AI Use Case
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 - Fraud reduction
Monzo~Monzo decreased pre-paid card fraud to 0.1% and false positive rate to 25% using machine learning,NatWest~NatWest Bank prevents over £7m worth of corporate fraud by using machine learning to detect suspicious invoice payment activity,Danske Bank~Danish Danske Bank increases payment fraud detection by 60% and reduces false positives by 50% with machine learning,OCBC Bank~OCBC bank reduces number of false positive financial transaction alerts by 35% with machine learning,Chime~Chime decreases basis point loss by 40% using a machine learning fraud detection platform,American Express~American Express identifies $2 billion in potential annual incremental fraud incidents with machine learning,Lyft~Lyft delivered a 40% increase in potentially fraudulent users detected without increasing false positives by using neural networks ,Revolut~Revolut reduces bank card fraud using machine learning to detect anomalies,eBay~eBay research identifies 40% of credit card fraud with high precision automatically using machine learning,Mastercard~Mastercard achieves 11% increase in transactions approval and a reduction in fraud with the use of deep learning,Danske Bank~Danske Bank prevents card fraud with the use of machine learning,ClearBank~Clearbank combats fraud and money laundering with the use of machine learning,Western Union~Western Union reduces fraud rate to below 1.2% using machine learning models for detection
Structured / Semi-structured,Time series
ML Task - Grouping - Anomaly Detection,Machine Learning (ML),ML Task - Prediction - Regression,ML Task - Prediction - Binary Classification