Identify and eliminate application and online fraud. Address the proliferation of identity theft and synthetic identities.
Detect more fraud, and reduce your losses.
Our unique, hybrid analytic approach uses multiple techniques – automated business rules, predictive modeling, text mining, database searches, exception reporting, network link analysis, etc. – to spot more suspicious activity with greater accuracy. A powerful fraud analytics engine uses embedded AI and machine learning techniques, combined with traditional detection methods, to process all data (not just a sample) in real time or in batch, to find more fraud and reduce false positives. And by analyzing device data in conjunction with demographic data, you can quickly uncover application and online banking fraud.
Get a consolidated view of fraud risk.
Identify cross-brand/product fraud by viewing customer accounts and transactions for all lines of business in one consolidated view. A unique visualization interface lets you spot linked entities and banking fraud crime rings that you would otherwise miss. Social network diagrams and sophisticated data mining capabilities enable you to better understand new banking fraud threats, so you can prevent substantial losses early. And stay on top of changing fraud trends by continuously improving models and adapting the system.
Boost investigator efficiency, reduce false positives and gain a competitive advantage.
The solution applies risk- and value-based scoring models to accurately score and prioritize alerts before they go to investigators. With the time saved, investigators can work more cases with greater efficiency, and focus on higher-value networks that generate a better ROI. More accurate scoring also means fewer false positives – and that translates to less customer inconvenience and greater customer satisfaction. You can also boost the efficiency of collection processes by identifying banking fraud losses that result from synthetic identities and have little to no chance of recovery.
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