Description
Detect potentially fraudulent or nefarious users
Technology & data
*** check categry and function
Linked Case Studies
Case Study
Spanish National Police
Spanish National Police identifies false robbery reports with over 80% accuracy using machine learning and natural language processing
Case Study
US Department of Homeland Security
The identifies impostors at airports with facial recognition technology
Case Study
Earthport
Earthport Payment Network reduces false positives of automated suspicious transaction detection using AML risk data in real-time
Case Study
HSBC
HSBC to implement Quantexa''s AI software to aid with compliance in identifying illegal and fraudulent customer profiles
Case Study
Danske Bank
Danske Bank prevents card fraud with the use of machine learning
Case Study
Chime
Chime decreases basis point loss by 40% using a machine learning fraud detection platform
Case Study
Soter Technologies
US and Canada schools achieve a 70% decrease in students vaping at bathrooms with the use of machine learning
Case Study
Government of the United Kingdom
The UK government identifies welfare and state benefits fraud with artificial intelligence
Case Study
Bank of the West
Bank of the West announced implementation of Pindrop''s machine learning fraud detection software in its call centres
Case Study
Holvi Payment Services
Holvi reduces time spent investigating false positives for customer risk using AI platform ComplyAdvantage
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Editor: sdg