When it comes to detecting money fraud, every little pattern counts. I recently came across a project that involved analyzing around 100,000 transaction records to identify potential fraud. The approach involved structuring a graph with networkx, where each node represents an entity, and each edge represents a transaction. But the real challenge lies in choosing the right machine learning algorithm to detect fraud.
The project had already tried DBSCAN, but it didn’t yield the desired results. I was curious to explore other options, such as isolation forest and autoencoders. However, I wanted to know what other algorithms might be suitable for this task.
Fraud detection is a critical application of machine learning, and it requires a thoughtful approach. The algorithm needs to be able to identify patterns that might indicate fraudulent activity, while minimizing false positives. In this case, the pattern involves the number of people involved in the transaction chain.
So, what algorithms do you think would be the most suitable for this task? Have you worked on a similar project? I’m open to any suggestions and would love to hear about your experiences.