Fraud Detection: How AI and ML help to fight criminals

Fraud attacks have become much more sophisticated. To combat the risks, fraud detection solutions have to be able to react quickly. Application of AI and Machine Learning allows keeping pace with fraudsters, increasing efficiency, reducing headcount in compliance and providing better customer experience.

In fact, there are various types of fraud in the financial industry.

An internal fraud refers to the situation when an employee modifies records acting with intent to defraud.

An external fraud is represented, for example, by a credit card fraud.

There are also staged car accidents, insurance frauds, and medical frauds.

AI-based fraud detection requires data sources. For example, transaction process from one or several banks as well as customer transactions for a credit card fraud. In order to detect insurance fraud, they usually use already asserted claims, interaction data, e.g. social media, mobile phone, ATMs.

AI crunches through all the data, it is able to detect abnormalities and indicate probable incidences of abuse.

Standard fraud detection

Transaction data is the main data source for credit card fraud detection: the time of a transaction, transfered amount of money, information on transaction point (via ATM, Internet, store, etc.), card number, issuer, country of the transaction, age and gender of the cardholder, etc. Additional information can be obtained via metadata from social media channels, localization data from smartphones and IP addresses.

A standard process of fraud detection is carried out using so-called expert rules. They include an If-Then rule based on the expertise of business specialists.  For example, if a card has been used on several ATMs within one hour, the transaction can be classified as a fraud. The same applies to the situation when the card is used for more than two transactions within five minutes.

These rules are easy to implement, however, they are able to detect just the past fraud patterns.

As soon as criminals change their behavior, it takes a long period of time till the expert identifies it and implements new rules.

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AI-based fraud detection

AI and Machine Learning are applied for predictive analysis and modeling. In this regard, historical data in form of transactions from previous years are required.

This data are identified as labeled data, as it is already known whether the related transactions were legal or not.  The previously used If-Then rules come now into play.

The goal here is to create new features for all transactions. This process is referred to as feature engineering.

The number of created features will be increased according to inputs of business experts and model feedback. At the same time, the number will also grow by adding new historical transaction data.

After the data preparation, multiple algorithms get evaluated in order to find the best model for precise fraud forecasts.

The model selection can range from a simple linear classifier to complex recurrent neural networks. Evaluation scheme and performance metrics are used to identify the best model. The selected model is then implemented for all new transactions. The actual performance of the model is consistently followed and can be extended with new transaction data for a greater precision.

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