How to use Machine Learning in preventing fraud detection?

Randall Hendricks
Randall HendricksAnswered

Fraud detection using ML (Machine Learning) can be solved in different ways. The type of fraud detection model that we need to use largely depends on the kind of dataset that is available for the given problem. The number of ways we can use ML for fraud detection are as follows:

Supervised Learning

It is the most common way of implementing fraud detection, mostly used in a setting where we have a labeled dataset for our task, like in FinTech companies where each transaction has to be labeled as good or bad, (normal or fraudulent). A supervised model is used for the training process, the accuracy of which is only as good as the training dataset it has seen. It might be a challenging task to implement this method with great accuracy as we are most likely to encounter class imbalance in this problem, as most of the transactions that in our fintech example come from normal cases where only a small proportion will be fraudulent.

Unsupervised Learning

In this type of learning method, we try to identify anomalies or outliers in a given dataset. This approach synchronizes well with the problem statement, as by the very definition, we can detect that they complement each other. Since the data available for fraud will always have very few fraudulent transactions, anomaly detections will greatly help us by learning the distribution of the normal transactions and flagging anything that is suspicious and not “normal.”

Semi-Supervised Learning

Here we combine the two categories above to try and get the “best of both worlds.” It is usually used  when generating enough labels for the data is either impossible, constrained by law, or is too expensive. A semi-supervised approach attempts to dissect the given samples into groups even though there is an absence of labels on some or almost all the given samples. Learning these group parameters is the main goal of the semi-supervised learning implementation.

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