Battling online fraud by leveraging data

Online fraud is on the rise and a new report from technology company Stripe unveiled several new patterns to assist online businesses as they combat fraudulent activities.

While chip-enabled credit cards have made bricks-and-mortar shopping safer, fraudsters are increasingly targeting online stores. Stripe looked across a year’s worth of data to seek out fraudulent behaviour patterns by country, time-of-day, industry, and other factors to guide businesses’ approaches to battling online fraud.

They found that fraud rates based on the country where the credit card is issued vary dramatically—by a factor of two or three. For example, in Singapore, fraudulent transactions are significantly larger than normal transactions.

The highest online fraud rates occur during days and times when many people aren’t shopping—such as on Christmas or late at night, according to Stripe. For US businesses, fraud rates as a percentage of overall traffic increase in the summer and in late December, but not on heavy shopping days like Black Friday.

Stripes also revealed fraudsters give themselves away by making rapid additional charges at the same businesses on the same credit card, initiating repeat purchases ten times more quickly than actual cardholders.

Additionally, fraudsters prefer products that don’t need to be delivered, can be delivered to locations like public buildings or parks without raising flags and can be obtained quickly before transactions are invalidated. These considerations can explain the prevalence of fraud among on-demand services, as well as low-end consumer goods, Stripe said.

“While there are some consistent patterns to fraudster behaviour -- for example, their high-purchase velocity, their propensity to work late at night, and their desire for cheap or immediately deliverable goods -- we've found that the predictive strength of these patterns varies widely depending on the location of the business and the fraudster,” said Michael Manapat, engineering manager for payments intelligence and experience at Stripe.

Because of this, Stripe recommends using anti-fraud tools based on machine learning trained on large amounts of data to ensure businesses are making the right trade-offs between battling fraud and maximising profits.