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My thoughts on Credit Card Fraud Detection with Machine Learning

Artificial Intelligence could change a lot of aspects in various industries - it can save lives in Healthcare, make more sales in Retail, and, of course, provide never before seen levels of security. Banking and Finance are one of the primary industries that are interested in eliminating financial fraud, learn how Machine Learning could help.

What is Credit Card 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.

What is the difference between ML Credit Card Fraud Detection and Conventional Fraud Detection?
Machine Learning-based Fraud Detection:

Detecting fraud automatically
- Real-time streaming
- Less time needed for verification methods
- Identifying hidden correlations in data
- Conventional Fraud Detection:

The rules of making a decision on determining schemes should be set manually.
- Takes an enormous amount of time
- Multiple verification methods are needed; thus, inconvenient for the user
- Finds only obvious fraud activity

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