Voice of the Industry

How digital native companies fight fraud

Thursday 25 May 2023 09:09 CET | Editor: Mirela Ciobanu | Voice of the industry

Attention all digital native companies, Kevin Lee, VP of Trust & Safety at Sift, shares ideas on how to manage evolving threats with real-time fraud detection.

As more consumers shift their purchasing and financial activity to digital spaces, even brick-and-mortar companies have had to adjust by providing online platforms for their customers, and in some cases, eliminating physical stores. This transformation, however, has also brought businesses face-to-face with an increasingly complex online fraud landscape. Companies are dealing with the difficult trade-off of managing fraud losses and ensuring quality user experiences for their customers. Leading businesses know both are necessary for success.

Fortunately, there is much to be learned from digital native companies that exist entirely online, which have been on the front lines of online fraud from their start. A recent survey revealed that cybersecurity and cloud computing are the top two technology initiatives prioritised by business leaders; in pursuit of both, digital native companies have learned valuable trust and safety lessons that offer a blueprint for how all companies need to approach and scale fraud prevention.


The future is digital-first, and so is fraud

Digital payments have grown rapidly in recent years and show no signs of slowing. The total transaction value of global digital payments is expected to top USD 9.46 trillion in 2023 and is estimated to reach over USD 14 trillion in 2027. Consumers have come to expect fast, frictionless online payments, and will switch from brand to brand to find the best benefits. But digital transactions don’t come without risk.

Cybercriminals have had their sights set on ecommerce for a while, and all merchants with an online presence are at risk. This year, card-not-present (CNP) fraud is projected to account for USD 9.49 billion in losses, making up 73% of all payment fraud. And with the rise in increasingly accessible fraud forums on the deep and dark web, businesses are seeing a relentless influx of attacks.

To make matters worse, fraudsters have also recognised the benefits of new technology, aligning their own pursuits with enhancements in automation, artificial intelligence, and digital payments. Similar to the software-as-a-service business model, cybercriminals operating in the Fraud Economy are launching fraud-as-a-service models designed to turn their fraud skills into on-demand services for sale on the deep/dark web. For example, phishing-as-a-service kits enable fraudsters to bypass multi-factor authentication with the same cost-effective and scalable benefits of a cloud service.

Automation is another example of how fraudsters are gaining an advantage. Card testing and card hopping attacks automate the process of making fraudulent transactions more quickly than manual reviews can detect them. If fraud prevention teams want to level the playing field, they also need to embrace the benefits of real-time machine learning.


Digital native companies at the forefront of fraud prevention

Digital native companies, who have always relied on their revenue from CNP transactions, have had to quickly embrace new fraud prevention technologies and re-evaluate their risk operations to survive.

Fraud operations can be particularly challenging to get right, especially when new technology solutions or threats disrupt the market. Most organisations tend to prioritise ease of use over scalable security. For example, they might minimise fraud by enforcing multi-factor authentication for every credit card transaction, but this friction would drive away more sales than the fraud it prevents. It is a common trade-off, but not an enviable one for fraud fighters.

Even worse, sophisticated new fraud techniques can quickly emerge when technology paradigms shift, leaving many fraud-fighting teams struggling to catch up. Bot attacks, for example, are nearly impossible to keep up with without having automation baked into fraud prevention.


Manage evolving threats with real-time fraud prevention

Fraud cost online businesses USD 41 bln in 2022, a price tag that is expected to grow in 2023. Online fraud prevention is no longer a choice—it’s a necessity for all modern businesses. As fraud attacks become more sophisticated, it’s imperative for risk teams to fight fire with fire.

Real-time data analysis enables fraud prevention teams to detect anomalies, patterns, and potential fraud indicators. Fraud prevention services can leverage large volumes of data across their entire customer network to quickly identify suspicious activities and take immediate action. For example, Sift ingests more than one trillion events from more than 34,000 apps and websites into its fraud decision engine. Machine learning powered by such a massive and diverse network can continuously adapt to new threats for accurate and real-time detection.

Digital natives understand the importance of having a scalable and elastic infrastructure to handle fluctuating workloads. Likewise, online fraud prevention services have the ability to scale resources dynamically, based on the current demand. By leveraging cloud services, organisations can handle sudden spikes in traffic and potential fraud attempts more effectively.

Fraud prevention teams can even the odds with continuous monitoring and automated policy enforcement, which combines monitoring behavioural analytics to detect anomalies with automated response actions, such as enforcing multi-factor authentication for suspicious transactions.


About Kevin Lee

Kevin Lee is VP of Digital Trust and Safety at Sift where he helps customers implement strategies that cross-functionally align risk and revenue programs. Prior to Sift, he has spent the last 15+ years leading various risk, chargeback, spam/scams, and trust and safety organizations at Facebook, Square, and Google.




About Sift

Sift is the leader in Digital Trust & Safety, empowering digital disruptors to Fortune 500 companies to unlock new revenue without risk. Sift dynamically prevents fraud and abuse through industry-leading technology and expertise, an unrivalled global data network of 70 billion events per month, and a commitment to long-term customer partnerships. Global brands such as DoorDash, Twitter, and Wayfair rely on Sift to gain a competitive advantage in their markets. Visit us at sift.com, and follow us on LinkedIn.





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Keywords: fraud detection, risk management, cloud, cybersecurity, Card-not-present fraud, artificial intelligence, data analytics, transaction monitoring
Categories: Fraud & Financial Crime
Companies: Sift
Countries: World
This article is part of category

Fraud & Financial Crime


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