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Isshymae Matzunaga


Member since September 27, 2013

  • Fraud-photo1_177_


    If a New Yorker’s credit card is used to buy jewellery in Bangkok, the bank might suspect that it has been stolen.

    However, if half an hour before, their partner’s card on the same account has been used at a restaurant in the same city, the bank could surmise the couple are on holiday.

    Data analytics allows checks such as this to happen in seconds – the time between the card being inserted into an electronic reader and its authorisation, says Paul Eagles, vice-president of product and future payment risk at Visa Europe.s

    It is a far cry from the situation 20 years ago, when attempting to use your credit card seven times in a day, or spending more than £1,500 on a single transaction, might well have led to your card being blocked.

    “Fraud detection needs to be as unobtrusive as possible,” says Mr Eagles. “The challenge is not to inconvenience customers.”

    In addition to speeding up security checks, data analytics improves detection rates by allowing many factors to be considered simultaneously. In the past, financial institutions set “rules” that computers checked to decide whether or not to allow transactions. But this meant predicting what might constitute suspicious behaviour.

    Now, rule checking is used in combination with technologies such as anomaly modelling, text mining, predictive modelling, data validation and social network analysis.

    This helps banks to spot the tell-tale signs they might never have previously thought of – the “unknown...