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FICO® Falcon® Fraud Manager

Prevent, detect, and manage fraud across the enterprise.

Overview

FICO® Falcon® Fraud Manager

As the foundation of the FICO® Falcon® Platform, FICO® Falcon® Fraud Manager provides core analytic processing power from artificial intelligence to handle an organization’s transactional fraud detection needs such as debt, credit, deposit, ePayments and mobile. It can be used to process events, develop strategies to detect fraud and create cases, and execute associated decisioning across an institution’s products, channels and customers.

FICO® Falcon® Fraud Manager provides deep insight into fraud trends and activity, available on-premises or in the cloud. Powered by FICO’s market-leading predictive analytics, it can adapt to changing fraud trends to detect up to 50% more fraud than rules-based systems.

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Core Benefits

Analytics across channels

Lower false positives

Shared data provides more insight

Analytics across channels

FICO® Falcon® Fraud Manger can protect payment cards and non-card electronic payments (such as ACH, wire, person to person), as well as deposit and other online/mobile banking activity.

Lower false positives

FICO's industry-leading analytics expertise translates into lower false positive rates, minimizing unnecessary customer disruptions. 

Shared data provides more insight

When any or all Platform Modules are deployed, FICO® Fraud Manager  allows data to be shared between them. In this way, FICO's core technology can handle all of an organization’s transactional fraud detection needs in a highly integrated fashion.

Solution Architecture

FICO® Falcon® Fraud Manager

FICO® Falcon® Fraud Manager, at the core of the FICO® Falcon® Platform, provides transaction event monitoring and decisioning technology that allows institutions to implement fraud protection with an end-to-end, holistic approach. Its comprehensive core functionality that can easily be extended with add-on and custom capabilities, as well as integrated with back-end payment monitoring and front-end authentication solutions.

Customers

It's not just who we work with, it's how well we work together.

Key Features

  • Analyze big data. FICO's behavioral analytics examine big data to extract behavior patterns across millions of transactions, to continuously update “behavioral profiles” for individual customers.
  • Learn in real-time.  FICO® Falcon®  Fraud Manager introduces adaptive analytics with models that learn in real time from case dispositions - either consortium or custom - focusing on flash fraud experienced within a portfolio. 

 

  • Detect more fraud. FICO® Falcon®  Fraud Manager leverages FICO’s proven leadership in predictive analytics to deliver real-time, end-to-end fraud, security and compliance management –– up to 50% more than rules-based systems.
  • Greater Customer Experience. FICO's Behavior Sorted Lists use sophisticated algorithms to further distinguish normal activity from suspicious transactions, reducing false positives.
  • Integrate merchant risk data.  FICO® Fraud Predictor with Merchant Profiles combines the capabilities of FICO® Falcon® Fraud Manager with merchant data for even greater fraud detection. 
  • Discover new fraud patterns as they surface. FICO’s self-learning, adaptive analytic models improve sensitivity to emergent fraud patterns. These advanced analytics don’t rely on historical data. 

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Hong Leong outsmarts fraudsters with FICO® Falcon® Fraud Manager

Credit card fraudsters are an inventive bunch. They change their tactics constantly, using both old fashioned methods (stealing credit cards) and new technology (counterfeit cards) to carry out fraudulent credit card transactions. Keeping up with their latest tactics isn’t easy. “When Malaysia moved to chip and PIN in 2005, many banks and card-issuing institutions were caught off guard by the quick change in fraud tactics,” says John. “At Hong Leong, we too saw a discernable change, with Internet fraud increasing considerably.” 

Learn more how FICO partnered with Hong Leong to fight in-person counterfeit fraud in this case study.