Payment Fraud Analytics - Dashboard Examples
Suspicious payment fraud transactions – When is excessive fraud activity occurring?
Investigate high velocity payment transaction volume patterns at an ATM using real-time fraud analytics. Identify patterns which indicate fraud such as when a card is being used at multiple locations simultaneously or when a card or customer ID is used for many high value payment transactions, in a short amount of time, at the same device or application.
EMV FALLBACKS – When are SIGNIFICANT FALLBACK Payment TRANSACTIONS or stand-in modes occurring?
Review merchant EMV fallback payments and high reversal patterns using fraud analytics. Investigate in real-time whether these are occurring due to an incorrectly configured chip reader terminal or a defective chip card. Avoid liability and shut down potential payment card fraud using a combination of real-time visualization, predictive algorithms and machine learning capabilities.
Consolidated card usage – Which customers are showcasing abnormal behaviors that indicate potential payment fraud?
Use real-time fraud analytics to flag suspicious card usage such as a higher amount of card or account activity happening across payment channels. Build out a consolidated view of all payment transactions performed by a specific card or customer ID, and map these occurrences by location and time of day to help pinpoint fraud using a real-time payment fraud monitoring analytics.
Money laundering – When are unusually large withdrawals or deposits occurring?
Use real-time fraud detection analytics to investigate unusually large deposits or withdrawals. Isolate when a specific ATM device, card or customer ID experiences higher than normal payment transaction values. Detect when high-value payments are occurring using a higher-risk product service, such as a money order or bank draft in real-time.
Customer activity – Are there an abundant number of refunds being performed?
Gain access to analytics that helps segment the most active customers based on payment volumes, activity and card types. Are there an abundant number of refunds or reversals being performed that could be related to detecting payment fraud? Investigate why using real-time fraud prevention analytics.