The use of digital shopping channels has seen a huge jump in the last year, with an increase of 60%.1 As the habits of shoppers change, so too have those of fraudsters. While the variety of fraud attacks experienced by businesses has decreased, the overall volume of attacks has risen.2 For businesses that rely on manual review processes to identify and stop fraud, a surge of digital transactions can overwhelm current fraud management systems.
With digital transactions and eCommerce continuing to grow in volume, successfully taking on fraud will require businesses to explore and rely on new tools and technologies. Incorporating tokenization along with a sophisticated machine learning solution can help automate and reduce the need for manual review, and better manage the complexity and challenges surrounding payment processing.
Securing Consumer Data in a Digital-First Setting
eCommerce has become dominant in the retail space, and with that, vast amounts of sensitive consumer data has moved online. That data, if managed incorrectly, can be vulnerable to devastating breaches that not only expose sensitive data but shine a negative light on the brand responsible for handling it. Now that consumers have a variety of commerce choices, a misstep of this nature can spell the end for retailers.
As digital commerce continues to rise, consumer interest in so-called 'protect me' features has seen steady growth as well. Over 40% of consumers surveyed in Cybersource's Global Digital Shopping Index reported data protection, returns and refunds as important features of their online shopping experience.3 When it comes to data protection, tokenization is an ideally suited solution because it replaces sensitive account information, card numbers, PINs and account numbers with unique digital identifiers called tokens to ensure no information is exposed during a transaction. At a time where interactions are increasingly more digital, anything that limits the exposure of data is critical to keeping consumers protected.
Machine Learning Reduces Errors and Boosts Efficiency
Addressing fraud often involves manual review cycles. However, these reviews are prone to human error and can run the risk of missing fraudulent activity. And with the sheer volume of data and transactions in today's digital era, manual review processes alone are not enough to keep up with fraudsters. Machine learning helps solve these challenges by reducing the need for manual reviews, allowing businesses to cut response times and update fraud models automatically to reflect new behaviors in a timely manner.
Machine learning solutions also know how to remove human bias from the decision-making process. They are trained using large volumes of past transaction data to establish cause and effect relationships about those transactions. At Cybersource, we have seen the importance of this data firsthand. Our fraud risk scoring engine uses insights from VisaNet's dataset, which aggregates and processes data from 141 billion transactions per year.4 Putting a high volume of data like this to use helps machine learning tools paint a clearer picture of potential signs of fraud and separate out the normal behaviors from those of nefarious actors.
When it comes to certain attacks, like account takeovers, having an effective machine learning model in place can help identify whether the person using an account is the true owner, preventing any further harm. But going beyond identifying transactions coming from fraudulent sources, machine learning can also help deal with cases of friendly fraud; situations where a customer may request a chargeback from their bank after receiving a purchased product or service claiming they didn't place the order. Machine learning can use the customer's historical data and patterns to prove whether the purchase was legitimate.
Machine Learning Leads the Way
The last year and a half have taken existing digital trends and thrown them into the spotlight. With digital transactions still on the rise, businesses have had to adjust the way they handle security risks, particularly with respect to fraud. As time goes on, the world will only continue to lean into digital technologies and shoppers will more than likely continue to embrace all things eCommerce. To keep up with the pace of change, businesses should be taking full advantage of tokenization and machine learning tools to help parse out fraud in the digital environment.
To learn more, visit https://www.cybersource.com/en-us/solutions/fraud-and-risk-management/fraud-report.html.
1. The Global Digital Shopping Index
2. 2021 eCommerce fraud report
3. The Global Digital Shopping Index
4. VisaNet transaction volume based on 2020 fiscal year. Volume may not include domestically routed transactions.
