Unsupervised learning techniques are really useful tools for finding strange or unusual activities, especially when spotting fraud. Fraud often shows up as rare events that are very different from what usually happens. This makes it a perfect fit for unsupervised learning methods since they don't need any labeled data for training.

Key Techniques in Unsupervised Learning:

1. Clustering:

   • Algorithms like K-means or DBSCAN help group similar data points together.
   • Fraudulent transactions can show up far away from normal behavior, helping to point out possible fraud.
   • Example: In banking, clustering could show a bunch of transactions happening quickly from different places far apart. This can mean someone might be trying to take over an account.

2. Dimensionality Reduction:

   • Techniques like PCA (Principal Component Analysis) make complex data simpler while keeping its important details. This method can help find unusual activities that might mean fraud.
   • Illustration: Think about plotting transactions based on different features. PCA helps show these transactions in 2D, making it easier to find anything odd.

3. Isolation Forest:

   • This method finds strange activities by focusing on what makes them different, rather than on what is normal. It randomly splits the data, and the points that need fewer cuts to be separated are seen as strange.
   • Application: In retail, this technique can quickly flag suspicious purchases when they stand out in the isolation forest model.

Conclusion

By using these unsupervised learning techniques, businesses can spot fraud before it becomes a bigger problem, even without needing a lot of labeled data. This allows them to act quickly against new threats, keeping both their business and their customers safe.