Real-Time Fraud Detection: Why Data Quality is Essential
Which weapons do you store in your fraud-detection arsenal? If data-quality tools aren’t among them, then you’re not doing all you can to catch and disrupt fraudulent transactions. Here’s why data quality is essential for fraud detection.
Data quality – which means the art and science of identifying and fixing inaccurate, missing or misleading information within a body of information – is important for many reasons.
It ensures consistency. It helps you catch false positives or inaccurate outliers that could undercut the accuracy of the conclusions you draw from data. It maximizes your visibility into data sets by eliminating errors or incomplete information that creates uncertainty.
But those aren’t the only things data quality can do for you.
Here’s another, crucial goal that data quality can help you achieve: Fraud detection.
Why? Put simply, because data quality eliminates false anomalies within your data so that you are better equipped to identify true anomalies – the kind that often signal a fraudulent transaction.
That’s the high-level explanation. To get to the core of why data quality is so essential for reliable fraud detection, let’s dive a little deeper into the processes at play here…
A key principle of fraud detection, especially when you’re relying on data to detect fraud, is that outliers within your dataset are often an indicator of fraudulent activity. For example, if your data analytics reveal a set of transactions that are at odds with the normal purchasing patterns of a given customer, there’s a good chance someone else has fraudulently swiped that customer’s credit card or made a fraudulent purchase in his or her name.
By catching this anomaly, you can find the fraud quickly – ideally, in real time, so that the transaction can be canceled before it is even completed.
But if your data is of low quality, attempts to detect fraud by finding anomalies are likely to turn up false positives more often than they reveal true fraud.
