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Instructions
You are a Continuous Improvement specialist focusing on: 1. **Model Enhancements**: - Move from deterministic to stochastic ORSA projections - Implement ESG/climate risk scenarios - Enhance correlation modeling and tail risk assessment 2. **Automation & Efficiency**: - Automate ORSA reporting and scenario runs - Reduce manual interventions in the closing cycle - Implement real-time monitoring and alerting 3. **Feedback Loop**: - Post-mortem analysis: compare actual vs. projected solvency movements - Improve projection models and assumptions continuously - Learn from experience and industry best practices Use continuous improvement methodologies, technology innovation, and learning frameworks to enhance ORSA processes and outcomes over time.
Knowledge Base (.md)
Business reference guide
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.md, .txt, .pdf
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Multiple files: .json, .csv, .xlsx, .xls, .pdf, .docx, .pptx, .txt
Tools 5
stress_test_scenarios
Model for storing functions that can be called by an agent.
stress_test_scenarios
Model for storing functions that can be called by an agent.
@tool( name="stress_test_scenarios", description="Perform stress testing under various risk scenarios", show_result=True, ) def stress_test_scenarios( base_solvency_ratio: float, base_scr: float, base_own_funds: float, stress_scenarios: List[str], ) -> Dict[str, Any]: """ Perform stress testing under various risk scenarios. Args: base_solvency_ratio: Base solvency ratio base_scr: Base SCR amount base_own_funds: Base own funds stress_scenarios: List of stress scenarios to test Returns: Dictionary containing stress test results """ scenario_definitions = { "interest_rate_shock": {"description": "Parallel shift in interest rates", "scr_impact": 0.05, "own_funds_impact": -0.08}, "equity_market_crash": {"description": "40% decline in equity markets", "scr_impact": 0.08, "own_funds_impact": -0.12}, "mortality_stress": {"description": "Pandemic-like mortality increase", "scr_impact": 0.10, "own_funds_impact": -0.15}, "lapse_shock": {"description": "Mass lapse event", "scr_impact": 0.04, "own_funds_impact": -0.06}, "credit_default": {"description": "Sovereign default scenario", "scr_impact": 0.06, "own_funds_impact": -0.09}, "operational_event": {"description": "Major operational failure", "scr_impact": 0.02, "own_funds_impact": -0.20}, } stress_results = {} for scenario in stress_scenarios: if scenario in scenario_definitions: d = scenario_definitions[scenario] stressed_scr = base_scr * (1 + d["scr_impact"]) stressed_own_funds = base_own_funds * (1 + d["own_funds_impact"]) stressed_solvency_ratio = (stressed_own_funds / stressed_scr) * 100 stress_results[scenario] = { "description": d["description"], "base_solvency_ratio": round(base_solvency_ratio, 2), "stressed_solvency_ratio": round(stressed_solvency_ratio, 2), "impact_on_solvency_ratio": round(stressed_solvency_ratio - base_solvency_ratio, 2), "stressed_scr": round(stressed_scr, 0), "stressed_own_funds": round(stressed_own_funds, 0), "adequacy_level": "Excellent" if stressed_solvency_ratio >= 150 else "Good" if stressed_solvency_ratio >= 130 else "Adequate" if stressed_solvency_ratio >= 100 else "Inadequate", } return {"base_metrics": {"solvency_ratio": base_solvency_ratio, "scr": base_scr, "own_funds": base_own_funds}, "stress_results": stress_results}
project_solvency_evolution
Model for storing functions that can be called by an agent.
project_solvency_evolution
Model for storing functions that can be called by an agent.
@tool( name="project_solvency_evolution", description="Project solvency ratio evolution over time under different scenarios", show_result=True, ) def project_solvency_evolution( current_solvency_ratio: float, current_scr: float, current_own_funds: float, projection_years: int, scenario_type: str, assumptions: Dict[str, Any], ) -> Dict[str, Any]: """ Project solvency ratio evolution under different scenarios. Args: current_solvency_ratio: Current solvency ratio current_scr: Current SCR amount current_own_funds: Current own funds projection_years: Number of years to project scenario_type: Type of scenario(base/optimistic/pessimistic) assumptions: Dictionary of scenario assumptions Returns: Dictionary containing projected solvency metrics """ scenario_multipliers = { "base": {"scr_growth": 1.02, "own_funds_growth": 1.05, "profit_margin": 0.08}, "optimistic": {"scr_growth": 1.01, "own_funds_growth": 1.08, "profit_margin": 0.12}, "pessimistic": {"scr_growth": 1.04, "own_funds_growth": 1.02, "profit_margin": 0.04}, } multipliers = scenario_multipliers.get(scenario_type, scenario_multipliers["base"]) current_scr_proj = current_scr current_own_funds_proj = current_own_funds projections = {} for year in range(1, projection_years + 1): current_scr_proj *= multipliers["scr_growth"] profit = current_own_funds_proj * multipliers["profit_margin"] current_own_funds_proj += profit solvency_ratio = (current_own_funds_proj / current_scr_proj) * 100 projections[f"year_{year}"] = { "solvency_ratio": round(solvency_ratio, 2), "scr_amount": round(current_scr_proj, 0), "own_funds": round(current_own_funds_proj, 0), "profit": round(profit, 0), "capital_buffer": round(current_own_funds_proj - current_scr_proj, 0), } return { "scenario_type": scenario_type, "projection_years": projection_years, "assumptions": assumptions, "projections": projections, }
file_tools
FileTools from agno framework
file_tools
FileTools from agno framework
calculator
CalculatorTools from agno framework
calculator
CalculatorTools from agno framework
reasoning_tools
ReasoningTools from agno framework
reasoning_tools
ReasoningTools from agno framework
Test Agent
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