TL;DR:
- Fraud prevention trends in 2023 focus on combating the commercialization of fraud and the accessibility of fraud forums and marketplaces on the dark web.
- Visa’s Compelling Evidence 3.0 (CE 3.0) introduces new rules for online merchants to dispute chargeback fraud, emphasizing the importance of evidence collection.
- Machine learning (ML) is crucial in the fight against fraud, as organized fraud actors leverage automation tools to accelerate attacks.
- Economic uncertainty drives up fraud rates, making machine learning essential for accuracy, efficiency, revenue protection, and customer retention.
- Fraud prevention strategies include in-house models, insurance models, and Digital Trust & Safety platforms.
- Transparency and control are vital considerations when choosing a fraud prevention solution, enabling informed decisions and risk threshold modifications.
- Sift’s customer community portal, Sifters, fosters collaboration among fraud fighters, enabling the sharing of best practices and emerging fraud threat information.
Main AI News:
As fraud rates continue to surge on a global scale, merchants are faced with the challenge of implementing effective fraud prevention measures in 2023. One key aspect gaining momentum is the application of machine learning (ML) and its significance in the fight against fraud. Let’s delve into some of the main trends and strategies that businesses should consider, along with the importance of machine learning in this landscape.
One worrisome trend is the commercialization of fraud, which has enabled even individuals lacking prior skills or resources to engage in fraudulent activities. The accessibility of fraud forums and marketplaces on the dark and deep web has made it easier than ever to obtain tutorials and tools that facilitate fraudulent acts. Shockingly, these forums openly promote their services to regular consumers, often through popular messaging apps like Telegram.
To address this evolving threat, Visa has introduced Compelling Evidence 3.0 (CE 3.0), a set of rules enabling online merchants to dispute chargeback fraud by providing an extended range of evidence. This evidence includes crucial elements such as IP addresses, device IDs, shipping addresses, and user accounts. Therefore, it is imperative for online merchants to invest in robust online payment fraud prevention solutions capable of continuously monitoring and collecting these vital analytics.
With fraud becoming increasingly rampant and simpler to execute, businesses aiming to safeguard their revenue and ensure security must invest in technology that harnesses the power of machine learning. Organized fraud actors are already leveraging automation tools to accelerate attacks, rendering legacy services inadequate, especially those reliant on manual review. The integration of machine learning, coupled with the right data, forms the foundation for a robust fraud prevention program that prioritizes user experience.
Furthermore, it is crucial to recognize that economic uncertainty plays a significant role in driving up fraud rates. Unfortunately, this uncertainty often results in staffing shortages due to corporate budget constraints. As organizations face the challenge of achieving more with reduced resources, machine learning becomes a non-negotiable requirement for any business aiming to combat fraud effectively. By deploying machine learning, companies can achieve accuracy, efficiency, revenue protection, and customer retention simultaneously, thereby enabling sustainable growth.
In the realm of global fraud prevention, solution providers are innovating with novel approaches to mitigate digital risks and facilitate safe business growth. So, what distinguishes various types of fraud prevention platforms available on the market? Let’s explore the main differences.
Currently, businesses can choose from three primary fraud prevention strategies: the build-your-own model, the insurance model, and the comprehensive Digital Trust & Safety platform.
The in-house fraud decisioning strategy, also known as the build-your-own model, requires substantial investment and commitment for successful execution. Businesses opting for this method must allocate significant capital and resources while waiting for a longer period to see returns on their investment.
Fraud prevention providers aligned with the insurance model may initially seem appealing since they offer a chargeback guarantee and the virtual outsourcing of an entire function. However, this guarantee often leads to a higher rejection rate of overall transactions, including legitimate ones. Consequently, user experience and customer retention can suffer a real negative impact.
On the other hand, Digital Trust & Safety platforms strike an optimal balance across several key attributes. These platforms offer nuanced and accurate fraud risk scoring, capital and resource efficiency, and real-time responsiveness. Leveraging the wisdom of machine learning, powered by a global data network, and employing management structures focused on transparency and control, these platforms enable businesses to combat fraud effectively.
When selecting a fraud prevention solution provider, it is crucial to emphasize transparency and control within fraud operations. These factors are essential for online merchants as they strive to make informed decisions quickly. Transparency provides visibility into fraud risks and enables analysis of patterns, including both red flags and positive signals. By understanding which customer segments are more susceptible to fraud, merchants can tailor their strategies accordingly.
Control, on the other hand, empowers businesses to make the right decisions for each transaction and adjust risk thresholds based on their unique requirements. For instance, merchants might choose not to challenge low-value sales, recognizing that the risk of an abandoned shopping cart outweighs the potential risk of fraud. Solutions like Sift offer dynamic friction, evaluating the risk score of each transaction on a case-by-case basis.
Finally, combating fraud requires a network of human and machine collaboration. Solution providers, merchants, and end-users must work together to stay ahead of fraudsters. This necessitates platforms that foster collaboration and knowledge sharing among fraud fighters. How does Sift’s new customer community portal contribute to bridging the gap in this field?
Conclusion:
The rise of fraud and the commercialization of fraudulent activities necessitates proactive fraud prevention measures in the market. The integration of machine learning is essential for businesses aiming to combat fraud effectively, as it enables accurate risk assessment and efficient fraud prevention programs. Transparency and control are crucial factors, allowing merchants to make informed decisions quickly.
The emergence of customer communities, such as Sift’s Sifters, promotes collaboration among fraud fighters, strengthening the collective defense against fraudsters. By investing in advanced fraud prevention technologies and fostering industry collaboration, businesses can mitigate risks, protect revenue, and build trust with their customers in an evolving fraud landscape.
