We trained a model on 150,000 real credit records. Now you can see exactly what it learned — and why it thinks what it thinks.
No black boxes. Every step is transparent, inspectable, and based on established statistical methods used in real credit scoring.
Missing incomes get median-imputed. Outliers are capped. Duplicates removed. The boring stuff matters.
OptimalBinning splits each feature into risk segments that make statistical sense — not arbitrary buckets.
Weight of Evidence turns each bin into a number that tells you: is this group more likely to default?
Logistic regression converts the pattern into a probability, then scales it to a 100–670 credit score.
Most ML demos spit out a number and call it a day. This one shows you the full story behind every prediction.
A credit score from 100 to 670
Higher is safer. The scale maps directly to the model's confidence.
Default probability
The raw percentage chance the model assigns to someone going 90+ days late.
Per-factor breakdown
See which variables helped the score, which hurt it, and by how much.
Default probability
4.84%
Base score
385
Plug in a hypothetical applicant's details and see what the model predicts. No data gets stored — it's all computed in your browser session.