Fraudsters never sleep — especially not during a global pandemic and economic collapse. Rather, they’re taking advantage of the widespread panic and spikes in digital traffic by targeting companies under pressure.
This has put banks, fintechs and any companies that process transactions at heightened risk. Already soaring prior to the coronavirus outbreak, card fraud, in particular, is forcing issuers and merchants to rethink their protective measures as the pandemic deepens.
One survey found that 37% of UK finance professionals agreed that their existing finance and procurement processes put them at risk of fraud—and this was a belief they held prior to the pandemic. It’s safe to say their fears were founded, as in a single week during the pandemic (13-19 April), hackers attacked businesses more than 22 million times globally. All told, more than a quarter of all transactions during the COVID-19 crisis have been fraud and abuse attempts—representing a 20% increase over the previous quarter.
With fraud growing in incidence and complexity, businesses issuing or processing payments must be equipped with the tools to win the fight against it. A single tool is not enough; to stay a few steps ahead of fraudsters, businesses need a multilevel solution that includes both artificial intelligence (AI)/machine learning and virtual cards.
AI and machine learning spot suspicious patterns
AI has ensured that companies are well-equipped to take on the kind of nuanced, highly sophisticated fraud now being carried out around the world. It quickly alerts analysts to anomalies, develops trend-based insights and discerns if a given transaction or series of financial activities are unusual or fraudulent. By applying machine learning, companies can mine historical and live data to locate patterns within customers’ behaviour and can then evaluate every transaction to make accurate fraud predictions. The more data that’s collected across historical transactions, the better the precision in fraud detection.
Further, when a breach or fraud is detected, AI allows businesses to move quickly to address challenges and swiftly problem-solve. Let’s say that fraudsters get creative and tailor their attacks to news events — for instance, what we’ve been seeing in the news recently with PPE (personal protective equipment)-selling scams. As these new types of fraudulent transactions are identified, the AI model will automatically adapt based on the pattern of these transactions. This is a contrast to the legacy way of managing fraud, which was reliant upon a person to trial and error new business rules to detect fraudulent transactions.
Virtual cards offer a simple solution to fraud
Uniquely valuable in B2B contexts, virtual cards have the ability to significantly reduce the risk of fraud in several ways during this time. First, they can be set up to be used only once, so even if the data is subject to a breach, the card cannot be used if the payment has been processed.
Additionally, single-use virtual cards typically have tight controls associated with them, such as the amount (which can be a range of amounts or one specific amount), expiration date, merchant, and even Merchant Category Codes, among other parameters. With regard to amounts, single-use virtual cards can be authorized to use with one exact amount only (such as USD 100), or they can be authorized for multiple transactions that ultimately result in that one exact amount (such as five USD 20 transactions).
Overall, virtual cards decrease the likelihood of fraud because the virtual card information isn’t as useful to a fraudster if stolen.
Diversify your fraud protection for best results
To fight fraudsters while maintaining a positive customer experience in our current climate, companies need to take nothing less than a comprehensive approach. That must include AI-based solutions and the implementation of fraud prevention tools such as virtual cards. Together, these tools will make it much easier to spot fraud attempts accurately without interrupting sales or declining genuine transactions.
About Ryan Taylor
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