The measurable case for localized payer AI
Fraud and claim leakage occur in every country, but the rules that catch them differ from country to country. This is why payer AI has to be built differently.
Every insurer deals with more or less the same problems: fraudulent or inflated claims, money lost through errors, and a slow, manual review between the arrival of a claim and the decision. It is easy to treat these as a pure language problem and expect a powerful general model to read the claims. A general model does help with the reading. But it falls short on the part that actually decides the outcome, the local rulebook that says whether a claim is valid in the first place.
In reimbursement, details usually decide the outcome. Which procedures are covered, under which conditions, with which codes and after which waiting periods is defined in a country-specific framework that changes over time. A model that has never been trained on that framework can summarize a claim well and still reach the wrong conclusion, because the conclusion depends on a clause it has never seen.
Our measurements showed a consistent picture. In our internal evaluation the localized model reached a level on fraud detection and policy-coverage decisions that a claims team can actually build a workflow around, surfacing the risky files without overwhelming reviewers with false alarms. The clearest difference was in reliability. Reimbursement depends on citing the right clause, and the general-purpose models we compared against hallucinated clauses far more often than the localized model did. In a payer setting, a confidently invented rule is a risk that can lead directly to costs. Task-level evaluation results for MINA v3.7 are on the MINA v3.7 page.
This is why payer AI has to be localized rather than merely translated. Coverage logic, coding standards and pre-authorization rules are specific to each market and they keep changing. A system trained on a market's real rules and grounded in citable sources provides automation that can be defended in an audit; without it, the outputs of automation have to be checked again by hand. Türkiye's SGK and SUT system was the first and hardest example of this; the same per-market method can be applied to any payer.
Used correctly, such a system does not act as an automatic adjudicator, and it is not meant to. It supports human reviewers: it surfaces the files worth a closer look, points to the governing rule and leaves the decision with the reviewer. These results come from an internal evaluation on synthetic data with anonymized comparison models. The real test is the live pilots now underway.