Why improving fraud detection is worth the effort
Fraud detection for QR codes means spotting scans, redemptions, and codes that are not genuine: bots, copied coupons, sticker overlays, and abuse of single-use offers. It protects revenue and keeps your numbers honest.Fraud rules trade false positives against missed fraud. Tune them, and always leave a path for a wrongly blocked person to get help.
Diagnose before you change anything
Improvement starts with diagnosis, not with redesign. Before changing a code, split fraud detection by code, placement, time, and rule triggered and look for the weakest segment. The weakest segment is where a fix buys the most, because it holds the largest pool of lost value. Also check the steps around fraud detection: a problem upstream, such as poor placement, can masquerade as a problem with the code itself, and a problem downstream, such as a slow landing page, can waste scans that were earned fairly. Fix the constraint, not the symptom.Write down your diagnosis in one paragraph before you touch anything. State what you observed, which segment is weakest, and what you believe is causing it. This paragraph is your hypothesis, and it keeps the work honest: if the fix does not move the number, the hypothesis was wrong, and you learn something instead of merely changing things.
Levers that move fraud detection
- Use single-use validation for coupons, tickets, and high-value offers. - Rate-limit redemptions and flag bursts from one source. - Inspect physical codes regularly for sticker overlays.Work through the levers in order of expected impact per unit of effort. A lever that costs nothing and affects every scanner beats a lever that costs a print run and affects one placement. For each lever, state the change, the cost, and the segment it should move, then implement only that change. Bundling several changes together feels productive, but it destroys your ability to learn which one worked.
Test the change properly
Flag rate = scans flagged by fraud rules divided by total scans, multiplied by 100. Confirmed fraud rate uses only flags confirmed on review.Illustrative example numbers, not real-world data: 25 of 400 scans are flagged by rules, giving 25 / 400 x 100 = 6.25% flagged. If review confirms 10 as genuine abuse, the confirmed rate is 10 / 400 x 100 = 2.5%. The gap between flagged and confirmed is why review matters.
Run the change as a comparison wherever you can: the old version against the new version, on comparable placements, over the same kind of period. Keep everything else stable, and decide in advance how long the test runs and what counts as a win. A test that ends the moment one variant pulls ahead is not a test, it is a reaction. If traffic is too small for a formal test, change one thing, record the before and after numbers, and treat the result as suggestive rather than proven.