5 Steps to Build an AI-based Fraud Detection System

5 Steps to Build an AI-based Fraud Detection System

5 Steps to Build an AI-based Fraud Detection System. It seems all modern fraud detection systems are AI-based, but how does it actually work? What are the exact steps to build such a system?

Originally this article has been [posted_](https://blog.dataart.com/how-to-build-a-fraud-detection-system-in-house) in the DataArt blog, a technology consultancy where I led the AI/ML competency. It was quite popular, so I’m making sure more people have access._

Let’s break down the high-level process of building an AI-based fraud detection system into 5 steps.

Step 0. Data Preparation

Data is a fuel of machine learning; the better the fuel is, the faster the car can run. I’ll skip the part that describes how to aggregate the data and how to store and transport it and jump straight to what should be done with the data to make ML magic happen.

It is important to start by cleansing data to get a specified set of features for analysis. Let’s look at payments as an example; the relevant features would be buyer details, seller details, payment amount, the time when the transaction was sent, bank details, and IPs as well as others. In fact, there could be hundreds of parameters. The more complex the field, the more parameters necessary. Hence, the better we clean the data, removing depending or correlated features, the better the performance of the final algorithm. Otherwise, it would be hard to tell which feature caused the prediction. Typically, data preparation and exploration can take as much or even more time than the rest of the ML project.

machine-learning fraud-detection ai

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