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Digital Identity, Security & Online Fraud

Dimebox launches machine learning fraud predictor

Wednesday 16 May 2018 | 12:20 PM CET

Dimebox, a white-label acquiring platform, has announced the release of a proprietary fraud predictor service based on machine learning.

The service is designed to detect evolving fraud patterns, allowing fraudulent payments to be blocked without hindering legitimate transactions. The fraud predictor trains itself on batches of transactions that are known to be legitimate or fraudulent, from specific merchant databases, creating a deep understanding of the kind of fraud that is targeting individual merchants.

Within the Dimebox platform, transaction rulesets can then be employed to automatically stop a transaction before it is completed, if the predicted likelihood of fraud is above a defined threshold. The predictor uses self-learning algorithms to calculate a fraud score for every transaction, allowing the user to decide whether or not the score is high enough to warrant blocking, in the context of other criteria.

Fraud data is collected directly by the gateway, via chargebacks and fraud reports—Visa’s “TC40” report and Mastercard’s “SAFE” report— through direct access to card issuers, according to the company's press release. By adding the fraud predictor feature to their platform, Dimebox expands their fraud offerings for merchants, PSPs and acquirers. For more information about Dimebox, please check out a detailed profile of this company in our dedicated, industry-specific online company database.

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