The mule that wasn't: a graph model, synthetic data, and the accounts that broke it
Article summary
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This is the second of a pair. The first was about card fraud, and its lesson was that a chart I was proud of turned out to be a mirror. It looked like my model had found fraud rings, and what it had really found was my own assumptions handed back to me. This post is about money laundering, and it opens on a number I was briefly pleased with and then spent a while trying to explain away: 0.99. That was the precision-recall AUC of a mule-detection model on a labelled dataset, with per-typology…
1Key Takeaways
- The first was about card fraud, and its lesson was that a chart I was proud of turned out to be a mirror.
- It looked like my model had found fraud rings, and what it had really found was my own assumptions handed back to me.
- This post is about money laundering, and it opens on a number I was briefly pleased with and then spent a while trying to explain away: 0.99.
- That was the precision-recall AUC of a mule-detection model on a labelled dataset, with per-typology….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that the first was about card fraud, and its lesson was that a chart I was proud of turned out to be a mirror.
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