Machine Learning is changing the ways industries do business - Healthcare, Manufacturing, Retail and Food and Beverage will never be the same again. Banking and Finance are also impacted by this innovation, and one of the most prominent uses is for Credit Card Fraud Detection. In this guide, I will go deep, and explain how it works.
Unauthorized card operations hit an astonishing amount of 16.7 million victims in 2017. Additionally, as reported by the Federal Trade Commission (FTC), the number of credit card fraud claims in 2017 was 40% higher than the previous year’s number. There were around 13,000 reported cases in California and 8,000 in Florida, which are the largest states per capita for such type of crime. The amount of money at stake will exceed approximately $30 billion by 2020.
Machine Learning-based Fraud Detection:
Conventional Fraud Detection:
“Fraud detection is a set of activities that are taken to prevent money or property from being obtained through false pretenses.”
Fraud can be committed in different ways and in many industries. The majority of detection methods combine a variety of datasets to form a connected overview of both valid and non-valid payment data to make a decision. This decision must consider IP address, geolocation, device identification, “BIN” data, global latitude/longitude, historic transaction patterns, and the actual transaction information. In practice, this means that merchants and issuers deploy analytically based responses that use internal and external data to apply a set of business rules or analytical algorithms to detect fraud.
Credit Card Fraud Detection with Machine Learning is a process of data investigation by a Data Science team and the development of a model that will provide the best results in revealing and preventing fraudulent transactions. This is achieved through bringing together all meaningful features of card users’ transactions, such as Date, User Zone, Product Category, Amount, Provider, Client’s Behavioral Patterns, etc. The information is then run through a subtly trained model that finds patterns and rules so that it can classify whether a transaction is fraudulent or is legitimate.