To understand how predictive data analytics can help improve fraud detection strategies, you must first understand the meaning of predictive data analytics. The goal of predictive data analytics is to use a large amount of historical data, along with statistical algorithms to determine the likelihood of various possible future outcomes. This includes using machine learning techniques to process data much faster than humans could do on their own. So, the result is not just knowing what has already happened but having a best guess of what will happen in the future.
Fraud can cost companies vast sums of revenue, and the longer fraudulent activity goes on, the more difficult it is to recover from it. Early detection is key to discovering and stopping fraud. Because predictive data analytics involves a massive amount of data, any anomalies that occur can be easier to spot. The computer systems used learn what type of transactions are normal and can both track trends and alert users to potential problems.
Common types of fraud that can be detected through predictive data analytics include tax fraud, pharmaceutical fraud and credit card or banking fraud. The IRS can assess the reliability of tax returns, using historical information about your returns and comparing it to your most recent one. Pharmacy computer systems monitor the frequency that prescriptions are filled, looking for anomalies in the data. Fraudulent charges can be reduced by credit card companies and banks that are always on the lookout for charges that stray from your normal spending habits.