Fighting Digital Payments Fraud with Deep Learning

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

Kaggle dataset:

Train Autoencoder on normal transactions and using the Autoencoder transformation there is now a clear separation between the normal and the fraudulent transactions.