Spotting Fraud Without Blocking Good Customers

Here’s a problem that comes up more than you’d think: imagine you process thousands of credit card transactions every day, and a tiny fraction of them are fraudulent. How do you catch the bad ones without accidentally blocking your good customers?

The obvious approach is to build a system that looks at every transaction and decides “fraud” or “not fraud.” The problem is, when fraud is rare (in the data I worked with, it was less than half a percent of all transactions), a basic system just learns to say “not fraud” every time. Technically it’s right 99.6% of the time. But it catches exactly zero actual fraud. Not helpful.

I had to take a different approach. Instead of treating every transaction the same, I built the system to pay extra attention to the rare fraudulent ones. Think of it like this: if you’re looking for a needle in a haystack, you don’t weigh the needle the same as a piece of hay. You make it heavier so it stands out.

After tuning things, the system catches about 75% of fraudulent transactions. It does flag some legitimate ones for a second look too, but that’s a trade-off most businesses would happily make. A quick “was this you?” text to a customer is a lot better than missing real fraud and dealing with chargebacks.

What this means for a business

This isn’t just about credit cards. The same kind of problem shows up whenever you’re looking for something rare but important in a big pile of normal activity:

• Retail: catching shoplifting patterns in thousands of normal transactions
• Insurance: spotting fraudulent claims among legitimate ones
• Quality control: finding the defective items in a production run

In all these cases, a simple average or count hides the problem. If you have a situation where “the numbers look fine” but you still feel like something’s off, that instinct might be right. I can help you figure out what the data is actually telling you.