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5 Ways to Detect Fraud with Data

Everyone already understands analyzing Big Data provides business insights to improve operations, but what about using analytics to detect fraud? If this is something that you’ve not considered, then keep on reading to see examples of how data helps to detect – and protect against – fraud.
Organizations traditionally detect fraud using manual processes that can take a lot of time. This would be things such as employee insights and audits to spot any unusual behavior that could be connected for fraud.
Using Data to Detect Fraud
By making use of data analytics it becomes possible to put together efficient and effective fraud detection systems. Here are some of the ways that a business can detect, prevent, and fight against fraud using data:
  • Analyzing large quantities of data for things such as customer transactions that are legitimate allows you to establish a baseline of healthy customer activity. This baseline can then be used with real-time analytics to hone in on an anomaly that could an indication of fraud.
  • Analyzing data known to be related to fraud gives you an insight about fraud in and of itself. Analyzing fraudulent data for example gives you an idea of when fraud is most likely to happen, where the fraudulent transactions came from, and the kind of account that is most susceptible to fraud.
  • Analytics allow you to notice errors in the data that aren’t the result of fraud, rather just an inconsistency or a problem with the data itself. Being able to detect such data errors helps to prevent fraud because an attack may try to use these inconsistencies to develop an entry point into the system. They may discover that there are mismatched customer records for example, and then make a call posing as the customer to phish private information out of the database.
  • Analytics allows you to collect metrics to measure how effective your anti-fraud efforts are. As is the case with investing time and money into your business, you want to be sure that what your efforts to prevent fraud are succeeding. Measuring the kinds and frequencies of fraud, and how this data changes over time, is a great way to collect metrics and measure how effective your actions are.
  • Real-time analytics enable you to stop fraud before it has a chance to become serious. Running real-time analytics on payment transactions enables you to use anomaly detection to spot fraud during the transaction, stopping the transaction before it goes through. Stopping fraud before it can complete is much better than being unable to detect the intrusion until after a thief has what they want.
This is by no means an exhaustive list. There are plenty of creative ways using data can help you spot and fight fraud, such as machine learning; the latest advancement in the field of fraud prevention. Machine learning makes use of automated data analysis and other features to find and prevent fraud in real time, as well as plugging up the holes that fraud can slip through.