Big Data and Fraud Detection
Online payment fraud is a growing issue in our digital world. Businesses lose billions per year because of online payment fraud. It’s important to strike a perfect balance. Taking the appropriate measures to combat fraud, while minimizing friction for users. This is where fraud detection and prevention systems come in, taking large amounts of data, analyzing and discovering patterns to drive decisions in real-time.
Fraud analytics systems are built to analyze large volumes of historical data to produce insight and make predictions. Many organizations perform manual reviews, a laborious approach with a higher likeliness of errors than an automated strategy. To keep with the growing threat of fraud, organizations must be willing to take the next step into automated fraud prevention.
To make accurate assessments on which cases are potential fraud and create accurate risk profiles, fraud systems rely on numerous micro-decisions. These decisions are made by gathering data in real-time on the customer, evaluating and monitoring customer data against patterns in real-time.
Putting to use customer data, fraud systems work to detect and prevent fraud, flagging suspicious activities utilizing analytic calculations and algorithms. Fraud systems can analyze and learn with more data, only improving accuracy rates in detecting fraud at a near-instant speed. An added core benefit of fraud systems is its delivery of low false positives.
How Fraud Systems Use Big Data:
- Gather information from mobile devices such as geolocation and device details combined with customer profile and account holder to asses the risk of the transaction in real-time.
- Risk-decision making is made based on IP address, fingerprint data, device fingerprints, and browser.
- Fraud systems can create a behavioral profile based on the data collected (geolocation, trust scores, device profiling, etc.) to asses what behaviors are indicators of potential fraud.