Explainable Claims Triage in Python: Why Reason Codes Come Before Accuracy

Machine learning tutorials typically highlight accuracy, but in insurance claims the decisive factor is whether a model can explain its routing in plain terms. This article presents a deliberately simple claims-triage scorer that emphasizes explanation over predictive power. The system only suggests a handling lane (fast track, standard review, or specialist review), while humans retain final authority over claim outcomes. Its scorecard-style design adds or subtracts points for each rule that fires, so the reasons behind every lane assignment are visible. The author recommends treating reason codes as a core output, with stable identifiers, plain-language descriptions, a short list of top drivers, and neutral wording. The code and rules are illustrative only and not intended for production use.