BOPIS fraud trends, strategies increase thanks to curbside demand

Whether retail businesses call it BOPIS, curbside pickup, or “click and collect,” — in the restaurant industry, it’s “mobile order-ahead” — customer demand for this buying option has skyrocketed. In fact, BOPIS purchases increased 554% between May 2019 and May 2020.

BOPIS Fraud Stats:BOPIS fraud saw a 7% attempt rate, compared to a 4.6% attempt rate in other delivery channels.

The “buy online, pick up in-store” (BOPIS) buying option combines physical and digital experiences. And it gives consumers more ways to engage with their favorite brands. BOPIS options help drive online traffic to brick-and-mortar locations. And once consumers are at the location, they’re likely to buy additional goods.

Businesses that offered this buying option before 2020 saw a 70% increase in transaction volume and a 58% increase in transaction value in 2020, according to data from ACI Worldwide. Unfortunately, most establishments only require minimal proof of purchase at pickup locations, making BOPIS fraud prevalent among bad actors. BOPIS fraud saw a 7% attempt rate, compared to a 4.6% attempt rate in other delivery channels, the ACI data revealed.

Bad actors have followed the popularity of BOPIS trends and are taking advantage of its structure. In BOPIS fraud schemes, bad actors use stolen credit cards or stolen account credentials to place orders online, pick up goods in-store, and either keep goods or resell them.

Stopping this type of fraud isn’t as easy as not offering a BOPIS option. The absence of a BOPIS option can cut off a critical path to growth and shopping convenience. So businesses will need to use advanced tools to prevent BOPIS fraud and decrease their risk of substantial product and chargeback losses.

Why BOPIS fraud is increasing

BOPIS fraud bypasses a standard method of basic fraud detection solutions. With the BOPIS purchasing option, bad actors avoid security measures that may be in place with traditional transactions. Fraud detection that doesn’t use sophisticated AI and digital identifier data has to rely on basic means to detect the probability of fraud.

Speed is another reason why BOPIS fraud is increasing. BOPIS purchases, especially mobile order-ahead, are designed for speed and convenience, so there’s little time for businesses to detect fraud. Excellent customer service and speed are essential in BOPIS purchases, so businesses have to approve as many orders as possible as quickly as possible. The purchase-to-pick-up fulfillment window is small. Fraud analysts have almost no time to perform manual reviews of suspicious purchase details, and bad actors know it. It’s this lack of time that makes it easy for bad actors to use mobile order-ahead apps in card testing attacks.

Consumers love BOPIS buying options because they reduce the time it takes to acquire goods. Bad actors know that any business that attempts to curb fraud by increasing customer friction at checkout or pickup is turning away potential business. Businesses that ask for additional verification information at the curbside pickup line risk reducing customer satisfaction. If a customer experiences any additional friction, they might think twice before shopping the business again.

So what’s the solution? A digital fraud prevention solution that uses networked data to make accurate, real-time identity trust decisions can help businesses prevent BOPIS fraud.

How networked data can help businesses improve BOPIS fraud strategies

Experienced fraud analysts know that establishing identity trust is about the quality of data available. That’s why Kount’s industry-leading, AI-driven fraud prevention solution, Kount Command, is built on the Identity Trust Global NetworkTM. The global network connects fraud and trust-related signals from 32 billion annual interactions across more than 75 industries and 50 payment processors and card networks.

Kount’s advanced AI links these signals using supervised and unsupervised machine learning. Supervised machine learning analyzes past decisions, while unsupervised machine learning detects emerging fraud. In milliseconds, these tools deliver accurate identity trust decisions.

Essentially, businesses can use real-time payments, location, and digital identifier data to prevent BOPIS fraud at the point of purchase. For example, in the case of BOPIS fraud, location and geography signals are especially useful. Let’s say a bad actor places a BOPIS or mobile order-ahead order using a stolen credit card. Fraud and trust signals for that credit card can indicate the current card activity is outside of normal behavior, and someone may be using it fraudulently.

Plus, the speed at which Kount can link fraud and trust signals means businesses can approve more good orders and decline high-risk orders faster. And they don’t have to rely on manual reviews or compromise the customer experience.

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