Insurance: Claims & Underwriting Under Constraints¶
Case study → insurance
Insurance decisions are governed actions, not “smart guesses”.
Claims and underwriting workflows combine policy rules, evidence, exceptions, and legal constraints. A decision-grade system must be able to prove why a decision was allowed — or refuse.
The question
Can AI assist claims and underwriting decisions while enforcing policy, fraud controls, and regulatory constraints — and producing an audit-ready trail?
Failure modes to avoid
Policy as prose
Policies contain non-local exceptions and precedence rules that text summaries routinely flatten.
Evidence leakage
Approvals without defensible evidence paths lead to leakage, disputes, and adverse selection.
Fraud blind spots
Fraud signals are multi-source and relational; similarity search misses structured contradictions.
Unbounded automation
High-risk actions must be constrained and sometimes escalated, not “handled end-to-end”.
What changes with constraint-gated reasoning
The model can propose; the system decides what is allowed.
Every step is validated against policy shapes, required evidence, and role permissions.
flowchart TB;
C["Claim / underwriting proposal"] --> E["Evidence set"];
E --> P["Policy rules"];
P --> V["Constraint gate"];
V -->|"Pass"| OK["Approve + trace"];
V -->|"Fail"| NO["Reject / escalate + violations"];Diagram: typical causal/evidence path (illustrative)
flowchart LR;
EV["Evidence"] --> F["Finding"];
F --> R["Risk factor"];
R --> D["Decision impact"];
D --> T["Trace"];Outputs
Audit-ready traces
Decision, evidence, rules applied, and policy violations (if any).
Deterministic abstention
If required evidence is missing, the system refuses and states what must be provided.
Fraud investigation graph
Relational signals and contradictions surfaced as navigable structures.
Governable automation
Explicit boundaries for what can be auto-approved vs what must escalate.