DBSCAN — a density-based unsupervised algorithm for fraud detection

DBSCAN — a density-based unsupervised algorithm for fraud detection

Today I’m going to introduce another technique called DBSCAN — short for Density-Based Spatial Clustering of Applications with Noise. As the name suggests, DBSCAN is a density-based and unsupervised machine learning algorithm.

According to a recent report financial losses due to fraudulent transactions have reached about $17 billion USD, with as many as 5% of consumers experiencing fraud incidents of some kind.

In light of such a big volume of financial losses, every industry is taking fraud detection seriously. It’s not just the financial industries that are susceptible, anomalies are prevalent in every single industry and can take many different forms — such as network intrusion, disturbances in business performances and abrupt changes in KPIs etc.

Fraud/anomaly/outlier detection has long been the subject of intense research in data science. In the ever-changing landscape of fraud detection, new tools and techniques are being tested and employed every day to screen out abnormalities. In this series of articles, so far I’ve discussed six different techniques for fraud detection:

Today I’m going to introduce another technique called DBSCAN — short for Density-Based Spatial Clustering of Applications with Noise.

As the name suggests, DBSCAN is a density-based and unsupervised machine learning algorithm. It takes multi-dimensional data as inputs and clusters them according to the model parameters — e.g. epsilon and minimum samples. Based on these parameters, the algorithm determines whether certain values in the dataset are outliers or not.

Below is a simple demonstration in Python programming language.

fraud-detection machine-learning anomaly-detection outlier-detection data-science

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