Resources · 2026-10-12

Explainable AI in insurance: why regulators and customers prefer it

When an insurer or a bank declines an application or investigates a claim, it must be able to say why. Regulators, auditors and customers all expect it.

What explainable AI is

A system whose decisions can be broken down into understandable factors: “the score dropped 35 points for a recent delinquency and 50 for a high debt-to-income ratio”.

Why it matters

In lending, rules require communicating the principal reasons for a denial. In insurance, explanations protect against complaints and disputes and speed up the analyst’s work.

How to avoid the black box

Use rules and scores with visible factors, store the rule version in force for each decision, allow simulating changes before publishing them, and log everything in a tamper-proof audit trail.

Human in the loop

AI recommends; a person decides doubtful or high-impact cases, and the decision is justified in writing.

This is the philosophy behind Interpolgar Nexus: every approval, denial or fraud alert comes with its reasons and evidence. Request a pilot to see it with your data.