Newest technology doesn’t mean best when it comes to stopping fraud
I recently attended the Merchant Risk Conference in Las Vegas, which brings together online merchants and industry vendors including payment service providers and fraud detection solution providers. The conference continues to grow year to year – similar to the fraud and risk challenges within the industry. In fact, we just released analysis, that we’ve seen fraud rates spike to 33% in the past year.
This year, the exhibit hall was full of new names on the scene – evidence that there is a growing market for controlling risk and fraud in the e-commerce space.
I heard from a few merchants at the conference that there were some “cool” new technologies out to help combat fraud. Things like machine learning, selfies and other two-factor authentication tools were all discussed as the latest in the fight against fraud.
The problem is, many of these “cool” new technologies aren’t yet efficient enough at identifying and stopping fraud. Cool, yes. Effective, no. Sure, you can ask your customer to take a selfie and send it to you for facial recognition scanning. But, can you imagine your mother-in-law trying to manage this process?
Machine Learning, while very promising, still has some room to grow in truly identifying fraud while minimizing the false positives. Many of these “anomaly detection” systems look for just that – anomalies. The problem is, we’re fighting motivated and creative fraudsters who are experts at avoiding detection and can beat anomaly detection.
I do not doubt that you can stop fraud if you introduce some of these new technologies. The problem is, at what cost? The trick is stopping fraud with efficiency – to stop the fraud and not disrupt the customer experience.
Companies, now more than ever, are competing based on customer experience. Adding any amount of friction to the buying process puts your revenue at risk.
Consider these tips when evaluating and deploying fraud detection solutions for your online business.
- Evaluate solutions based on all metrics
- What is the fraud detection rate?
- What impact will it have on approvals?
- What is the false positive rate and impact on investigations?
- Does the attack rate decline after implementing the solution?
- Is the process detectable by fraudsters?
- What friction is introduced to the process?
- Use all available data at your disposal to make a decision
- Does the consumer exist?
- Can we validate the person’s identity?
- Is the web-session and user-entered data consistent with this consumer?
- Step up authentication but limit customer friction
- Is the technology appropriate for your audience (i.e. a selfie, text-messaging, document verification, etc…)?
- Are you using jargon in your process?
In the end, any solution can stop 100% of the fraud – but at what cost. It’s a balance – a balance between detection and friction. Think about customer friction and the impact on customer satisfaction and revenue.