Multilayered Fraud Prevention: Combining Manual Reviews with Machine Learning

It’s no surprise that ecommerce businesses count on high-tech solutions to screen incoming orders and prevent revenue loss due to fraud and false declines. But even with advances in technology, these automated fraud solutions still leave merchants vulnerable.

So what happens when merchants combine machine learning technology with manual review processes?

Not only can this approach help ensure good customers are never inconvenienced with false declines, but it also gives merchants the peace of mind that comes with knowing that every suspicious transaction can be reviewed before it is declined.

Here’s how this multilayered approach to ecommerce fraud prevention works.

The Benefit of Manual Reviews

While machine learning can be great at identifying and stopping clear-cut cases of fraud, it’s not always good at discerning fraud from legitimate orders in circumstances where it’s not so black-and-white. This is where your manual reviewers can step in.

Having a specially trained team of fraud analysts manually reviewing every questionable order can help fill those gaps in an automated fraud solution, which can ensure legitimate transactions are never incorrectly denied outright.

Merchants who outsource these manual reviews to a fraud solutions partner also benefit by gaining access to specially trained teams and specially designed review processes that are customized to fit their business, industry and risk levels. Even better, a trusted partner brings fraud-specific problem-solving knowledge and unique skills, which a merchant can rely on to develop and apply smarter, more accurate fraud rules that guide transactional decisions.

Additionally, a fraud team that includes data scientists, statisticians, mathematicians, and development professionals can:

  • Detect subtle fraud patterns as they develop and customize the fraud-spotting algorithm for each merchant.
  • Allow reviewers to immediately contact customers with flagged transactions.
  • More accurately distinguish between fraudulent and legitimate orders.

Thanks to the efforts of these fraud teams, merchants see higher approval rates and safer revenue.

Why Machine Learning Is Important

Machine learning uses computer algorithms, custom rules and proven statistical techniques to analyze current and historical data, fraud statistics across industries, and transactional information. By assessing every order — not just the high-risk ones — the algorithms can quickly “learn” the merchant’s business and fraud risk profile. As the algorithm processes more and more data, it self-adjusts based on what the data shows. The result is an increasingly accurate fraud review process that can perform exceedingly well when the data surrounding a transaction is clear-cut.

This ability to learn means that even when fraudsters develop new strategies for perpetrating fraud, machine learning can pick up on these patterns, integrate them into existing data, and improve its risk-scoring algorithms.

But that’s not the only reason machine learning solutions are so popular. Machine learning also makes it possible to:

  • Analyze a wide range of data, such as information on a merchant’s unique segments and geographic markets, fraud patterns that are specific to a merchant’s vertical, and unique customer and credit card data.
  • Minimize the losses due to fraudulent transactions and chargebacks. In 2018, every dollar of fraud losses cost merchants $3.13 in chargebacks, fees and merchandise replacement, so savings can add up quickly.
  • Evaluate and process more transactions faster than human analysts can.

Why a Combined Fraud Approach Works So Well

While each solution on its own can help reduce fraud, the real magic happens when merchants implement a hybrid solution that combines experienced staff who are experts in transaction investigation with advanced AI.

First, an AI solution is only as good as the data it receives. Algorithms also lack the flexibility, creativity, and intuition human experts can provide – which becomes important when evaluating orders requires a nuanced approach.

However, AI solutions can scale far more effectively than human teams can. Transactional decisions can be made by automated solutions in real time, with human analysts needing to review only transactions that have been flagged as suspicious by the AI system. The result is a seamless online ordering experience for customers that catches every fraudulent transaction but enables every legitimate transaction to be approved.

Implementing the Right Solution for Your Ecommerce Business

At ClearSale, we combine data analytics, statistical intelligence and more than 700 seasoned analysts to perform real-time evaluations on every order and offer the immediate decisions that keep your business running.

If you’re interested in learning how our unique approach can help you improve fraud protection while maximizing sales, download our “Fraud Protection Buyers Guide.” It walks you through your options and helps you ask the right questions — guaranteeing that the fraud protection solution you choose will be the one that works best for your needs.Do you know your fraud program impacts incoming orders