an embezzlement scheme at construction company Marco Contractors, according to Pittsburgh Post-Gazette reports. The former controller at the firm, Sue O’Neill, reportedly admitted to the fraud in federal court last week, a scam she said involved manipulation of the firm’s payroll processes as well as accounts payable (AP). Reports noted that O’Neill allegedly issued payroll checks deposited directly into a separate company account, while also initiating wire transfers and writing company checks made payable to that company, subsequently manipulating AP records to suggest they were legitimate vendor payments.
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