Frictionless payments vs Fraud mitigation

As the E-commerce industry evolves, fraud becomes more prevalent and sophisticated. In fact, online payment gateway fraud is expected to cost more than $200 billion over the next five years. Evidently, fraud management is becoming increasingly important to businesses attempting to break the cycle of this never-ending cat and mouse game. In this article, we will discuss the main challenges associated with fraud prevention, as well as the tools that your company can use to mitigate the risks.

Evolving fraud patterns

Because of the constantly changing patterns, detecting fraud is difficult. Fraudsters are constantly looking for new ways to commit online fraud, and as soon as the market discovers a solution to detect a specific type of fraud, the fraudsters are likely to have already found an "innovative" way around it. This means that businesses are typically one step behind, attempting to catch up with new patterns. As a result, the nature of fraud places an undue burden on the company's departments, which must keep up with the pace and adapt anti-fraud solutions on a daily basis.

The trade-off between happy customers and fraud costs

Customer service concerns also severely limit fraud management. Frequently, fraud management tools add friction to the checkout process, which is incompatible with the company's desire for an effortless shopping experience. Furthermore, businesses are concerned that strict prevention measures will cause legitimate orders to be rejected, resulting not only in decreased sales but also in the loss of a lifetime customer. After all, no customer wants to be treated as if he or she is a criminal.

Omnichannel Retail

Because it promotes sales and provides a holistic customer experience, omnichannel retail is a market leader in the e-commerce industry. However, this complicates fraud prevention because customer behavior is less predictable, and abnormal behavior is more difficult to detect. When customers are constantly switching between devices and channels, fraudulent activity becomes more difficult to detect and necessitates a more sophisticated analysis of customer behavior. As a result, fraud prevention no longer relies on a single, static view of the customer, but rather on data spanning the entire customer journey.

Data Breaches

Although customer data is essential for detecting fraud, it can also be a double-edged sword and a source of vulnerability. Customers are typically required to provide personal information during the account setup process, which can later be used by fraudsters in the event of a data breach. In order to facilitate fraud, legitimate customer information is frequently sold on illegal markets. Furthermore, traditional data protection methods, such as passwords, may jeopardize data security. Aside from the fact that passwords are easily hacked, a global study conducted by Experian discovered that only 19% of customers change their passwords at the recommended time, increasing the likelihood of fraud.

Solutions

Fraud prevention is a complicated issue that poses numerous challenges for businesses. Some of the factors that complicate fraud management are evolving fraud patterns, customer service concerns, omnichannel retail, and data breaches. Traditional solutions, such as manual review, are clearly no longer appropriate because they are rigid, prone to error, and time-consuming. So, what are your options?

First, machine learning systems offer numerous advantages; they are proactive and detect fraud in real-time by combining past data and new knowledge. This can help to mitigate some of the risks associated with changing fraud patterns because machine learning can detect new patterns that humans haven't noticed yet. It can also help businesses better understand customer behavior and detect anomalies, all while reducing false positives and maintaining customer relationships. Aside from being more accurate, machine learning is also more efficient and less expensive than manual review by fraud analysts. However, it is also important to remember that machine learning is only as good as the data that is "given" to it, so high-quality and accurate data is required for effective prevention.

Furthermore, biometric authentication, such as fingerprint, face, or voice recognition, can be used as a preventative measure. If a transaction is deemed high-risk, 3DS2 provides such authentication, presenting a frictionless solution to online fraud. This replaces traditional, easily hacked authentication methods such as passwords and makes it more difficult for fraudsters to impersonate customers. As a result, the adoption of 3DS2 is unquestionably worthwhile.

Finally, tokenization is an effective fraud prevention strategy. To summarise, tokenization replaces sensitive account and card information with something that cannot be decrypted, thereby securing sensitive data transmission. This prevents data breaches and fraudsters from obtaining customer information.

Conclusion