How We Approximated an Industry Leading Credit Score in 30 Minutes

MaxDecisions ran an experiment to reverse-engineer a leading credit scoring methodology, training the industry-leading score as a dependent variable against more than 500 credit attributes using stepwise regression.

The research examined five primary behavioral categories: delinquencies, debt ratio, average age of credit history, credit tradeline mixture, and inquiry frequency, seeking empirical validation rather than accepting published proportions at face value.

The resulting model achieved an R-squared of 0.8703, indicating strong correlation between predicted and actual scores, with sample potential score ingredients identified through the analysis -- while acknowledging exact formula replication remains difficult without the underlying bureau data.