Detecting fraud using machine learning and AI is possible. The problem with AI fraud detection is that the machine learning models needed to make it are often dependent on unbalanced data. Unbalanced data is essentially where a data set is a small number of the target variable, and it is not proportionate to other variables in that data set. Most transactions are not going to be fraudulent. Because of that, application fraud detection is quite difficult. Fraud detection using AI is even more difficult on top of that.
For example, the data set used to look at machine learning models and unbalanced data have a 0.172% fraud rate. That shows you that it is quite difficult to build a model using exploratory data analysis to do AI fraud detection well. Companies need to understand how AI plays a monumental role in making this process work.
A big problem with using AI fraud detection is that you cannot rely on traditional accuracy readings. For example, an accuracy rate of 99.98% would sound like a great model on paper. However, it would mean nothing if that classifier missed all the fraudulent transactions. Machine learning models in detecting fraud essentially have to be perfect. It is one of the many downsides of dealing with unbalanced data. Because of that unfortunate reality, it has to be a way of measuring metrics that makes a lot of sense for AI fraud detection.
Instead of using accuracy, many practitioners have decided to focus on precision. Precision is where you focus on how often the algorithm gets the correct answer which is then divided by the number of samples predicted. You can then focus on other things like recall to ensure that your machine learning models work in application fraud detection.
Application to Fraud Detection
All of these things are difficult to understand, but it all starts with exploratory data analysis. This is the first step because of our important it is to have a good understanding of the data you are trying to analyze. That understanding makes all the difference, as your machine learning models will be dependent on how well you can make the data set work for your specific situation.
One way we can make things even better is to have under sampling of the good transactions. That would involve having 50% fraud and 50% good transactions. This way makes it possible to build machine learning models that are quite useful for detecting fraud more frequently than other methods.
Building a Machine Learning Model
Ultimately, the final step is always building those machine learning models that will be put into a product. This is where you need to be careful in analyzing the unbalanced data to do fraud detection in AI well. If you know what you’re doing, it will often lead you to much more accurate results. However, application fraud detection is a difficult feat.
That is why you need the right set of tools to help you start the process and complete it much faster. You need to have tools that enable you to get things done without focusing on the infrastructure needed. A platform like xpresso.ai makes a big difference.