Securing Your Business with a Fraud Prevention Solution

Finance companies have recorded a drastic increase in attempted credit card fraud since the beginning of the pandemic.

Only in 2020, the FTC reported over two million fraud cases involving American consumers, which resulted in a loss of $3.3 billion.

This trend is only going to continue, which is why companies can’t afford to disregard the importance of fraud detection.

Rules-Powered Anti-Fraud Tools

Business rules are a proven way of fighting fraud and lowering risk. A rule is a conditional “If-Then” statement that when satisfied, can label a transaction as fraudulent.

Fraud prevention software solutions that use business rules to examine a variety of uncommon factors — such as timestamps, unusual locations, large transaction amounts, and locations — to spot fraudulent behavior.

Rules-powered fraud prevention software tools are not difficult to understand since they consist of a set of conditional statements, which dramatically speed up the development process.

This is particularly important when companies need to oversee a large number of transactions.

Advantages of Implementing a Rules-Driven Anti-Fraud Tool

There are many important reasons why companies elect to safeguard their data with a rules-based fraud prevention solution.

Efficacy and Productivity

Rules-based fraud prevention solutions are easy to install and set up. An anti-fraud tool that uses a business rules engine is able to examine transactions in real-time and spot potentially fraudulent activity.

Immediate Response

Fraud detection tools enable non-technical users to instantly write new rules once fresh fraud trends appear.

For example, if an organization tracks an attack back to a precise location, then the fraud department can instantly block transactions from that area.

Fraud Detection in Real-Time

Here are some of the most common fraud cases that a rules-based fraud detection software system can spot with ease.

Location-Based Threats

Location-based factors are useful indicators of fraudulent activity. For instance, a charge late at night that took place in a convenience store in a different state from the card’s billing address will be flagged.

Suspicious Transactions

When a customer with the same IP address opens multiple accounts and proceeds to make large payments.

Using Machine Learning to Fight Fraud

Machine learning platforms are more sophisticated and complex than rules-based solutions.

Machine learning apps can uncover underlying links between variables like no other software solution. They are able to spot complex correlations that are impossible for a human to identify.

Having said that, it’s important to note that large volumes of data are necessary to uncover these hidden patterns. Without analyzing a large number of fraud cases, machine learning algorithms can’t learn to spot fraudulent activity.

As a result, companies that use machine learning have to contend with lengthy development cycles, expensive maintenance, slower implementation, and costly maintenance.

Black Box

Companies need to be able to understand why a particular transaction was labeled as fraudulent. However, over time, machine learning platforms don’t provide this necessary transparency. Therefore, it becomes almost impossible for the fraud department to understand why a machine learning system flagged a transaction.

Fight Fraud with Rules

Anti-Fraud systems that use business rules enable companies to immediately react to threats. Once a new threat has been uncovered, the fraud department has all the necessary software tools to protect your organization.