Fraud Prevention with Rules and Decisions

Financial institutions have been hit by a huge surge in attempted credit card fraud in the last couple of years. There have been over 2.2 million fraud cases in the U.S. alone in 2020, which resulted in over $3 billion dollars worth of damages!

Rates of documented fraud continue to be at an all-time high. Therefore, businesses across industries need to find effective solutions to the threat that fraudulent behavior poses.

Fraud Detection Tool

Rules are a tried and tested way of creating protection from fraudulent behavior and limiting risk. A business rule is a conditional statement that can be used to write anti-fraud software.

A fraud prevention solution that runs rules examines a collection of uncommon factors, such as odd locations or large transaction amounts to spot patterns of fraud.

When these suspicious behaviors are spotted, the anti-fraud solution flags them which allows a human fraud manager to examine the case further.

One of the reasons why companies use rules-based fraud prevention tools is the fact they are easy and quick to implement, which is critically important when examining a large volume of transactions.

The Benefits of a Fraud Detection Tool Powered by Business Rules

Let’s take a closer look at why companies of various sizes and across industries opt to safeguard their operations with a rules-based fraud prevention solution.

Performance

Fraud prevention systems that run on business rules are incredibly fast and installing them is straightforward. Business rules engines that power the most efficient anti-fraud systems are able to examine all transactions in seconds and identify suspicious ones.

Immediate Response

Anti-fraud tools based on rules engines enable subject matter experts to immediately write new rules that fight against newly discovered fraudulent behavior.

Frequency and Suspicious Behavior

There are many examples of suspicious behavior. For instance, If a bank account that’s generally inactive out of nowhere starts making transactions, then this might be a cause for concern.

Deploying Machine Learning to Fight Fraud

Machine learning tools are definitely more sophisticated than rules-powered anti-fraud systems.

One of the features of machine learning that sets it apart is the capability of identifying hidden connections and patterns in datasets that would be next to impossible for a human to see.

The Black Box

Companies need to have a clear understanding as to why a particular transaction or event was marked as suspicious. However, this presents a problem for machine learning tools because their decision-making procedures lack transparency. Simply put, it’s difficult for fraud managers to figure out the reasoning behind certain actions taken by a machine learning solution.

A Rules-Based Fraud Detection Tool is the Answer

Another major issue with machine learning systems is that they use datasets that are approximately three months old to make decisions. Clearly, this makes it hard to identify the newest fraud trends if you have to rely on old information.

Conversely, a fraud prevention tool that uses a business rules engine enables businesses to simply add the necessary safeguards as soon as new threats emerge.

Therefore, the moment a new threat is discovered, a company’s anti-fraud team can use a business rules engine to react.