Why Data Quality is Essential for Real-Time Fraud Detection
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.
Data Quality and Fraud
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…
How Fraud Detection Works
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.
Why You Need Quality Data to Catch Fraud
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.
For instance, a transaction that seems to be out of place because it takes place at a time of day when a customer does not normally make purchases could be the result of misalignment between data and time data and transaction data. Data quality tools would help you catch that error. But if you don’t catch it, you’ll waste your time – and anger your customer – by disrupting a transaction that is actually legitimate.
Worse, you can also get false negatives – which mean you overlook anomalies that are significant – if you perform anomaly detection using low-quality data. When transaction data is incomplete or out of order because of data-quality issues, your anomaly detection tools may not be able to identify abnormal activity because they don’t have enough accurate data to work with.
Enabling Real-Time Fraud Detection
Keep in mind, too, that when it comes to fraud detection, real-time results are key. By extension, the data-quality tools on which your fraud analysis depend on also need to be able to work in real time.
After all, if data-quality issues lead to false positives or false negatives in your fraud detection processes, you could sort out those data quality problems manually in order to pinpoint fraudulent transactions. But cleaning up your data by hand would take a long time – not to mention very tedious – and by the time your manually cleansed data was ready to be put to work for fraud detection, the fraudsters it would help you catch would be long gone.
Fraud Detection with Syncsort
Syncsort provides the comprehensive suite of tools that you need to enable real-time fraud detection powered by high-quality data.
Those tools include Connect for Big Data, which helps you transfer data quickly and automatically from mainframe systems that process transactions to Hadoop or other big data analytics tools that can interpret the data and catch anomalies in real time.
For more information, download Syncsort’s eBook: Detecting Fraud in Real Time with Legacy Data