Machine Learning or Manual Fraud Protection? Which Is Better?

It’s not easy for online retailers to stay one step ahead of sneaky cybercriminals and credit card fraud. But if they don’t, they can put their business (and their customers) at serious financial risk.

When it comes to implementing a fraud protection program, however, merchants have a lot of options, and the right choice isn’t always obvious. Should they rely on technology-based machine learning or human-focused manual review? Or is something even better available?

Let’s explore the options.

Using Machine Learning Technology as a Fraud Protection Solution

The machine learning approach uses computer algorithms to analyze current and historical data, fraud statistics across industries, and transactional information. Many e-commerce retailers are turning to the power of technology and artificial intelligence (AI) to evaluate a transaction’s fraud risk and flag potentially fraudulent transactions.

How Machine Learning Can Improve Fraud Protection

One of the reasons machine learning is attractive is its ability to “learn” new data and include it in its decision-making algorithms. So even when fraudsters alter their strategies for perpetrating fraud, AI picks up on these new patterns and integrates them into existing data, improving its risk-scoring algorithms. Machine learning solutions also offer merchants the ability 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 those spaces, and unique customer and credit card data.
  • Minimize losses due to fraudulent transactions and chargebacks. (LexisNexis reports every dollar of fraud losses in 2016 cost merchants $2.40 in chargebacks, fees and merchandise replacement, so savings add up quickly.)
  • Quickly evaluate and process a higher volume of transactions than human analysts can.

The Drawbacks of Relying Exclusively on AI

While technology seems like an effective, efficient way to screen credit card transactions, it’s not without its flaws. Online merchants can actually increase their false decline rate (and also reduce sales) by using inflexible algorithms that don’t consider how new data and special circumstances affect a transaction’s legitimacy — like when customers make purchases on an overseas vacation.

Machine learning can also hamper fraud detection during the period between when fraudsters develop a new tactic and the AI learns how to recognize it, leading to windows of opportunity for fraud to occur.

Using Manual Review as a Fraud Protection Solution

In contrast with the automated, hands-off approach of machine learning, manual reviews rely exclusively on in-house or outsourced teams of trained analysts to identify and prevent fraud.

How Human Reviews Can Improve Fraud Protection

Having trained staff reviewing transactions can help reduce the risk of both false declines and fraudulent transactions that are accidentally approved by:

  • Using the problem-solving knowledge and unique skills of the staff to develop and apply new rules that guide transactional decisions.
  • Letting staff identify fraud patterns specific to a merchant and implement personalized fraud screening at the client level, giving staff the opportunity to flag more suspicious transactions.
  • Giving reviewers the opportunity to immediately contact clients with flagged transactions.
  • Having experienced staff on hand who can detect subtle fraud patterns as they develop.

The Drawbacks of Relying Exclusively on Manual Reviews

While having a dedicated team for catching fraud has its benefits, it may not always be a good fit for merchants for several reasons:

  • Business owners have to hire either in-house or outsourced staff to screen every transaction, which can be expensive.
  • Having the right staffing level at all times is challenging. Merchants must ensure they have enough staff on hand during sales spikes, while still being able to reduce staffing levels when sales slow.
  • It can be more time-consuming and difficult to scale a manual review solution as a business grows and the number of transactions increases dramatically.
  • Time-consuming reviews can delay order processing, frustrating customers.
  • The effectiveness of manual reviews is only as good as the knowledge and expertise of the staff. That means extra training to ensure staff is knowledgeable about the latest cyberattacks and creating incentives to retain quality staff.

So — What’s the Best Solution?

If neither solution on its own is optimal, what’s the right answer for a growing business looking to protect itself against fraud?

Consider a hybrid solution — a sophisticated fraud prevention solution that combines machine learning algorithms and experienced staff who are experts in transaction investigation. Human analysts add their transaction data to the extensive data sets, making the AI component smarter and more effective. Transactional decisions can be made in real time, with human analysts needing to review only transactions that have been flagged by the AI system. The result is a seamless online ordering experience for customers that still slashes the risk of approving fraudulent transactions — increasing security and sales.

When you’re looking for the best protection against fraud and lost business, consider implementing a solution like ClearSale’s. Our unique approach combines leading-edge automated fraud detection and a large in-house CNP fraud department (boasting more than 700 seasoned analysts) to deliver the solutions that let merchants take back control of their businesses.

Still not sure which fraud protection solution is right for your business? Our “Fraud Protection Buyers Guide” 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.ClearSale Fraud Protection Buyers Guide