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Use CaseFinancial Services / Banking

Identify fraudulent activity using unusual payment transaction patterns and other data

Analysing payment transactions to flag potential fraudulent activity - which is typically automatically blocked and usually requires human intervention to then unblock.

£

Cost

Cost - Fraud reduction

Description

Identify fraudulent activity using unusual payment transaction patterns and other data

Technology & data

*** check categry and function

Linked Case Studies

OCBC bank reduces number of false positive financial transaction alerts by 35% with machine learning

Revolut reduces bank card fraud using machine learning to detect anomalies

Danske Bank prevents card fraud with the use of machine learning

Monzo decreased pre-paid card fraud to 0.1% and false positive rate to 25% using machine learning

Chime decreases basis point loss by 40% using a machine learning fraud detection platform

eBay research identifies 40% of credit card fraud with high precision automatically using machine learning

Lyft delivered a 40% increase in potentially fraudulent users detected without increasing false positives by using neural networks

NatWest Bank prevents over £7m worth of corporate fraud by using machine learning to detect suspicious invoice payment activity

Western Union reduces fraud rate to below 1.2% using machine learning models for detection

American Express

American Express identifies $2 billion in potential annual incremental fraud incidents with machine learning

Danish Danske Bank increases payment fraud detection by 60% and reduces false positives by 50% with machine learning

Clearbank combats fraud and money laundering with the use of machine learning

Mastercard achieves 11% increase in transactions approval and a reduction in fraud with the use of deep learning

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Editor: sdg