Play 50
Financial Risk Intelligence
Very High✅ Ready
Real-time market analysis, credit risk, fraud detection with explainable AI decisions.
Financial services AI agent — real-time market analysis, credit risk assessment, regulatory document processing (SEC/Basel III), fraud detection, with explainable AI decisions, audit trails, and human-in-the-loop escalation. Uses AI Search for regulatory knowledge, Event Hubs for market feed ingestion, and Cosmos DB for risk state management. Every decision includes confidence scores and reasoning chains.
Architecture Pattern
RAG-powered financial agent: real-time feeds, explainable decisions, human escalation
Azure Services
Azure OpenAI (gpt-4o)Azure AI SearchCosmos DBEvent HubsAzure FunctionsKey VaultAzure Monitor
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Financial Risk Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (251 lines), evaluate (163 lines), tune (213 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + Cosmos DB inputs + envFile
TuneKit (AI Config)
- config/openai.json — gpt-4o, temp=0.1, deterministic
- config/risk.json — risk models, confidence thresholds, escalation rules
- config/guardrails.json — PII protection, regulatory compliance
- evaluation/eval.py — Risk accuracy >90%, Fraud detection >95%
Tuning Parameters
Risk confidence thresholdRegulatory compliance levelFraud sensitivityMarket feed latency SLAHuman escalation rules
Estimated Cost
Dev/Test
$150–300/mo
Production
$5K–15K/mo