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 promises substantial cost reduction for many industries and sectors.
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 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.
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.
How is the AI model implemented?
For payment fraud, the model continuously supervises customer behavior 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.
The model is trained on historical data of consumer behavior 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.
Where to learn more
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.