One Article Review
| Source |  Network World |
|---|---|
| Identifiant | 360275 |
| Date de publication | 2017-04-28 09:21:00 (vue: 2017-04-28 09:21:00) |
| Titre | Sift Science uses machine learning to help businesses reduce fraud while enhancing the user experience​ |
| Texte | This column is available in a weekly newsletter called IT Best Practices. Â Click here to subscribe. Â Companies that provide online transactional services to consumers or other businesses have to be concerned about fraud. Whether it is renting hotel rooms to travelers, selling books to avid readers, arranging shipping services for hard goods, or any of the thousands of other types of sales and services transacted online, the entity behind the online business needs to know if the end user and transaction can be trusted.The credit reporting company Experian says that e-commerce fraud attack rates spiked 33% in 2016 compared to 2015. Experian attributes this increase to the recent switch to EMV (those chip-based credit cards), which drove fraudsters to online card-not-present fraud, and to the vast number of data breaches in which users' online credentials were stolen. The Federal Trade Commission says the number of consumers who reported their stolen data was used for credit card fraud increased from 16% in 2015 to 32% in 2016.To read this article in full or to leave a comment, please click here |
| Envoyé | Oui |
| Condensat | 2015 2016 about any arranging article attack attributes available avid based behind best books breaches business businesses called can card cards chip click column comment commerce commission company compared concerned consumers credentials credit data drove emv end enhancing entity experian experience​ federal fraud fraudsters from full goods hard have help here hotel increase increased know learning leave machine needs newsletter not number online other please practices present provide rates read readers recent reduce renting reported reporting rooms sales says science selling services shipping sift spiked stolen subscribe switch those thousands trade transacted transaction transactional travelers trusted types used user users uses vast weekly whether which who â click â companies |
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