Set Fair and Explainable Data Rules in Insurance Underwriting and Claims
Insurance companies face mounting pressure to balance automation with fairness in underwriting and claims processes. This article examines practical strategies for establishing transparent data rules that protect both insurers and policyholders, featuring insights from industry experts. Learn how to validate contracted rates accurately and implement effective conflict resolution protocols that maintain trust while streamlining operations.
Validate Contracted Rate Claims
We routinely support insurance underwriters and plan sponsors to investigate validity of claims using third party contracted rate data within the healthcare insurance market. Medlyze supports employers and providers of healthcare to compare their claims payments against the contracted rates they have negotiated with insurance companies. As a result we identify overpayments and underpayments and support employers and providers to recoup discrepancies.
Review gates and third party data are essential parts of this process to identify mistakes, increase trust and maximize profits for all parties involved.
Escalate Application Conflicts to Human Review
Third-party data should confirm or add context to what an applicant discloses, not silently override it. The moment a data flag can trigger a decision without an actual person being able to explain why, you've lost transparency.
Our clearest governance example comes from accelerated underwriting: when a data check (MIB, MVR, prescription history) raises something inconsistent with the application, the file doesn't get an automatic decline. It gets escalated back to traditional underwriting for a human review. That single rule has caught real discrepancies before they became a denied claim or a client dispute, and it means every accelerated decision leaves a trail we can walk back through if a client or regulator asks how we got there.
Chris Funnell, Founder, TermCanada
Publish Approved Source Policy
Insurance companies should clearly document which data sources may be used in underwriting and claims decisions. The policy should explain why each type of data is needed and how it supports a valid business purpose. Data that is unrelated to risk, coverage, or claim review should not be collected or used.
Clear records also help employees follow the same standards and help regulators review decisions. Create and publish a data-use policy that defines allowed sources and purposes.
Apply Consistent Standards Across Similar Cases
Comparable policyholders should receive comparable treatment when similar facts are present. Rules for pricing, eligibility, claim review, and fraud checks should be applied in the same way across the business. Any exceptions should be limited, supported by clear evidence, and recorded for review.
Consistent practices reduce unfair differences that can arise from individual judgment or uneven processes. Review decision rules regularly to ensure similar cases are handled fairly.
Clarify Outcomes and Provide Appeals
Customers deserve clear and simple reasons when an insurance decision affects them. A denial, higher price, reduced coverage, or claim action should explain the main facts and rules behind the result. The explanation should avoid technical terms that make the decision hard to understand.
Customers should also be told how to correct inaccurate data or ask for a review. Provide plain-language decision notices and a simple appeal process.
Set Retention Limits and Restrict Access
Sensitive customer information should only be kept for as long as there is a lawful business need. Retention rules should state when data must be deleted, archived, or made anonymous. Strong security controls can reduce the risk of theft, misuse, or unauthorized access.
Access should be limited to workers and vendors who need the information for approved tasks. Set clear retention periods and strengthen protections for sensitive data.
Test Models for Unequal Effects
Data models can create unfair results even when they do not use protected traits directly. Regular testing can show whether a model has a worse effect on certain groups of people. Testing should cover underwriting, pricing, claim handling, and fraud detection tools.
When a problem is found, the company should investigate the cause and adjust the model or process. Schedule routine fairness tests and act quickly on harmful results.