Finance
Accountant Module
Accounting Controller Module
Analyst Financial Reporting & Ref Module
Asset-Liability Management Module
Consolidation Module
CSRD Consultant Module
Environmental, Social & Governance Module
- Corporate Strategy Integration AgentLive
- ESG Business Processes AgentLive
- ESG Management TeamLive
- Identifying Regulatory Requirements AgentLive
- Regulatory Reporting AgentLive
- Sectoral Decarbonization Pathways AgentLive
- Strategic Decision-Making AgentLive
- Taxonomy Business Processes AgentLive
- Taxonomy Compliance AgentLive
- Taxonomy Regulatory Requirements AgentLive
Financial Reporting Module
Forward Looking Financial Actuarial Module
IFRS17 & Solvency2 Module
Inventory Actuary Module
ISR Consultant Module
Life & Health Module
Product Design Aging Module
Product Design Life Insurance Module
Structural Risk Analyst Module
Tax Specialist Module
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Contact usPurpose
Advanced AI-powered agent for automated processing and analysis
AI-Powered Intelligence — Advanced AI capabilities for automated processing and analysis
Enterprise Ready — Built for production with security, scalability, and reliability
Seamless Integration — Easy to integrate with your existing systems and workflows
Agent Capabilities
This agent is equipped with the following advanced capabilities:
Knowledge Base
Vector search & retrieval
Knowledge (PgVector)
Available Tools
Calculate Solvency Ratio
Model for storing functions that can be called by an agent.
Calculate Solvency Ratio
Model for storing functions that can be called by an agent.
@tool( name="calculate_solvency_ratio", description="Calculate Solvency II ratio and capital adequacy metrics", show_result=True, ) def calculate_solvency_ratio( own_funds: float, scr_amount: float, mcr_amount: float, risk_modules: Dict[str, float], ) -> Dict[str, Any]: """ Calculate Solvency II ratio and capital adequacy metrics. Args: own_funds: Available own funds scr_amount: Solvency Capital Requirement mcr_amount: Minimum Capital Requirement risk_modules: Dictionary of risk module amounts Returns: Dictionary containing solvency metrics and analysis """ solvency_ratio = (own_funds / scr_amount) * 100 if scr_amount > 0 else 0 mcr_ratio = (own_funds / mcr_amount) * 100 if mcr_amount > 0 else 0 total_risk = sum(risk_modules.values()) risk_concentration = { k: (v / total_risk * 100) if total_risk > 0 else 0 for k, v in risk_modules.items() } adequacy_level = ( "Excellent" if solvency_ratio >= 150 else( "Good" if solvency_ratio >= 130 else "Adequate" if solvency_ratio >= 100 else "Inadequate" ) ) return { "solvency_ratio": round(solvency_ratio, 2), "mcr_ratio": round(mcr_ratio, 2), "adequacy_level": adequacy_level, "risk_concentration": risk_concentration, "capital_buffer": own_funds - scr_amount, "regulatory_margin": own_funds - mcr_amount, }
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, }
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}
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
Required Inputs
Generated Outputs
Business Value
• Automated processing reduces manual effort and improves accuracy
• Consistent validation logic ensures compliance and audit readiness
• Early detection of issues minimizes downstream risks and costs
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Pricing
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Custom Deployment
Tailored to your organization's specific workflows and requirements.
Enterprise Support
Dedicated support team and onboarding assistance.
Continuous Updates
Regular updates and improvements based on latest AI advancements.
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For enterprise deployments.
€Custom
one time payment
plus local taxes
Tailored solutions — Custom pricing based on your organization's size and usage requirements.