Most machine learning tutorials end with an accuracy number. In insurance claims, that number is rarely the thing that decides whether a model goes live. The question a claims manager, an auditor or a
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Treating reason codes as stable, versioned identifiers is something I'd second, and it pairs well with logging the ruleset version alongside each triage result so an auditor can replay exactly what fired. One question on the human override: when a handler moves a claim to a different lane, do you capture a structured override reason that maps back to the R-codes? In our experience those disagreement rows are the best source for finding rules that are miscalibrated, much more useful than aggregate accuracy.
Kartik N V J K
AI Developer | Making AI reliable, trustworthy & accessible to everyone | Active community contributor
Mapping SHAP back onto the existing R-codes keeps the vocabulary handlers already know, which I like. I'd also count how often the new model's top feature for a claim has no R-code at all. If that happens a lot in shadow mode, handlers will see a familiar code on a lane the model picked for a different reason.