How Machine Learning Can Improve Fraud Risk Assessment

When you’re trying to prevent fraud from happening, you might be tempted to set up a basic fraud prevention solution and let it go, assuming it will catch each instance of fraud on its own.

While that hands-off approach sounds appealing, it just isn’t enough to protect merchants against emerging types of fraud — especially with today’s increasingly savvy fraudsters.

But integrating machine learning into your solution can help you improve your transaction scoring and decrease your risk for fraud. How? It comes down to risk assessment.

The Two Critical Components of a Fraud Prevention Solution

If you want to maximize your fraud prevention program, you should pay close attention to two critical components:

  • The first is putting a robust risk assessment plan in place that lets you quickly and accurately evaluate a transaction for risk. Once you know where a transaction falls on the risk spectrum, you then know how to proceed with a transaction, whether it’s approving it, declining it or requesting a secondary level of authentication.
  • The second is deciding how to proceed with flagged orders. Moving forward with low-risk orders is easy: Approve the order, and let it go. But high-risk orders are a different story. With these transactions, you’ll need to decide what level of risk you’re comfortable accepting.

So how can you ensure your risk assessment plan is correctly capturing and evaluating flagged orders?

The Role of Rules in Risk Assessment

Over the years, how a merchant handles risk assessment and flagged orders has changed dramatically.

It used to be that these were solely rule-based decisions: Merchants would decide whether to approve or reject an order based on logical statements that classify each order. Some merchants might base their rules on whether an order was more or less than $300 or whether the shipping address was in a high- or low-risk ZIP code.

But the rules could also be far more complex, like those that evaluate whether an email address has been used with the same credit card for at least six months without a chargeback. These increasingly complex rules can bolster your confidence in correctly evaluating the legitimacy of a transaction.

But here’s the catch: The more complex you make the rules and the more rules you have, the harder it is to manage them.

Sometimes one transaction can be subject to multiple rules — all with a different action trigger. Because each rule is looking at different elements of an order, you may find that one tells you to decline an order while another tells you to automatically approve it. And still another rule may suggest a manual review is needed.

One order, following three different rules, with three different outcomes. It’s no wonder that fraud prevention has become so complicated.

This process is not only complex, but risky. Putting the wrong rule at the top of your hierarchy can not only invalidate subsequent rules, but it can also cause good transactions to be rejected and fraudulent ones to be approved.

How Machine Learning Can Improve Risk Assessment

While this rules-based approach has been the standard since the early 2000s, a machine learning approach to risk assessment has skyrocketed in popularity.

Now, instead of those rules just automatically triggering an approval or a rejection, merchants can assign a weight to each of them. With this weighting, some rules have a larger impact than others on a transaction decision. Some add points to a final score; others subtract them. The process is not unlike how different variables combine for an individual’s credit score.

Merchants appreciate that these variables can be combined in different weights to create a final score. So now, instead of making a decision based on each individual rule (or variable), you can identify a score threshold and make the transactional decision based on that final score. If you have a score range of 0-1,000, for example, you can set a threshold of 800. Any fraud score higher than 800 results in a declined transaction. Any scores lower than that will be approved.

This helps simplify fraud scoring, because you no longer have to worry about the order in which fraud rules were applied. Instead, the rules are applied simultaneously, with all factors being considered based on their relative importance, giving you an even more accurate idea of how risky a particular order is.

Building a Historical Database

But even this technique is evolving. As you continue to apply this fraud scoring, you can build a historical database that lets you fine-tune the weights you give each variable.

Once you have a year of transactions under your belt, you probably have a good understanding of which transactions were good, which ones resulted in chargebacks, and which ones the manual reviewers decided were fraudulent and rejected. This extensive background information lets fraud prevention systems review and optimize their scores regularly to ensure that each variable is given its proper weight. And this reoptimization isn’t arbitrary. Instead, it comes directly from an algorithm that analyzes the data you input, making your solution more robust, more scalable and more accurate.

At ClearSale, we know that even with the capabilities machine learning offers, having trained staff reviewing transactions can help reduce the risk of both false declines and fraudulent transactions.

Our highly skilled and trained analysts add their transaction data to the extensive data sets, making the machine learning component of our fraud prevention solution smarter and more effective. That means we can make transactional decisions in real time, since our human analysts need to review only transactions that have been flagged by the machine learning system.

The result? You benefit from a seamless online ordering experience for customers that dramatically decreases the risk of approving fraudulent transactions while simultaneously increasing security and sales.

If you’re interested in learning more about this approach to fraud prevention, download our “Fraud Protection Buyers Guide.” It walks you through your options and helps ensure you pick the right fraud protection solution for your needs.

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