Omnichannel commerce has reshaped how customers shop and pay for goods and services. Modern consumers love the ability to order and pay online within a mobile app, seek out the convenience of Buy Online and Pickup in Store (BOPIS), are turning to digital wallets to simplify checkout experiences, and are exploring emerging payment methods that span everything from QR codes to biometrics.
With many industries evolving to cater to the experiences today’s consumers are demanding, a critical consideration has become how a business can develop a comprehensive fraud mitigation strategy that adequately defends against multiple fraud types and does so across a myriad of consumer engagement channels.
Payment fraud is a broad description of several unique ways a bad actor may attempt to take advantage of how money and information is exchanged when one is paying for goods or services. The fraud itself can adversely impact multiple parties across the payments value chain, including consumers, the businesses they buy from, and technology providers that enable money movement between parties.
Traditional payments fraud includes phishing and identity theft, chargebacks or friendly fraud, card testing, skimming, and the use of stolen credit card information in a card-not-present environment. Now, as customer engagement channels expand, businesses are tasked with mitigating new omnichannel fraud threats that span physical and digital interactions.
Consider the shopper that purchases goods online before returning them at a physical location; or the shopper purchasing goods in-store while using stored value and loyalty points accrued within a retailer’s mobile wallet. These new omnichannel experiences are increasing the pressure on merchants to authenticate customers, validate good transactions and mitigate bad actors that are looking to take advantage of digital/physical vulnerabilities in an omnichannel world.
To put the fraud problem in perspective, each dollar of fraud loss costs merchants a whopping $3.751, a recent study found that refund abuse fraud has increased by 51%2, and online payment fraud has seen close to a 40 percent increase in omnichannel environments.
Even if fraud rates remain steady, as online sales grow, a merchant’s losses will as well. Whatever the fraud methods used, merchants and their customers face growing risk.
While emerging payment experiences create new engagement opportunities between brands and their customers, they can also open more doors for fraudsters. The best defense for a merchant is to leverage intelligence from across the payments ecosystem to improve customer identification, and to identify patterns of fraudulent behavior.
A wide variety of data must be collected and analyzed to detect anomalies and minimize the effects of multiple fraud methods. This can include tapping into data from card networks, issuers, acquirers, and even the merchant themselves to help create the clearest picture possible before approving a transaction.
Historically, merchant fraud prevention tools have leaned heavily on third-party data from technology providers and payment processors, but pairing payments data with the merchant’s own customer intelligence helps create a 360-degree view of a customer’s purchasing habits before validating a payment. In this vein, merchant data can be particularly useful at offering viewpoints into how and where a consumer typically shops, the historical location of their device and address verification, and unauthorized login attempts to the retailer’s digital wallet or platform.
Combined with vast sets of cards and payments data, a merchant’s fraud prevention models become much stronger by maximizing the utility of available data intelligence through AI and machine learning (AI/ML). Recognizing the value of data, an increasing number of merchants are opting for this proactive approach as their primary strategy to prevent fraud before it happens, or minimize losses in the event of fraud incident.
When choosing a fraud prevention partner, it’s crucial to look for a provider that can connect merchant data with intelligence from across the payment ecosystem, and then and apply those insights to a model tailored to a unique merchant and their risk appetite. This approach will help merchants build strong relationships with their loyal customers and identify unusual behavior patterns indicating fraudulent activity.
It's essential that all data be encrypted both at rest and in transit to minimize third-party data theft at the point of purchase.
Transactional data should be matched against card and issuer data. Adding a merchant's customer data into the equation can create a more accurate decision-making process.
Maximizing the use of AI and ML models created from vast data sets will increase the likelihood of accurate payment denial decisions and decrease false positives.
Because a business’ risk appetite can vary by channel or experience, orchestration models have become critical to executing payment optimization strategies. Within payments orchestration, accurate fraud scoring is vital—harnessing payment data and providing a reliable score and recommendation based on the fraud signals of the individual customer making the transaction is imperative. Fraud detection machine-learning models should be built around one’s specific business, with configurable rules and risk thresholds.
A well-executed multi-faceted fraud prevention strategy ensures that legitimate transactions are approved while denying purchases from fraudsters by taking a customized approach designed for each retailer's unique needs.