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Using AI 
to detect fraud

Detecting fraud can be challenging in a dynamic global business environment with an overwhelming amount of traffic and data to monitor. Fraud detection is an ideal use case for machine learning, which has been proven in the past in many industries such as banking and insurance.

The Challenge

Historically, fraud detection was performed using rule-based algorithms, which tend to be complicated and not always easy to circumvent. These techniques run the risk of overlooking a lot of fraudulent activity or continuing to produce an inordinate number of false positives where customers' cards are declined due to misidentified suspicious behavior. Traditional models are also very inflexible, which is a problem in an application where fraudulent users are constantly finding new ways to slip under the radar.

The opportunity for deep learning

Deep learning algorithms are able to process large amounts of data and recognize complicated underlying patterns from seemingly unrelated information. They are also able to continuously learn and evolve to keep pace with a dynamic environment.

How is the AI model implemented ?

In the case of payment fraud, the model continuously monitors customer behavior, and once a threshold of suspicion is reached, the bank is notified and then has the information to require additional authentication from the customer. A similar problem arises with insurance claims, where the model analyzes all the information the customer provides about the particular case and is able to identify whether a particular claim could be fraudulent.

What can be archieved

Fraud costs the global economy an estimated $4 billion. The improved accuracy and versatility associated with using machine learning for fraud detection promise to significantly reduce costs for many industries and sectors.

Data requirements

This model is trained using historical consumer behavior data that is known to have been either fraudulent or normal. A major advantage of deep learning is the ability to combine multiple types of data. For example, a Deep Learning model can analyze the text a customer has written in an insurance application and use it in combination with simpler input data to make an accurate prediction.

Where to learn more?

Fraud detection models can be built in many different ways. Many fraud data consists of large spreadsheets with tabular data. Take a look at our tutorials within the platform to see how you can solve this at

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