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bdr-ai-org/bdr-decision-engine
bdr-decision-engine is a machine learning model from bdr-ai-org. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for bdr-decision-os. The card lists the license as apache-2.0.
The foundational orchestration engine for insurance decision intelligence.
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Updated Jan 1, 2026
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From the Hugging Face model README
The foundational orchestration engine for insurance decision intelligence.
This is NOT a standalone model. It is the reusable core engine that powers all insurance decision systems in the Bader AI platform.
Capabilities are NOT projects.
Insurance decision systems ARE projects.
This engine is industry-agnostic. Insurance logic is injected via configuration.
The BDR Decision Engine provides:
All systems using this engine output the same contract:
{
"decision": "approve | reject | escalate | investigate | adjust",
"confidence": 0.0-1.0,
"rationale": "human-readable explanation",
"risk_signals": ["fraud", "coverage_gap", "inconsistency"],
"recommended_next_step": "string"
}
This engine powers 4 production insurance systems:
| System | Purpose | Capabilities Activated |
|---|---|---|
| ClaimsGPT | Claim approval decisions | vision_ocr, document_parser, vision_vqa, financial_analysis, risk_scoring, decision_optimizer |
| FraudSimulator-AI | Fraud detection | anomaly_detection, scenario_simulator, risk_scoring, financial_analysis |
| AutoRiskScoreEngine | Underwriting & pricing | financial_analysis, optimization_engine, time_series_forecasting, scenario_simulator |
| InsuranceKnowledgeAgent | Policy interpretation | language_rag, policy_clause_reasoner, document_parser |
Each capability module must expose:
{
"input_schema": {},
"output_schema": {},
"supported_decisions": []
}
Vision & Documents (3 modules)
vision_ocr - Extract text from imagesvision_vqa - Visual question answeringdocument_parser - Parse structured documentsLanguage & Knowledge (3 modules)
language_rag - Retrieval-augmented generationtext_analysis - Text classification and NERpolicy_clause_reasoner - Policy interpretationRisk & Analytics (4 modules)
anomaly_detection - Detect unusual patternsrisk_scoring - Calculate risk scoresfinancial_analysis - Financial impact assessmenttime_series_forecasting - Predict trendsDecision Intelligence (2 modules)
decision_optimizer - Optimize decision outcomesscenario_simulator - Simulate decision scenariosGovernance (4 modules)
audit_logger - Log all decisionsconfidence_tracker - Track confidence scoresoverride_handler - Manage human overridesdrift_monitor - Detect model driftβ
Every decision is logged
β
Rationale is always stored
β
Confidence is tracked
β
Human overrides are recorded
β
Audit replay is supported
Governance is shared across all systems - never duplicated.
from bdr_decision_engine import DecisionEngine
# Initialize engine with config
engine = DecisionEngine(
system="claims",
enabled_capabilities=[
"vision_ocr",
"document_parser",
"risk_scoring",
"decision_optimizer"
],
governance="strict",
metrics="claims_kpis"
)
# Process decision
result = engine.process({
"claim_id": "CLM-2026-001",
"claim_type": "motor",
"claim_amount": 5000,
"documents": [...],
"images": [...]
})
print(result.decision) # "approve"
print(result.confidence) # 0.92
print(result.rationale) # "Claim meets all criteria..."
print(result.risk_signals) # []
Bader AI β Insurance Decision Operating System
This engine is the foundation of the GCC reference architecture for insurance AI, designed for:
β Demo chaos
β Capability sprawl
β Architectural drift
β Ungoverned AI outputs
β Non-insurance use cases
Apache 2.0
Building the future of insurance decision intelligence for the GCC region.