Insurance / Delivered project

Risk Validator AI

A reviewer-first system for parsing policy documents, comparing evidence, and surfacing inconsistencies before underwriting decisions.

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Document intelligence workflow converting complex records into reviewable evidence — Insurance · Data → Intelligence → ActionPhoto by Romain Dancre · Unsplash License ↗

01 / Problem statementDelivered project · Insurance

The operating problem

Reviewers reconcile long policies, submissions, schedules, and standardized forms manually, making omissions and conflicting values difficult to spot.

Conditions the system must survive03 operating constraints

Policy sets contain long, cross-referenced, and inconsistent documents

Validation rules vary by product, jurisdiction, and underwriting policy

The system must not make an underwriting decision

02 / How we approached it

From operating uncertainty to testable evidence.

The work was decomposed into four engineering decisions. Each one produced an artifact the customer could inspect, test, and carry into deployment.

01Frame

Classify and parse policy document sets

Evidence produced

Policy entity, clause, and validation-rule model

02Evaluate

Normalize entities, limits, dates, and clauses

Evidence produced

Extraction benchmark across forms, schedules, and endorsements

03Engineer

Compare evidence using explicit validation rules

Evidence produced

Discrepancy precision review with risk specialists

04Operationalize

Present discrepancies with source-page references

Evidence produced

Reviewer workspace with rule, source, and disposition trace

03 / Deployment record

Deployed around the workflow—not beside it.

The system boundary includes where inference runs, how evidence reaches existing tools, and how people handle uncertainty after launch.

Implementation statusDelivered project

Topology

Private document, rules, and evidence services integrated with underwriting case workflows.

Integration

Submissions, policy systems, document stores, and approved risk data resolve into a case-level evidence graph.

Operation

Flags remain reviewer-owned; rule changes, source pages, model versions, and dispositions stay auditable.

04 / Evaluation metrics

What must be measured before the system earns trust.

Evaluation covers model behavior, workflow burden, and production performance. The metric defines the gate; the customer baseline and acceptance threshold define the target.

01Evaluation gate

Policy field F1

How it is measured

Precision and recall for entities, limits, dates, and clauses.

What it decides

Defines which evidence can be normalized automatically.

02Evaluation gate

Discrepancy precision

How it is measured

Reviewer-confirmed issues among surfaced validation flags.

What it decides

Controls case noise and rule priority.

03Evaluation gate

Review preparation time

How it is measured

Time to assemble a source-complete risk case.

What it decides

Measures reduction in manual reconciliation.

04Evaluation gate

Source traceability

How it is measured

Flags linked to rule, document, page, extracted values, and disposition.

What it decides

Determines audit and reviewer defensibility.

05 / Customer perspective

Value has to appear in the customer’s operating day.

What matters in practice

A useful flag names the rule, conflicting values, source pages, and uncertainty; it never replaces underwriting judgment.

01Observable value signalFaster case preparation

02Observable value signalMore consistent discrepancy checks

03Observable value signalTraceable reviewer evidence

04Observable value signalNo automated underwriting decision

06 / Technology context

Tools follow the system—not the other way around.

Final architecture depends on data quality, operating conditions, integrations, risk, and evaluation criteria established during discovery.

Document AIOCRLLMsRules engineKnowledge graphs

Test the operating assumption

Define the evidence required to move from possibility to production.

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Risk Validator AI

Reviewers reconcile long policies, submissions, schedules, and standardized forms manually, making omissions and conflicting values difficult to spot.

From operating uncertainty to testable evidence.

Classify and parse policy document sets

Normalize entities, limits, dates, and clauses

Compare evidence using explicit validation rules

Present discrepancies with source-page references

Deployed around the workflow—not beside it.

What must be measured before the system earns trust.

Policy field F1

Discrepancy precision

Review preparation time

Source traceability

Value has to appear in the customer’s operating day.

Tools follow the system—not the other way around.

Related operational systems.

Edge AI for predictive maintenance and leak detection

Computer vision for visual quality inspection

Define the evidence required to move from possibility to production.