Finance
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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
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- Identifying Regulatory Requirements AgentLive
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- Sectoral Decarbonization Pathways AgentLive
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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 usProduct Monitoring & Innovation Agent
An AI agent monitoring product performance and driving innovation in life insurance. Specializes in experience monitoring, lifecycle management, and innovation trends.
Instructions
You are ProductMonitoringAgent, an AI-powered monitoring and innovation specialist operating under the Product Design Life Insurance Module. ALWAYS reference the Product_Design_Life_Insurance knowledge base. ## Your Responsibilities: 1. **Experience Monitoring** - Track mortality, persistency, and expense experience vs. assumptions - Adjust pricing and reserves if deviations arise - Monitor product performance and customer satisfaction 2. **Product Lifecycle Management** - Monitor profitability and relevance of in-force portfolios - Decide on repricing, redesign, or withdrawal of products - Optimize product portfolios for profitability and customer value 3. **Innovation Trends** - Develop ESG-linked life insurance (discounts for healthy lifestyles) - Explore embedded insurance and micro-life policies - Design hybrid products combining life cover + retirement + investment ## Tool Usage Guidelines: - Use FileTools to access performance data, monitoring reports, and innovation research - Use ExaTools for innovation research and industry trend analysis - Use calculate_embedded_value for profitability monitoring and portfolio optimization - Use calculate_cash_value for policy performance analysis and customer value assessment - Always consider customer needs, market trends, and competitive positioning - Drive continuous improvement and innovation in product design and features Your goal is to ensure **optimal product performance** while driving innovation and maintaining competitive advantage in the life insurance market.
Knowledge Base (.md)
Business reference guide
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.md, .txt, .pdf
Data Files
Upload data for analysis (CSV, JSON, Excel, PDF)
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Multiple files: .json, .csv, .xlsx, .xls, .pdf, .docx, .pptx, .txt
Tools 4
file_tools
FileTools from agno framework
file_tools
FileTools from agno framework
calculate_embedded_value
Model for storing functions that can be called by an agent.
calculate_embedded_value
Model for storing functions that can be called by an agent.
@tool( name="calculate_embedded_value", description="Calculate embedded value for life insurance products", show_result=True, ) def calculate_embedded_value( present_value_future_profits: float, adjusted_net_asset_value: float, cost_of_capital: float, risk_margin: float, ) -> Dict[str, Any]: """ Calculate embedded value for life insurance products. Args: present_value_future_profits: PV of future profits adjusted_net_asset_value: Adjusted net asset value cost_of_capital: Cost of capital rate risk_margin: Risk margin amount Returns: Dictionary containing embedded value calculations """ embedded_value = present_value_future_profits + adjusted_net_asset_value cost_of_capital_amount = embedded_value * cost_of_capital value_of_in_force = embedded_value - cost_of_capital_amount new_business_value = value_of_in_force * 0.15 return { "present_value_future_profits": round(present_value_future_profits, 2), "adjusted_net_asset_value": round(adjusted_net_asset_value, 2), "embedded_value": round(embedded_value, 2), "cost_of_capital_rate": cost_of_capital, "cost_of_capital_amount": round(cost_of_capital_amount, 2), "value_of_in_force": round(value_of_in_force, 2), "risk_margin": round(risk_margin, 2), "new_business_value": round(new_business_value, 2), "key_metrics": {"embedded_value": round(embedded_value, 2), "value_of_in_force": round(value_of_in_force, 2), "new_business_value": round(new_business_value, 2)}, }
calculate_cash_value
Model for storing functions that can be called by an agent.
calculate_cash_value
Model for storing functions that can be called by an agent.
@tool( name="calculate_cash_value", description="Calculate cash value accumulation for whole life and endowment policies", show_result=True, ) def calculate_cash_value( policy_type: str, premium: float, policy_duration: int, interest_rate: float, expense_ratio: float, ) -> Dict[str, Any]: """ Calculate cash value accumulation for life insurance policies. Args: policy_type: Type of policy(whole/endowment) premium: Annual premium amount policy_duration: Years since policy inception interest_rate: Annual interest rate(decimal) expense_ratio: Annual expense ratio(decimal) Returns: Dictionary containing cash value calculations """ if policy_type.lower() not in ["whole", "endowment"]: return {"error": "Policy type must be 'whole' or 'endowment'"} net_premium = premium * (1 - expense_ratio) cash_value = 0.0 cash_value_progression = [] for year in range(1, policy_duration + 1): cash_value += net_premium cash_value *= 1 + interest_rate cash_value_progression.append({"year": year, "cash_value": round(cash_value, 2), "net_premium": round(net_premium, 2)}) surrender_value = cash_value * 0.85 return { "policy_type": policy_type, "annual_premium": premium, "net_premium": round(net_premium, 2), "interest_rate": interest_rate, "expense_ratio": expense_ratio, "current_cash_value": round(cash_value, 2), "surrender_value": round(surrender_value, 2), "cash_value_progression": cash_value_progression, "total_premiums_paid": round(premium * policy_duration, 2), }
exa
ExaTools is a toolkit for interfacing with the Exa web search engine, providing
functionalities to perform categorized searches and retrieve structured results.
Args:
enable_search (bool): Enable search functionality. Default is True.
enable_get_contents (bool): Enable get contents functionality. Default is True.
enable_find_similar (bool): Enable find similar functionality. Default is True.
enable_answer (bool): Enable answer generation. Default is True.
enable_research (bool): Enable research tool functionality. Default is False.
all (bool): Enable all tools. Overrides individual flags when True. Default is False.
text (bool): Retrieve text content from results. Default is True.
text_length_limit (int): Max length of text content per result. Default is 1000.
api_key (Optional[str]): Exa API key. Retrieved from `EXA_API_KEY` env variable if not provided.
num_results (Optional[int]): Default number of search results. Overrides individual searches if set.
start_crawl_date (Optional[str]): Include results crawled on/after this date (`YYYY-MM-DD`).
end_crawl_date (Optional[str]): Include results crawled on/before this date (`YYYY-MM-DD`).
start_published_date (Optional[str]): Include results published on/after this date (`YYYY-MM-DD`).
end_published_date (Optional[str]): Include results published on/before this date (`YYYY-MM-DD`).
type (Optional[str]): Specify content type (e.g., article, blog, video).
category (Optional[str]): Filter results by category. Options are "company", "research paper", "news", "pdf", "github", "tweet", "personal site", "linkedin profile", "financial report".
include_domains (Optional[List[str]]): Restrict results to these domains.
exclude_domains (Optional[List[str]]): Exclude results from these domains.
show_results (bool): Log search results for debugging. Default is False.
model (Optional[str]): The search model to use. Options are 'exa' or 'exa-pro'.
timeout (int): Maximum time in seconds to wait for API responses. Default is 30 seconds.
exa
ExaTools is a toolkit for interfacing with the Exa web search engine, providing functionalities to perform categorized searches and retrieve structured results. Args: enable_search (bool): Enable search functionality. Default is True. enable_get_contents (bool): Enable get contents functionality. Default is True. enable_find_similar (bool): Enable find similar functionality. Default is True. enable_answer (bool): Enable answer generation. Default is True. enable_research (bool): Enable research tool functionality. Default is False. all (bool): Enable all tools. Overrides individual flags when True. Default is False. text (bool): Retrieve text content from results. Default is True. text_length_limit (int): Max length of text content per result. Default is 1000. api_key (Optional[str]): Exa API key. Retrieved from `EXA_API_KEY` env variable if not provided. num_results (Optional[int]): Default number of search results. Overrides individual searches if set. start_crawl_date (Optional[str]): Include results crawled on/after this date (`YYYY-MM-DD`). end_crawl_date (Optional[str]): Include results crawled on/before this date (`YYYY-MM-DD`). start_published_date (Optional[str]): Include results published on/after this date (`YYYY-MM-DD`). end_published_date (Optional[str]): Include results published on/before this date (`YYYY-MM-DD`). type (Optional[str]): Specify content type (e.g., article, blog, video). category (Optional[str]): Filter results by category. Options are "company", "research paper", "news", "pdf", "github", "tweet", "personal site", "linkedin profile", "financial report". include_domains (Optional[List[str]]): Restrict results to these domains. exclude_domains (Optional[List[str]]): Exclude results from these domains. show_results (bool): Log search results for debugging. Default is False. model (Optional[str]): The search model to use. Options are 'exa' or 'exa-pro'. timeout (int): Maximum time in seconds to wait for API responses. Default is 30 seconds.
Test Agent
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