Loan Term Sheet Generation Automation

Generate complete, policy-compliant loan term sheets in minutes instead of hours. This guide shows you how to build an intelligent term sheet automation system that extracts borrower data, applies internal credit policies, and produces professional documentation automatically.

Overview

Time Savings: 85% reduction in term sheet preparation time (8 hours → 1.2 hours per deal) Complexity: Advanced Prerequisites: Familiarity with credit policies, covenant structures, and document generation

Blocks & Tools You'll Use

Core Blocks

• Agent Block - For intelligent data extraction, policy application, and covenant selection
• Knowledge Base Tool - To store credit policies, covenant libraries, and pricing grids
• Vision Tool - To extract financial data from PDFs, charts, and scanned documents
• Function Block - For custom Python logic to calculate metrics and apply pricing rules
• Condition Block - To route deals based on credit tier, loan structure, and compliance checks
• File Tool - To generate formatted term sheet documents (PDF/DOCX)

Supporting Tools

• Response Block - To deliver completed term sheets
• Mail Tool - To send term sheets to credit committee and borrowers
• Slack Tool - For real-time notifications when term sheets are generated

Workflow Overview

Borrower Documents → Data Extraction → Policy Application → Covenant Selection → Document Generation → Compliance Review → Delivery

Step 1: Document Upload & Data Extraction

Create an Agent that extracts key borrower information from financial statements, tax returns, and loan applications.

Agent Configuration

System Prompt:

You are a credit analyst extracting borrower financial data from loan applications and supporting documents.

EXTRACTION REQUIREMENTS:

**Borrower Information:**
- Legal entity name
- Business type (Corporation, LLC, Partnership, etc.)
- Industry sector
- Years in business
- Management team experience

**Financial Metrics (Most Recent Year):**
- Total Revenue
- EBITDA / Operating Income
- Total Debt
- Current Assets / Current Liabilities
- Net Worth / Tangible Net Worth
- Cash Flow from Operations

**Loan Request Details:**
- Requested loan amount
- Proposed use of proceeds
- Requested loan term (months)
- Collateral description and estimated value
- Personal guarantees offered

**Credit Analysis:**
- Leverage Ratio (Total Debt / EBITDA)
- Debt Service Coverage Ratio (EBITDA / Annual Debt Service)
- Current Ratio (Current Assets / Current Liabilities)
- Revenue trend (YoY growth %)

OUTPUT FORMAT (JSON):
{
  "borrower": {
    "legal_name": "...",
    "business_type": "...",
    "industry": "...",
    "years_in_business": 0,
    "management_experience_years": 0
  },
  "financials": {
    "revenue": 0,
    "ebitda": 0,
    "total_debt": 0,
    "current_assets": 0,
    "current_liabilities": 0,
    "tangible_net_worth": 0,
    "operating_cash_flow": 0
  },
  "loan_request": {
    "amount": 0,
    "use_of_proceeds": "...",
    "term_months": 0,
    "collateral_description": "...",
    "collateral_value": 0,
    "guarantees": ["..."]
  },
  "credit_metrics": {
    "leverage_ratio": 0.0,
    "dscr": 0.0,
    "current_ratio": 0.0,
    "revenue_growth_yoy": 0.0
  }
}

Extract data accurately. If a field is not available, use null.

Tools Enabled:

• Vision Tool (to extract data from charts, PDFs, and scanned financial statements)
• File Tool (to read uploaded documents)

Example Input:

• Borrower financial statements (PDF)
• Loan application form (PDF)
• Bank statements (PDF)
• Tax returns (PDF)

Example Output:

{
  "borrower": {
    "legal_name": "Acme Manufacturing LLC",
    "business_type": "Limited Liability Company",
    "industry": "Manufacturing - Industrial Equipment",
    "years_in_business": 12,
    "management_experience_years": 18
  },
  "financials": {
    "revenue": 8500000,
    "ebitda": 1200000,
    "total_debt": 2500000,
    "current_assets": 3200000,
    "current_liabilities": 1800000,
    "tangible_net_worth": 4500000,
    "operating_cash_flow": 950000
  },
  "loan_request": {
    "amount": 1500000,
    "use_of_proceeds": "Equipment acquisition and working capital",
    "term_months": 60,
    "collateral_description": "First lien on all equipment and inventory",
    "collateral_value": 2200000,
    "guarantees": ["Personal guarantee from CEO"]
  },
  "credit_metrics": {
    "leverage_ratio": 2.08,
    "dscr": 1.42,
    "current_ratio": 1.78,
    "revenue_growth_yoy": 8.5
  }
}

Step 2: Credit Tier Classification

Use a Function Block to classify the borrower into a credit tier based on extracted metrics.

Python Function

def classify_credit_tier(credit_metrics, financials, borrower_info):
    """
    Classify borrower into credit tier based on financial metrics.

    Credit Tiers:
    - AAA: Leverage < 2.0, DSCR > 2.0, Current Ratio > 2.0, 10+ years in business
    - AA: Leverage < 2.5, DSCR > 1.75, Current Ratio > 1.5, 7+ years
    - A: Leverage < 3.0, DSCR > 1.5, Current Ratio > 1.3, 5+ years
    - BBB: Leverage < 3.5, DSCR > 1.3, Current Ratio > 1.2, 3+ years
    - BB: Leverage < 4.0, DSCR > 1.2, Current Ratio > 1.0, 2+ years
    - B: Does not meet BB requirements
    """
    leverage = credit_metrics.get('leverage_ratio', 999)
    dscr = credit_metrics.get('dscr', 0)
    current_ratio = credit_metrics.get('current_ratio', 0)
    years_in_business = borrower_info.get('years_in_business', 0)

    if leverage < 2.0 and dscr > 2.0 and current_ratio > 2.0 and years_in_business >= 10:
        return {
            'credit_tier': 'AAA',
            'risk_rating': 'Minimal Risk',
            'tier_score': 95
        }
    elif leverage < 2.5 and dscr > 1.75 and current_ratio > 1.5 and years_in_business >= 7:
        return {
            'credit_tier': 'AA',
            'risk_rating': 'Very Low Risk',
            'tier_score': 85
        }
    elif leverage < 3.0 and dscr > 1.5 and current_ratio > 1.3 and years_in_business >= 5:
        return {
            'credit_tier': 'A',
            'risk_rating': 'Low Risk',
            'tier_score': 75
        }
    elif leverage < 3.5 and dscr > 1.3 and current_ratio > 1.2 and years_in_business >= 3:
        return {
            'credit_tier': 'BBB',
            'risk_rating': 'Moderate Risk',
            'tier_score': 65
        }
    elif leverage < 4.0 and dscr > 1.2 and current_ratio > 1.0 and years_in_business >= 2:
        return {
            'credit_tier': 'BB',
            'risk_rating': 'Elevated Risk',
            'tier_score': 50
        }
    else:
        return {
            'credit_tier': 'B',
            'risk_rating': 'High Risk',
            'tier_score': 35
        }

# Example usage
classification = classify_credit_tier(
    credit_metrics={'leverage_ratio': 2.08, 'dscr': 1.42, 'current_ratio': 1.78},
    financials={'revenue': 8500000, 'ebitda': 1200000},
    borrower_info={'years_in_business': 12}
)
# Returns: {'credit_tier': 'A', 'risk_rating': 'Low Risk', 'tier_score': 75}

Step 3: Policy-Based Pricing & Terms

Store your credit policies in a Knowledge Base and use an Agent to retrieve appropriate pricing and terms.

Knowledge Base Setup

Create a Knowledge Base document with your pricing grid:

# Credit Policy - Pricing Grid

## AAA Tier
- Interest Rate: SOFR + 4.00% to 4.50%
- Origination Fee: 1.00%
- Maximum Leverage: 2.0x
- Minimum DSCR: 2.0x
- Loan-to-Value: Up to 80%
- Reporting: Quarterly financials

## AA Tier
- Interest Rate: SOFR + 4.50% to 5.50%
- Origination Fee: 1.25%
- Maximum Leverage: 2.5x
- Minimum DSCR: 1.75x
- Loan-to-Value: Up to 75%
- Reporting: Quarterly financials

## A Tier
- Interest Rate: SOFR + 5.50% to 6.50%
- Origination Fee: 1.50%
- Maximum Leverage: 3.0x
- Minimum DSCR: 1.5x
- Loan-to-Value: Up to 70%
- Reporting: Quarterly financials

## BBB Tier
- Interest Rate: SOFR + 6.50% to 7.50%
- Origination Fee: 2.00%
- Maximum Leverage: 3.5x
- Minimum DSCR: 1.3x
- Loan-to-Value: Up to 65%
- Reporting: Monthly financials

## BB Tier
- Interest Rate: SOFR + 7.50% to 9.00%
- Origination Fee: 2.50%
- Maximum Leverage: 4.0x
- Minimum DSCR: 1.2x
- Loan-to-Value: Up to 60%
- Reporting: Monthly financials

## B Tier
- Interest Rate: SOFR + 9.00% to 12.00%
- Origination Fee: 3.00%
- Maximum Leverage: 4.5x
- Minimum DSCR: 1.1x
- Loan-to-Value: Up to 50%
- Reporting: Monthly financials + weekly cash reporting

Agent Prompt for Pricing Retrieval

System Prompt:
"You are a credit policy specialist retrieving appropriate loan pricing and terms based on borrower credit tier.

INPUT:
- Credit tier (AAA, AA, A, BBB, BB, B)
- Loan amount
- Loan term
- Collateral value

TASK:
Query the Knowledge Base for pricing guidelines matching the credit tier. Return the applicable interest rate range, fees, and reporting requirements.

OUTPUT FORMAT (JSON):
{
  "pricing": {
    "interest_rate_range": "SOFR + X.XX% to X.XX%",
    "recommended_rate": "SOFR + X.XX%",
    "origination_fee_pct": 0.0,
    "max_leverage": 0.0,
    "min_dscr": 0.0,
    "max_ltv_pct": 0
  },
  "terms": {
    "reporting_frequency": "...",
    "financial_covenants_required": true/false,
    "cash_flow_monitoring": "..."
  }
}"

Use the Knowledge Base tool to retrieve policy information.

Step 4: Covenant Selection

Create an Agent that selects appropriate covenants from your covenant library based on credit tier and loan structure.

Covenant Library (Knowledge Base Document)

# Covenant Library

## Financial Covenants - AAA/AA Tier

### Minimum DSCR Covenant
"Borrower shall maintain a Debt Service Coverage Ratio of not less than 1.50x, tested quarterly."

### Maximum Leverage Covenant
"Borrower shall maintain a Leverage Ratio (Total Funded Debt / EBITDA) of not more than 2.50x, tested quarterly."

### Minimum Tangible Net Worth Covenant
"Borrower shall maintain Tangible Net Worth of not less than $[AMOUNT], tested quarterly."

## Financial Covenants - A/BBB Tier

### Minimum DSCR Covenant
"Borrower shall maintain a Debt Service Coverage Ratio of not less than 1.35x, tested quarterly."

### Maximum Leverage Covenant
"Borrower shall maintain a Leverage Ratio (Total Funded Debt / EBITDA) of not more than 3.00x, tested quarterly."

### Minimum Current Ratio Covenant
"Borrower shall maintain a Current Ratio of not less than 1.25x, tested quarterly."

### Capital Expenditure Limit
"Borrower shall not make Capital Expenditures exceeding $[AMOUNT] in any fiscal year without Lender consent."

## Financial Covenants - BB/B Tier

### Minimum DSCR Covenant
"Borrower shall maintain a Debt Service Coverage Ratio of not less than 1.20x, tested monthly."

### Maximum Leverage Covenant
"Borrower shall maintain a Leverage Ratio (Total Funded Debt / EBITDA) of not more than 3.75x, tested monthly."

### Minimum Liquidity Covenant
"Borrower shall maintain unrestricted cash and cash equivalents of not less than $[AMOUNT] at all times."

### Revenue Floor Covenant
"Borrower's trailing 12-month revenue shall not be less than 85% of prior year revenue."

## Affirmative Covenants (All Tiers)

### Financial Reporting
"Borrower shall provide Lender with: (a) quarterly unaudited financial statements within 45 days of quarter end, (b) annual audited financial statements within 120 days of fiscal year end."

### Compliance Certificates
"Borrower shall provide a Compliance Certificate with each financial statement delivery, certifying compliance with all loan covenants."

### Insurance Maintenance
"Borrower shall maintain comprehensive general liability, property, and business interruption insurance with limits acceptable to Lender."

## Negative Covenants (All Tiers)

### Prohibition on Additional Debt
"Borrower shall not incur additional Funded Debt without Lender's prior written consent, except for: (a) trade payables in the ordinary course, (b) purchase money debt not exceeding $[AMOUNT] annually."

### Prohibition on Distributions
"Borrower shall not make distributions, dividends, or equity repurchases if (a) an Event of Default exists, or (b) such distribution would cause a covenant violation on a pro forma basis."

### Prohibition on Asset Sales
"Borrower shall not sell, transfer, or dispose of assets outside the ordinary course of business without Lender consent."

Agent Prompt for Covenant Selection

System Prompt:
"You are a credit documentation specialist selecting appropriate loan covenants based on borrower credit tier and loan structure.

INPUT:
- Credit tier
- Loan amount
- Borrower financial metrics
- Loan term and structure

TASK:
Query the Covenant Library (Knowledge Base) and select appropriate financial covenants, affirmative covenants, and negative covenants. Ensure covenant thresholds align with the borrower's current financial position and credit tier requirements.

COVENANT SELECTION RULES:
1. All tiers require minimum DSCR, maximum leverage, and tangible net worth covenants
2. BB/B tiers require monthly testing instead of quarterly
3. BB/B tiers require additional liquidity and revenue floor covenants
4. Insert actual dollar amounts based on borrower financials (e.g., TNW covenant set at 90% of current TNW)
5. Include all standard affirmative and negative covenants

OUTPUT FORMAT (JSON):
{
  "financial_covenants": [
    {
      "covenant_name": "...",
      "covenant_text": "...",
      "test_frequency": "...",
      "threshold": "..."
    }
  ],
  "affirmative_covenants": ["..."],
  "negative_covenants": ["..."],
  "reporting_requirements": ["..."]
}"

Use the Knowledge Base tool to retrieve covenant language.

Step 5: Term Sheet Document Generation

Use an Agent with Function Block to generate the complete term sheet document.

Agent Prompt for Document Generation

System Prompt:
"You are a senior credit documentation specialist drafting a professional loan term sheet.

TERM SHEET STRUCTURE:

**I. BORROWER INFORMATION**
- Legal name, address, business type
- Industry and years in operation
- Key financial metrics summary

**II. LOAN STRUCTURE**
- Loan amount
- Interest rate and pricing
- Loan term and amortization
- Origination fees and closing costs
- Prepayment terms

**III. COLLATERAL & GUARANTEES**
- Collateral description
- Lien position and priority
- Loan-to-value calculation
- Personal guarantees required

**IV. FINANCIAL COVENANTS**
- List each financial covenant with testing frequency
- Specify calculation methodology
- Include initial compliance baseline

**V. AFFIRMATIVE COVENANTS**
- Financial reporting requirements
- Insurance requirements
- Compliance certificate delivery

**VI. NEGATIVE COVENANTS**
- Restrictions on additional debt
- Restrictions on distributions
- Asset sale restrictions
- Change of control provisions

**VII. EVENTS OF DEFAULT**
- Payment default
- Covenant default (with cure periods)
- Bankruptcy or insolvency
- Material adverse change

**VIII. CONDITIONS PRECEDENT**
- Loan documentation
- Legal opinions
- Insurance certificates
- UCC filings and lien perfection

**IX. OTHER TERMS**
- Governing law
- Amendment provisions
- Confidentiality
- Exclusivity period (if applicable)

OUTPUT:
Generate a complete, professional term sheet in markdown format with all sections populated based on the borrower data, credit tier, pricing, and selected covenants.

Use clear, precise language. Include specific dollar amounts, percentages, and dates. Ensure all covenant thresholds are explicitly stated."

Python Function for Document Formatting

def format_term_sheet(borrower_data, pricing, covenants, loan_terms):
    """
    Format the term sheet as a professional document (PDF/DOCX).

    This function takes the structured data and generates a formatted document.
    """
    from datetime import date

    term_sheet = f"""
CONFIDENTIAL LOAN TERM SHEET

Date: {date.today().strftime('%B %d, %Y')}

**BORROWER INFORMATION**
Legal Name: {borrower_data['borrower']['legal_name']}
Business Type: {borrower_data['borrower']['business_type']}
Industry: {borrower_data['borrower']['industry']}
Years in Business: {borrower_data['borrower']['years_in_business']}

**FINANCIAL SUMMARY**
Annual Revenue: ${borrower_data['financials']['revenue']:,.0f}
EBITDA: ${borrower_data['financials']['ebitda']:,.0f}
Total Debt: ${borrower_data['financials']['total_debt']:,.0f}
Tangible Net Worth: ${borrower_data['financials']['tangible_net_worth']:,.0f}

Credit Tier: {loan_terms['credit_tier']}
Leverage Ratio: {borrower_data['credit_metrics']['leverage_ratio']:.2f}x
DSCR: {borrower_data['credit_metrics']['dscr']:.2f}x

---

**I. LOAN STRUCTURE**

Loan Amount: ${borrower_data['loan_request']['amount']:,.0f}

Interest Rate: {pricing['pricing']['recommended_rate']}
- Base Rate: SOFR (30-day average)
- Margin: {pricing['pricing']['recommended_rate'].split('+')[1].strip()}
- Rate Floor: SOFR subject to a floor of 0.00%

Loan Term: {borrower_data['loan_request']['term_months']} months

Amortization: {loan_terms.get('amortization', '60-month straight-line amortization')}

Origination Fee: {pricing['pricing']['origination_fee_pct']:.2f}% (${borrower_data['loan_request']['amount'] * pricing['pricing']['origination_fee_pct'] / 100:,.0f})

Prepayment: Permitted at any time without penalty after 12 months. Prepayments within first 12 months subject to make-whole premium.

---

**II. COLLATERAL & GUARANTEES**

Collateral: {borrower_data['loan_request']['collateral_description']}
Estimated Collateral Value: ${borrower_data['loan_request']['collateral_value']:,.0f}
Loan-to-Value: {(borrower_data['loan_request']['amount'] / borrower_data['loan_request']['collateral_value'] * 100):.1f}%

Guarantees: {', '.join(borrower_data['loan_request']['guarantees'])}

---

**III. FINANCIAL COVENANTS**
"""

    # Add financial covenants
    for covenant in covenants['financial_covenants']:
        term_sheet += f"\n{covenant['covenant_name']}:\n{covenant['covenant_text']}\nTested: {covenant['test_frequency']}\n"

    term_sheet += """
---

**IV. AFFIRMATIVE COVENANTS**
"""
    for covenant in covenants['affirmative_covenants']:
        term_sheet += f"\n- {covenant}"

    term_sheet += """

---

**V. NEGATIVE COVENANTS**
"""
    for covenant in covenants['negative_covenants']:
        term_sheet += f"\n- {covenant}"

    term_sheet += f"""

---

**VI. REPORTING REQUIREMENTS**

Frequency: {pricing['terms']['reporting_frequency']}

Required Deliverables:
"""
    for requirement in covenants['reporting_requirements']:
        term_sheet += f"\n- {requirement}"

    term_sheet += """

---

**VII. EVENTS OF DEFAULT**

1. Payment Default: Failure to pay principal, interest, or fees within 5 business days of due date
2. Covenant Default: Failure to comply with any financial or other covenant (30-day cure period for non-financial covenants)
3. Representation Default: Any representation or warranty proves to be materially incorrect
4. Bankruptcy/Insolvency: Filing of bankruptcy, insolvency, or receivership proceedings
5. Cross-Default: Default under any other loan agreement with outstanding principal exceeding $100,000
6. Material Adverse Change: Any event that has a material adverse effect on Borrower's business, financial condition, or ability to repay

---

**VIII. CONDITIONS PRECEDENT**

1. Execution of definitive loan documentation satisfactory to Lender
2. Delivery of legal opinions from Borrower's counsel
3. Evidence of insurance coverage with Lender named as loss payee/additional insured
4. Perfection of security interests via UCC-1 filings
5. Satisfactory environmental assessment (Phase I)
6. Payment of all closing costs and legal fees

---

**IX. OTHER TERMS**

Governing Law: [State]
Exclusivity: Borrower agrees to negotiate exclusively with Lender for 45 days from date of this term sheet
Confidentiality: Terms of this term sheet are confidential
Expiration: This term sheet expires 30 days from date unless extended by Lender

---

**DISCLAIMER**

This term sheet is provided for discussion purposes only and does not constitute a commitment to lend. Any loan will be subject to satisfactory completion of due diligence, credit committee approval, and execution of definitive documentation acceptable to Lender.

"""

    return term_sheet

# Example usage with actual data
# formatted_document = format_term_sheet(borrower_data, pricing, covenants, loan_terms)
# Save to PDF or send via email

Step 6: Compliance Review Check

Use a Condition Block to verify the term sheet meets all internal policy requirements before delivery.

Validation Logic

def validate_term_sheet_compliance(borrower_data, pricing, loan_terms, credit_tier):
    """
    Validate that the generated term sheet complies with internal policies.

    Returns: {'compliant': True/False, 'issues': [...]}
    """
    issues = []

    # Check 1: Loan amount within credit tier limits
    max_loan_by_tier = {
        'AAA': 10000000,
        'AA': 7500000,
        'A': 5000000,
        'BBB': 3000000,
        'BB': 2000000,
        'B': 1000000
    }

    if borrower_data['loan_request']['amount'] > max_loan_by_tier.get(credit_tier, 0):
        issues.append(f"Loan amount exceeds maximum for {credit_tier} tier")

    # Check 2: LTV within policy limits
    ltv = (borrower_data['loan_request']['amount'] / borrower_data['loan_request']['collateral_value']) * 100
    max_ltv = pricing['pricing']['max_ltv_pct']

    if ltv > max_ltv:
        issues.append(f"LTV {ltv:.1f}% exceeds maximum {max_ltv}% for {credit_tier} tier")

    # Check 3: DSCR meets minimum
    if borrower_data['credit_metrics']['dscr'] < pricing['pricing']['min_dscr']:
        issues.append(f"DSCR {borrower_data['credit_metrics']['dscr']:.2f}x below minimum {pricing['pricing']['min_dscr']}x")

    # Check 4: Leverage within limits
    if borrower_data['credit_metrics']['leverage_ratio'] > pricing['pricing']['max_leverage']:
        issues.append(f"Leverage {borrower_data['credit_metrics']['leverage_ratio']:.2f}x exceeds maximum {pricing['pricing']['max_leverage']}x")

    # Check 5: Required guarantees present
    if not borrower_data['loan_request']['guarantees']:
        issues.append("No personal guarantees provided")

    return {
        'compliant': len(issues) == 0,
        'issues': issues,
        'review_status': 'APPROVED' if len(issues) == 0 else 'REQUIRES_MANUAL_REVIEW'
    }

Route compliant term sheets directly to delivery. Route non-compliant term sheets to manual review queue.

Step 7: Automated Delivery

Use Response Block + Mail Tool to deliver the completed term sheet.

Email Template

To: {credit_committee_email}, {loan_officer_email}
Subject: Term Sheet Generated - {borrower_name} - ${loan_amount:,.0f}

A new term sheet has been generated and is ready for review.

**Borrower:** {borrower_name}
**Loan Amount:** ${loan_amount:,.0f}
**Credit Tier:** {credit_tier}
**Interest Rate:** {interest_rate}
**Compliance Status:** {compliance_status}

The complete term sheet is attached.

**Next Steps:**
1. Review term sheet for accuracy
2. Confirm pricing aligns with current market conditions
3. Approve for delivery to borrower or request modifications

---
Generated automatically by Klyntos

For borrower delivery (after credit committee approval):

To: {borrower_contact_email}
Subject: Loan Term Sheet - {borrower_name}

Dear {borrower_contact_name},

We are pleased to provide the attached term sheet for your review.

**Loan Summary:**
- Loan Amount: ${loan_amount:,.0f}
- Interest Rate: {interest_rate}
- Loan Term: {loan_term} months
- Collateral: {collateral_description}

Please review the attached term sheet carefully. We are available to discuss any questions you may have.

This term sheet is valid for 30 days from the date shown.

Best regards,
{loan_officer_name}
{lender_name}

Step 8: Workflow Integration & Monitoring

Complete Workflow

1. Document Upload (File Tool)
   ↓
2. Data Extraction Agent (Vision Tool + Agent Block)
   ↓
3. Credit Tier Classification (Function Block)
   ↓
4. Pricing Retrieval (Knowledge Base + Agent Block)
   ↓
5. Covenant Selection (Knowledge Base + Agent Block)
   ↓
6. Document Generation (Agent Block + Function Block)
   ↓
7. Compliance Validation (Condition Block + Function Block)
   ↓
8a. If Compliant → Deliver to Credit Committee (Mail Tool)
8b. If Non-Compliant → Route to Manual Review (Slack Tool)
   ↓
9. After Approval → Send to Borrower (Mail Tool + Response Block)

Monitoring & Notifications (Slack Integration)

def send_slack_notification(channel, borrower_name, loan_amount, credit_tier, status):
    """
    Send Slack notification when term sheet is generated.
    """
    message = {
        'channel': channel,
        'blocks': [
            {
                'type': 'header',
                'text': {
                    'type': 'plain_text',
                    'text': f'Term Sheet Generated: {borrower_name}'
                }
            },
            {
                'type': 'section',
                'fields': [
                    {'type': 'mrkdwn', 'text': f'*Loan Amount:*\n${loan_amount:,.0f}'},
                    {'type': 'mrkdwn', 'text': f'*Credit Tier:*\n{credit_tier}'},
                    {'type': 'mrkdwn', 'text': f'*Status:*\n{status}'},
                    {'type': 'mrkdwn', 'text': f'*Generated:*\n{datetime.now().strftime("%Y-%m-%d %H:%M")}'}
                ]
            }
        ]
    }
    return message

Testing Your Workflow

Test Case 1: AAA Tier Borrower

Input:

• Revenue: $15M
• EBITDA: $3.5M
• Total Debt: $5M
• Years in Business: 15
• Loan Request: $2M for equipment acquisition

Expected Output:

• Credit Tier: AAA
• Interest Rate: SOFR + 4.25%
• Origination Fee: 1.00%
• Quarterly reporting
• Standard financial covenants (DSCR > 2.0x, Leverage < 2.0x)
• Term sheet generated in < 2 minutes

Test Case 2: BBB Tier Borrower

Input:

• Revenue: $5M
• EBITDA: $600K
• Total Debt: $1.8M
• Years in Business: 4
• Loan Request: $750K for working capital

Expected Output:

• Credit Tier: BBB
• Interest Rate: SOFR + 7.00%
• Origination Fee: 2.00%
• Monthly reporting
• Enhanced covenants (DSCR > 1.3x, Leverage < 3.5x, minimum liquidity)
• Term sheet generated in < 2 minutes

Test Case 3: Non-Compliant Deal (Should Route to Manual Review)

Input:

• Revenue: $2M
• EBITDA: $200K
• Total Debt: $900K
• Loan Request: $500K
• Leverage: 4.5x (exceeds policy maximum)

Expected Output:

• Compliance validation fails
• Routes to manual review queue
• Slack notification sent to credit team
• Issues flagged: "Leverage 4.5x exceeds maximum 4.0x for BB tier"

Test Case 4: Document Extraction Accuracy

Input:

• Scanned financial statements with charts/graphs
• Multi-page tax returns
• Bank statements with varying formats

Expected Output:

• Vision Tool accurately extracts revenue, EBITDA, debt levels
• All required fields populated correctly
• No manual data entry required

Advanced Enhancements

1. Market Rate Integration

Connect to real-time SOFR rate feeds to calculate actual all-in rates in the term sheet.

def get_current_sofr_rate():
    """Fetch current 30-day SOFR rate from Fed API"""
    # Integration with Federal Reserve Economic Data (FRED) API
    pass

2. Historical Deal Comparison

Query past term sheets to ensure pricing consistency for similar credit profiles.

def compare_to_historical_deals(borrower_profile, knowledge_base):
    """
    Query Knowledge Base for similar historical deals and compare pricing.
    Flags deals that deviate significantly from historical norms.
    """
    pass

3. Interactive Term Sheet Negotiation

Build a chatbot interface where borrowers can ask questions about term sheet provisions and receive automated explanations.

4. Portfolio-Level Analytics

Track all generated term sheets to identify pricing drift, covenant trends, and credit tier distribution across your portfolio.

ROI Metrics

Metric	Before Klyntos	After Klyntos	Improvement
Time per term sheet	8 hours	1.2 hours	85% reduction
Term sheets per month	20	75	275% increase
Policy compliance	78% (manual errors)	100%	22% improvement
Borrower response time	3-5 days	Same day	80% faster
Legal amendment requests	15% of deals	3% of deals	80% reduction

Troubleshooting

Issue: Data extraction missing key fields Solution: Ensure uploaded documents are clear and legible. Use Vision Tool with high-quality PDFs. Add fallback prompts asking Agent to request missing data.

Issue: Pricing not matching internal guidelines Solution: Verify Knowledge Base contains current pricing grid. Check credit tier classification logic for accuracy.

Issue: Covenant language inconsistent Solution: Standardize Covenant Library with approved language. Use exact text retrieval instead of paraphrasing.

Issue: Compliance validation false positives Solution: Review validation thresholds in Function Block. Ensure calculation methodologies match internal definitions (e.g., how EBITDA is calculated).

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Next Steps

1. Document your credit policies - Gather pricing grids, covenant requirements, and underwriting guidelines
2. Build your Covenant Library - Compile approved covenant language for all credit tiers
3. Test with historical deals - Run past term sheets through the system to validate accuracy
4. Train your team - Show credit analysts and loan officers how to use the automated system
5. Monitor and refine - Track compliance accuracy and generation speed; adjust prompts as needed

For implementation support, visit the Klyntos documentation or contact our team.