Interesting presentation today at the DataScience SG meet-up
Conventional fraud prevention methods are rule based, expansive and slow to implement
Q1 2016: $5 of every $100 subject to fraud attack!
Key fraud types: account takeover, friendly fraud & fraud due to stolen card information
Consumers want: easy, instant, customized, mobile and dynamic options to suit their personal situation. Consumers do NOT want to be part of the fraud detection process.
Key technology enablers:
Historically fraud detection systems have relied on rues hand-curated by fraud experts to catch fraudulent activity.
An auto-encoder is a neural network trained to reconstruct its inputs, which forces a hidden layer to try and to learn good representations of the input
Train Autoencoder on normal transactions and using the Autoencoder transformation there is now a clear separation between the normal and the fraudulent transactions.