Using AI to detect fraud

Detecting fraud can 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 with plenty of past success in many industries like banking and insurance.

Can AI be a gate-keeper against fraud?

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02/ What can be achieved?

Globally, fraud costs the world economy an estimated £3.24tn. The improved accuracy and versatility associated with the use of machine learning for fraud detection promise substantial cost reduction for many industries and sectors.

03/ The problem

In the past, fraud detection has been done by rules-based algorithms which are typically complicated and not always very hard to circumvent. These techniques risk missing a lot of fraudulent activity or continuing to have excessive amounts of false positives where client’s cards get declined due to misidentified suspicious behaviour. 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.

04/ Opportunity for deep learning

Deep learning algorithms can process great amounts of data and detect complicated underlying patterns from seemingly unrelated information. They also have the ability to continuously learn and evolve to remain up to date with a dynamic environment.

05/ How is the AI model implemented?

For payment fraud, the model continuously supervises customer behaviour and once a threshold level of suspicion is detected, the bank is notified and then has the information to request additional authentication from the customer. A similar problem is insurance claims where the model analyses all of the information the customer gives about the particular case and is able to detect whether a particular claim might be fraudulent.

06/ Data requirements

The model is trained on historical data of consumer behaviour which is known to have been either fraudulent or normal. A major benefit of deep learning is that you are able to combine multiple types of data. For example, a deep learning model can analyze the text written by a customer in an insurance claim and use it in combination with more basic input data to make an accurate prediction.

07/ Where to learn more?

Fraud detection models can be built in many different ways. A lot of fraud data features large spreadsheets of tabulated data. Have a look at our Building a model for tabulated data tutorial to see how to solve this on the Peltarion Plattform.

To learn how to use the Peltarion platform to build a regression model and combine a variety of input data types try our Predicting real estate prices tutorial.