Micro-insurance and Micro-claims
Status: Planned (Enabled through adversarial disputes with predefined rulesets)
The core problem with micro-insurance
Micro-insurance is designed to cover:
small financial losses,
short-term risks,
high-frequency events,
users with limited access to traditional insurance.
But there’s a paradox:
The smaller the claim, the harder it is to resolve fairly.
Why?
Traditional claim reviews are expensive.
Human adjusters don’t scale for $20–$300 claims.
Automation alone can’t handle edge cases.
Users feel ignored or unfairly rejected.
As a result, many micro-insurance systems either:
auto-approve everything (risking abuse), or
auto-reject everything unclear (destroying trust).
Real-world micro-claim scenarios
These cases happen every day:
Flight delay insurance with disputed delay times.
Delivery insurance for lost or damaged packages.
Weather-based insurance with unclear local impact.
Device insurance with ambiguous damage causes.
Gig-economy insurance for short jobs or shifts.
Parametric insurance where conditions partially trigger.
Each claim is small — but the trust impact is huge.
Why traditional insurance logic breaks down
For micro-claims:
Manual review costs more than the payout.
Centralized decisions feel opaque.
Appeals are slow or non-existent.
Users assume bias toward the insurer.
This creates a toxic loop:
Low trust → high churn
High churn → stricter automation
Stricter automation → more rejected claims
The missing layer: scalable, neutral judgment
Micro-insurance doesn’t need perfect accuracy. It needs fair, explainable decisions at low cost.
Slice introduces a middle layer between:
fully automated payouts, and
expensive human adjusters.
A layer where:
disputes are rare but resolvable,
decisions are transparent,
costs stay proportional to claim size.
How Slice fits into micro-insurance systems
Slice acts as an on-demand dispute resolver.
Typical flow:
A claim is submitted.
The system auto-processes it.
If the claim is disputed, it is escalated to Slice.
Evidence is submitted (photos, receipts, timestamps, sensor data).
Independent jurors review the case.
A ruling is issued.
The payout contract executes the decision automatically.
No claims agents. No back-and-forth emails. No black-box decisions.
Example: delivery micro-insurance
A user insures a package for $50.
The package arrives damaged.
The insurer’s system flags the claim as “unclear”.
Instead of rejecting it:
The dispute is sent to Slice.
The user submits photos and delivery timestamps.
Jurors evaluate whether the damage matches transit issues.
The ruling:
approves full payout,
approves partial payout,
or rejects the claim with justification.
The result is enforced automatically.
Example: parametric weather insurance
A farmer has micro-insurance for rainfall.
Sensors report borderline data.
The claim is disputed due to conflicting sources.
Slice allows:
evidence from multiple oracles,
local context evaluation,
human interpretation where automation fails.
This avoids:
blind oracle dependence,
rigid yes/no logic.
Why this matters for insurers and protocols
For insurers
Lower operational costs.
Reduced fraud without blanket rejections.
Higher user trust and retention.
For users
A real chance to contest unfair outcomes.
Transparent decisions.
Faster resolutions.
For on-chain insurance protocols
Removes reliance on admin intervention.
Enforces rulings trustlessly.
Keeps systems decentralized under stress.
Micro-claims need proportional justice
Big insurance can afford:
lawyers,
adjusters,
long processes.
Micro-insurance can’t.
Slice enables:
low-cost justice for low-value claims,
without sacrificing fairness or decentralization.
The takeaway
Micro-insurance fails when disputes are ignored. It scales when disputes are cheap, fair, and enforceable.
Slice makes micro-claims:
economically viable,
socially fair,
technically enforceable.
Micro-claims are typically resolved using Tier 1, enabling fast and cost-effective evaluations that would be impractical in traditional insurance systems.
→See how disputes are categorized in Dispute Tiers
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