How we select the right business data for feature engineering

Traditional features

These are the typical aspects that predict fraud, for example orders, transactions, cards, location, email. These features generally cover the data you would expect to find on your receipt and are customer-centric.

Behavioral features

We derive behavioral features from the customer session - these are features are based on describing the customer actions eg. velocity of orders, time spent on the page, length of time between adding a new card and making an order. One purpose of extracting these features is to capture other subversive technology use eg. if a fraudster is using a script to scrape a webpage vs normal browsing activity.

Real-time features

Real-time features are based on the up to date, real-world incidences of fraud. These features are all based on categorical data - give the real-time rate of fraud by category eg. country / ASN card digits / email domain etc. An example feature could be the fraud rate in certain regions/countries.

One purpose of these features is to help merchants to expand into new markets where they have no existing data. We monitor the real-time traffic to help our merchants seamlessly move into new markets, without seeing any adverse effects from the machine learning models eg. bias.

Individual customer features

These features tell us about the similarity with the specific customer’s typical past behavior. This could be their typical spend, their regular billing address, home IP address etc.

Session tracking features

These features are a little more involved than the behavioral features. These features cover the data we get from Javascript eg. whether the customer is pasting a card number into the checkout, cookies, if they are using a password vault etc. One purpose of these features is to capture genuine customer behavior eg. taking time to change the size of a piece of clothing.

Entity features

We divide features into customer-centric and entity-centric. Entities are things like devices, addresses, locations, domains and emails. An example feature is the number of orders shipped to a certain address. One purpose of these features is to alert us to a fraud goods drop-off point.

Network-derived features

As well as customer-centric and entity-centric features, we also look for network level features. These features focus on network topology (network shape) as a means of enhancing our customer data. An example is account sharing between a family in the same house vs. account takeover where networks of hundreds of accounts use the same few devices.