II.
StackProfile JSON
Structured · livestack-profile:insurance-claims-processing
Insurance Claims Processing Stack (Python, FastAPI, PostgreSQL, Kafka, OCR/ML) json
Inspect the normalized record payload exactly as the atlas UI reads it.
{
"id": "stack-profile:insurance-claims-processing",
"_kind": "StackProfile",
"_file": "domain/stack-profiles/deep-stacks-4.yaml",
"_cluster": "domain",
"attributes": {
"displayName": "Insurance Claims Processing Stack (Python, FastAPI, PostgreSQL, Kafka, OCR/ML)",
"description": "An end-to-end claims processing pipeline for insurance carriers combining\nFastAPI as the orchestration API, PostgreSQL for policy and claims data,\nand Kafka for event-driven intake of new claims from agents and policyholders.\nOpenCV and scikit-learn power OCR extraction and fraud-scoring models that\nclassify claim documents, estimate damage, and flag anomalies. Celery handles\nlong-running ML inference jobs asynchronously. Targeted at mid-to-large\ninsurers replacing manual adjudication workflows. The primary tradeoff is\nregulatory complexity — claim state machines must satisfy jurisdiction-specific\nrules, and model decisions require explainability for audit compliance.\n",
"composes": [
"language:python",
"framework:fastapi",
"library:sqlalchemy",
"library:opencv-python",
"library:scikit-learn",
"library:celery",
"library:pydantic"
]
},
"outgoingEdges": [
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"to": "language:python",
"kind": "composed_of"
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"kind": "composed_of"
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"to": "library:sqlalchemy",
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"kind": "used_by_role"
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{
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"to": "role:ml-engineer",
"kind": "used_by_role"
},
{
"from": "stack-profile:insurance-claims-processing",
"to": "role:data-engineer",
"kind": "used_by_role"
},
{
"from": "stack-profile:insurance-claims-processing",
"to": "workflow:claims-processing-audit",
"kind": "follows_workflow"
},
{
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"to": "workflow:data-pipeline-deployment",
"kind": "follows_workflow"
},
{
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"to": "domain:insurance",
"kind": "applies_to"
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"kind": "applies_to"
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"kind": "requires_skill_area"
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"incomingEdges": []
}