AI in a Zimbabwean insurer: claims first

Sector figures

Regulatory position

Three use cases with risk notes

1. 01

   Claims triage and document extraction

   Models classify incoming claims, extract fields from forms, quotes and reports, check completeness against the policy, draft the request-for-information letter and route the claim. Assessors decide; the system shortens the queue.

   Risk notes. Medical claims are health data: written consent at policy inception and claim (s.12(1)); rejection, reduction or repudiation stays human (s.25); retrieval filtered so an assessor sees only assigned claims; in-country hosting for the index; measurable outcome is the IPEC complaint category itself.

2. 02

   Fraud and duplicate-claim detection

   Scoring across claims, providers and repairers surfaces patterns for an investigator; the investigator opens the case.

   Risk notes. A fraud flag is not a decision until someone acts on it; keep it that way in the process design and in the audit log; bias review across regions and provider types; data sharing with other insurers is a transfer requiring a basis, even inside Zimbabwe under the Act's processing principles.

3. 03

   Underwriting and pricing support

   Models assist with risk classification and pricing recommendations that an underwriter or actuary approves; recommendations and outcomes are logged for back-testing.

   Risk notes. No RBZ model standard applies, but adopt its register and independent-validation discipline anyway; forex is structural (77% of revenue in USD) so hosted-model costs are affordable in principle, yet personal data still cannot leave without a s.28 basis; document actuarial sign-off on any model that changes price.