UK-based payment orchestration platform Corefy has launched the first version of a new fraud prevention tool named Firewall. The Firewall fraud prevention tool includes a decision tree based on a rule engine, static attributes, and lists.

Static attributes contain information about the transaction, merchant, payment requisites, and user’s device data, including IP address, the type of browser, or fingerprint data. The system also includes block lists, grey lists, and allow lists that merchants can obtain from third-party platforms, or they can create them themselves. Company officials revealed in the official press release that the new Firewall system was implemented at the request of their clients.

Consequently, the team decided to develop a Firewall as a specific instrument against fraud. The same official emphasised that Firewall will be useful not only for accepting payments but also for making payouts as it meets all PCI DSS requirements. From the clients’ point of view, Firewall V1.

0 is customisable in the sense that they can adjust different rules for payments and payouts separately, create general requirements for all merchants, or customise them for particular ones. They can also set special rules for different stages of the transaction lifecycle or specific events. Corefy gives some advice on payment fraud prevention According to Corefy, payment fraud affects cardholders as well as merchants, and most of the schemes are aimed at  obtaining the card data or the card itself through fraudulent means.

Governments, businesses, and even cardholders are constantly trying to find ways to tackle each scheme. However, as the payment industry develops, the diversity of fraudulent schemes develops as well. In order to fight payment fraud and minimise its likelihood, banks advise their customers to not trust their cards to third parties, to not leave them unattended, and to never share their PIN code.

Online businesses often utilise various antifraud solutions to cope with fraudsters, but these solutions often require a level of customisation in order to prevent profit drop and maintain a high level of security. To solve this issue, companies can turn to individual fine-tuning, which means setting up corresponding filters after analysing the business and the clients’ geography. However, in some cases, it might be necessary for online stores to monitor suspicious transactions and decline them manually.

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