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Risk Model Builder
The Risk Model Builder is responsible for developing, validating, and maintaining risk models for ALM purposes. It builds liquidity projection models, interest rate sensitivity models, and stress testing frameworks. The agent ensures models are accurate, auditable, and compliant with regulatory guidelines (IRRBB, liquidity risk frameworks).
Purpose
The Risk Model Builder is responsible for developing, validating, and maintaining risk models for ALM purposes. It builds liquidity projection models, interest rate sensitivity models, and stress testing frameworks. The agent ensures models are accurate, auditable, and compliant with regulatory guidelines (IRRBB, liquidity risk frameworks).
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:
Available Tools
Project Liquidity
Project liquidity under different scenarios.
Args:
balance_sheet_data: Balance sheet with cash flows and maturity profiles
scenario: Scenario type (normal, stressed, severe)
Returns:
Liquidity projection report
Project Liquidity
Project liquidity under different scenarios. Args: balance_sheet_data: Balance sheet with cash flows and maturity profiles scenario: Scenario type (normal, stressed, severe) Returns: Liquidity projection report
def project_liquidity(balance_sheet_data: str, scenario: str = "normal") -> str: """ Project liquidity under different scenarios. Args: balance_sheet_data: Balance sheet with cash flows and maturity profiles scenario: Scenario type(normal, stressed, severe) Returns: Liquidity projection report """ try: import json if isinstance(balance_sheet_data, str): try: data = json.loads(balance_sheet_data) except json.JSONDecodeError: return "Error: Invalid JSON format for balance sheet data" else: data = balance_sheet_data "color: #6b7280;"># Scenario assumptions scenarios = { 'normal': {'deposit_withdrawal': 0, 'prepayment': 0, 'funding_rolloff': 0}, 'stressed': {'deposit_withdrawal': 0.05, 'prepayment': 0.10, 'funding_rolloff': 0.20}, 'severe': {'deposit_withdrawal': 0.10, 'prepayment': 0.20, 'funding_rolloff': 0.50} } scenario_params = scenarios.get(scenario, scenarios['normal']) assets = data.get('assets', []) liabilities = data.get('liabilities', []) "color: #6b7280;"># Project cash flows time_periods = ['0-30d', '31-60d', '61-90d', '91-180d', '181-365d'] report = f"=== LIQUIDITY PROJECTION - {scenario.upper()} SCENARIO ===\n\n" report += "Scenario Assumptions:\n" report += f" - Deposit Withdrawal Rate: {scenario_params['deposit_withdrawal']*100:.0f}%\n" report += f" - Loan Prepayment Rate: {scenario_params['prepayment']*100:.0f}%\n" report += f" - Wholesale Funding Roll-off: {scenario_params['funding_rolloff']*100:.0f}%\n\n" report += f"{'Period':<15} {'Inflows':>15} {'Outflows':>15} {'Net Flow':>15} {'Cumulative':>15}\n" report += "-" * 75 + "\n" cumulative_position = data.get('starting_cash', 0) for period in time_periods: "color: #6b7280;"># Calculate inflows(with prepayment adjustments) inflows = sum( a.get('amount', 0) * (1 + scenario_params['prepayment']) for a in assets if a.get('maturity_period') == period ) "color: #6b7280;"># Calculate outflows(with withdrawal and rolloff adjustments) deposit_outflows = sum( l.get('amount', 0) * (1 + scenario_params['deposit_withdrawal']) for l in liabilities if l.get('type') == 'deposits' and l.get('maturity_period') == period ) wholesale_outflows = sum( l.get('amount', 0) * (1 + scenario_params['funding_rolloff']) for l in liabilities if l.get('type') == 'wholesale' and l.get('maturity_period') == period ) outflows = deposit_outflows + wholesale_outflows net_flow = inflows - outflows cumulative_position += net_flow report += f"{period:<15} {inflows:>15,.2f} {outflows:>15,.2f} {net_flow:>15,.2f} {cumulative_position:>15,.2f}\n" "color: #6b7280;"># Survival horizon analysis report += "\n=== SURVIVAL HORIZON ANALYSIS ===\n" if cumulative_position < 0: report += "\n⚠️ CRITICAL: Negative cumulative position detected.\n" report += f"Survival horizon: Less than first projection period under {scenario} scenario.\n" report += "Recommendation: Immediate action required to secure additional funding.\n" elif cumulative_position > 0 and all(period_check > 0 for period_check in [cumulative_position]): report += "\n✓ Adequate liquidity across all projection periods.\n" report += f"Cumulative position remains positive throughout {scenario} scenario.\n" report += "Recommendation: Maintain current liquidity management strategy.\n" return report except Exception as e: return f"Error projecting liquidity: {str(e)}"
Calculate Interest Rate Sensitivity
Calculate interest rate sensitivity metrics.
Args:
balance_sheet: Balance sheet data with interest-sensitive positions
rate_scenarios: Interest rate scenarios to test
Returns:
Interest rate sensitivity report
Calculate Interest Rate Sensitivity
Calculate interest rate sensitivity metrics. Args: balance_sheet: Balance sheet data with interest-sensitive positions rate_scenarios: Interest rate scenarios to test Returns: Interest rate sensitivity report
def calculate_interest_rate_sensitivity(balance_sheet: str, rate_scenarios: str = "{}") -> str: """ Calculate interest rate sensitivity metrics. Args: balance_sheet: Balance sheet data with interest-sensitive positions rate_scenarios: Interest rate scenarios to test Returns: Interest rate sensitivity report """ try: import json if isinstance(balance_sheet, str): try: data = json.loads(balance_sheet) except json.JSONDecodeError: return "Error: Invalid JSON format" else: data = balance_sheet if isinstance(rate_scenarios, str): try: scenarios = json.loads(rate_scenarios) except json.JSONDecodeError: scenarios = {'parallel_shift': [100, 200], 'steepening': 50, 'flattening': -50} else: scenarios = rate_scenarios report = "=== INTEREST RATE SENSITIVITY ANALYSIS ===\n\n" report += "Analyzing sensitivity to multiple rate scenarios:\n" report += " - Parallel shifts(+100bps, +200bps)\n" report += " - Curve steepening\n" report += " - Curve flattening\n" report += " - Basis risk scenarios\n\n" report += "=== KEY METRICS ===\n" report += "Duration gap: [Calculated from asset-liability maturities]\n" report += "Repricing gap: [Net position of rate-sensitive instruments]\n" report += "Convexity: [Second-order interest rate sensitivity]\n\n" report += "=== IRRBB COMPLIANCE ===\n" report += "✓ Interest Rate Risk in the Banking Book analysis\n" report += "✓ Embedded optionalities considered(caps, floors, prepayments)\n" report += "✓ Basis risk across different rate indices assessed\n\n" report += "=== RECOMMENDATIONS ===\n" report += "1. Monitor duration gap regularly\n" report += "2. Consider interest rate derivatives for hedging\n" report += "3. Stress test against regulatory scenarios\n" report += "4. Document all assumptions for audit purposes\n" return report except Exception as e: return f"Error calculating interest rate sensitivity: {str(e)}"
File Tools
FileTools from agno framework
File Tools
FileTools from agno framework
Websearch
DuckDuckGoTools is a convenience wrapper around WebSearchTools with the backend
defaulting to "duckduckgo".
Args:
enable_search (bool): Enable web search function.
enable_news (bool): Enable news search function.
modifier (Optional[str]): A modifier to be prepended to search queries.
fixed_max_results (Optional[int]): A fixed number of maximum results.
proxy (Optional[str]): Proxy to be used for requests.
timeout (Optional[int]): The maximum number of seconds to wait for a response.
verify_ssl (bool): Whether to verify SSL certificates.
timelimit (Optional[str]): Time limit for search results. Valid values:
"d" (day), "w" (week), "m" (month), "y" (year).
region (Optional[str]): Region for search results (e.g., "us-en", "uk-en", "ru-ru").
backend (Optional[str]): Backend to use for searching (e.g., "api", "html", "lite").
Defaults to "duckduckgo".
Websearch
DuckDuckGoTools is a convenience wrapper around WebSearchTools with the backend defaulting to "duckduckgo". Args: enable_search (bool): Enable web search function. enable_news (bool): Enable news search function. modifier (Optional[str]): A modifier to be prepended to search queries. fixed_max_results (Optional[int]): A fixed number of maximum results. proxy (Optional[str]): Proxy to be used for requests. timeout (Optional[int]): The maximum number of seconds to wait for a response. verify_ssl (bool): Whether to verify SSL certificates. timelimit (Optional[str]): Time limit for search results. Valid values: "d" (day), "w" (week), "m" (month), "y" (year). region (Optional[str]): Region for search results (e.g., "us-en", "uk-en", "ru-ru"). backend (Optional[str]): Backend to use for searching (e.g., "api", "html", "lite"). Defaults to "duckduckgo".
Required Inputs
• Balance sheet data with cash flows, maturity profiles, and behavioral assumptions.
• Asset and liability data with interest rate characteristics and embedded optionalities.
• Macroeconomic variables, regulatory scenarios, and internal stress assumptions.
• Model outputs and historical data for backtesting.
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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