You already know that Big Data analytics can deliver business insights and improve operations. But have you thought about the role that analytics can play in detecting fraud? If not, this article is here to remind you by describing several examples of how you can use data to help defeat fraud.
When I say fraud, I mean any kind of fraudulent transactions or intrusive activity that harms your business or your customers.
Traditionally, organizations detected fraud through manual, time-consuming processes. They relied on audits and employee insights to catch activity that was out of the ordinary and linked to fraud.
Detecting fraud used to be difficult and time consuming, but that’s not the case when you leverage data analytics.
Detecting Fraud with Data
With the use of data analytics, however, much more sophisticated and efficient forms of detecting fraud become possible.
Following are specific ways in which your business can use data to detect, counteract and prevent fraud:
- By analyzing large amounts of data regarding events like customer transactions that you are confident to be free of fraud, you can establish a baseline for healthy activity. You can then compare that baseline to real-time analytics data in order to pinpoint anomalies that signal fraud.
- You can analyze data that you know to be associated with fraud in order to gain a better understanding of the nature of the fraud itself. For example, analytics on fraudulent data can help you determine which times of day are the likeliest to coincide with fraud, where fraudulent transactions originate and which types of accounts are likeliest to be targeted for fraud.
- Analytics can help you find errors in your data that are the result not of fraud, but simply of inconsistencies or other problems within the data itself. Detecting data errors helps prevent fraud because attackers might attempt to exploit inconsistencies to develop entry points into your systems. For example, they might find that some customer records are mismatched, then make calls posing as the customers in an attempt to phish more private information out of your databases.
- Analytics help you collect metrics in order to measure the effectiveness of your anti-fraud efforts. As with any kind of investment of time and resources in your business, you want to know whether the work you’re doing to fight fraud is paying off. Measuring the frequency and types of fraud, and how that data changes over time, is a good way to collect metrics that will allow you to trace the effectiveness of your actions.
- With real-time analytics, you can stop fraud in its tracks. For instance, if you run analytics on payment transactions in real time, you can use anomaly detection to identify fraud as a transaction is in process, then stop the transaction before it completes. Stopping an attack before it reaches its conclusion is much more valuable than waiting until the thieves have made off with the goods to find out about the intrusion.
This list isn’t exhaustive. There are many other creative ways that data can assist in fighting fraud. An example is machine learning, which is fast becoming the new frontier of anti-fraud activity. Machine learning uses automated data analysis and response features to detect and stop fraud in real time or to plug holes automatically that could facilitate fraud.
Fraud Detection and Syncsort
If you want to make the most of your data analytics resources when combating fraud, you need to be sure that you’re putting all of your data to use and analyzing it using the most efficient and modern platforms. Syncsort’s Big Data solutions help you do that. They allow you to access data from any source, in any format – even data that exists in obscure formats on your mainframe systems – and integrate it into modern analytics environments like Hadoop.
And because you can leverage this capability to keep fresh data consistently accessible to your data lake, Syncsort maximizes your ability to detect and stop fraud in real-time before the effects are felt.
Download our eBook “Mainframe Meets Machine Learning” to learn the challenges and issues facing mainframes today, and how the benefits of machine learning could help alleviate some of these issues.