Our AI/ML Model Hit 99.8% Accuracy—Then Failed in Production. Here's Why.

We built a fraud detection model that was “perfect” in testing—99.8% precision, extensive validation, bias audits, the works.

Then we deployed it.

Within 3 weeks, it was letting real fraud through and flagging legitimate users at 10x the expected rate.

In this postmortem, I break down:

• Why test metrics lie in production
• How feedback loops and real-time behavior broke our model
• The hidden cost of AI maintenance
• The ethical implications we missed

📘 Full article: https://tinyurl.com/3jpzrx6r

Curious to hear your production AI failure stories or lessons learned. What would you do differently?

Comments

[alvaro]

This highlights a critical gap most ML teams miss: statistical significance testing. A 99.8% accuracy model might have a p-value of 0.4 - meaning there's a 40% chance those results are just random noise.
The fraud detection industry especially needs this because:

• Small sample biases get amplified in production
• Edge cases (0.1% → 15%) weren't statistically validated
• Confidence intervals would have flagged the demographic bias issues Chi-square testing and effect size analysis (Cramer's V) catch these problems before deployment. Most teams focus on accuracy metrics but skip asking "Is this performance statistically meaningful vs. algorithmic noise? This kind of statistical validation should be standard in ML pipelines, especially for high-stakes applications like fraud detection.