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Data breaches have given criminals access to more personal information than ever before. Armed with this information, they steal real peoples’ identities or construct synthetic identities. The move to a digital economy, where applications often happen online, means little face-to-face interaction. This gives fraudsters the ability to hide behind stolen or fabricated identities. They use these identities to open accounts and access money, goods and services they have no intention of repaying. If application fraud is not prevented, it quickly:
It is vital to stop fraudsters at point of origination. Decisions driven by machine learning analytics and fuzzy matching ensure businesses can say yes to more of their legitimate customers while turning away the fraudsters. FICO® Application Fraud Manager is designed to:
Tackling fraud rings
Brute force application fraud attacks are a new norm. Perpetrated by organized criminals, a sustained attack sees losses quickly multiply – millions lost in the matter of days. The attacks are both systemic and methodical, but the most common identifying features are velocity and recycled personally identifying information. Spotting repeated information – whether exact or fuzzy - uncovers the wider web of data that strings together a criminal enterprise. FICO helps you automate this to:
Proactively uncover fraud rings with graph analytics.
Stop fraud at the point of origination.
Cyber attacks and fraud continue to make headlines so, unsurprisingly, a vendor driving change in this globally important area has won the RiskTech100 ® Innovation award for a second consecutive year. FICO’s win reflects its focus on issui...
FICO commissioned an independent research study by TM Forum to look at how global telecommunication providers are using (and plan to use) machine learning and advanced analytics to improve the customer experience in credit risk and beyond....
The previous white paper in this series, Open Banking: Multi- Layered Self-Calibrating (MLSC) models, discussed the use of self-calibrating/semi-supervised machine learning (ML) models for open banking. Regardless of the ML approach, monit...
In an always on, digital environment, those involved in preventing financial crime are sometimes seen as an impediment to providing a smooth customer journey. Security and financial crime checks are vital to protect both customers and the ...