Fighting against payments fraud is a balancing act. If you’re not diligent enough, it’s exceedingly easy for criminals to commit financial fraud. But too tight a hold on the reins can cause you to miss out on legitimate transactions, which can harm customer satisfaction as well as your bottom line.
The breadth of data that businesses have access to can be both a blessing and a curse, as the information you’re looking for is likely buried under a mountain of other data. Using this data to curb fraud effectively means fighting smarter, not harder, with tools and techniques that help you understand, identify and curtail fraud.
Types of Data
There are two types of data. Structured data is information that fits neatly into database fields, such as dates, credit card numbers, names, locations, device types, order numbers, phone numbers and Social Security numbers. It’s usually singular and can be fully understood without relying on other data. Unstructured data, on the other hand, consists of things like text documents, web pages, social media posts, emails, customer service logs and audio files, as well as information such as how many customers are using a particular IP address and web traffic origins. It’s often made up of structured data elements, as in a document that contains names and email addresses, but doesn’t itself fit neatly into a single database field.
Both kinds of data are important to helping you understand what’s normal so you can spot uncharacteristic activity that could point to fraud.
Using Data
Having a broad view of the data contained in each transaction can help your company leverage this information to reduce payments fraud, but that’s only part of the solution. There are three additional elements that your fraud-fighting strategy needs to employ.
Connecting the Dots
Combining different data points can provide better context for transactions and help you identify fraud more accurately. This means matching changes in structured data, such as an address or phone number, with unstructured data, such as an immediate request via customer service for a large transfer to an unfamiliar account or a replacement card. Neither of these events is suspicious alone, but together, they help detect credit card fraud.
Establishing Patterns
Learning the typical behaviors and activities that users carry out is key to identifying fraud. Machine learning is extremely helpful here, allowing companies to forgo rules-based fraud detection systems—that are often either too strict or too lenient—in favor of models of behavior and activity that become increasingly accurate as time goes on.
Real-Time Data Forensics
Machine learning models use real-time predictive analytics to assign scores that point to the possibility of fraud, in contrast to the definitive “yes/no” conclusions provided by rules-based systems. Combining these models with custom rule management can enable you to make more precise determinations.
Mitigating Revenue Loss
Research has found that organizations lose 5 percent of their revenue to fraud each year. This technology can thus play a huge role in reducing this revenue loss, not only from the losses via chargebacks and other reimbursement but from the costs of hiring multiple, experienced fraud analysts as well as other related costs. In fact, research finds that proper fraud management can provide 150 to 200-percent ROI over five years.
