Use Cases

Product-FARM is a domain-agnostic rule engine that can power various business applications. This document provides detailed examples across different industries.

Table of Contents

Insurance Premium Calculation

Calculate insurance premiums based on customer demographics, coverage options, and risk factors.

Business Requirements

  • Base premium calculated from coverage amount
  • Age-based risk adjustments
  • Lifestyle factors (smoking, BMI)
  • Coverage type multipliers
  • Multi-policy discounts

Data Model

flowchart LR
    subgraph Product["🏦 INSURANCE PRODUCT"]
        direction TB
        subgraph Inputs["📥 INPUTS"]
            I1["customer_age: INT"]
            I2["coverage: DECIMAL"]
            I3["smoker: BOOL"]
            I4["bmi: DECIMAL"]
            I5["coverage_type: ENUM"]
            I6["policy_count: INT"]
        end

        subgraph Calculated["⚙️ CALCULATED"]
            C1["base_premium"]
            C2["age_factor"]
            C3["smoker_factor"]
            C4["bmi_factor"]
            C5["type_multiplier"]
            C6["discount_factor"]
        end

        subgraph Output["📤 OUTPUT"]
            O1["final_premium: DECIMAL"]
        end
    end

    Inputs --> Calculated --> Output

    style Product fill:#0f172a,stroke:#3b82f6,color:#fff
    style Inputs fill:#1e3a5f,stroke:#3b82f6,color:#fff
    style Calculated fill:#4c1d95,stroke:#8b5cf6,color:#fff
    style Output fill:#065f46,stroke:#10b981,color:#fff

Rules Configuration

Rule 1: Base Premium

{
  "rule_type": "BASE_CALCULATION",
  "display_expression": "base_premium = coverage × 0.02",
  "expression": {
    "*": [{"var": "coverage"}, 0.02]
  },
  "inputs": ["coverage"],
  "outputs": ["base_premium"]
}

Rule 2: Age Factor

{
  "rule_type": "RISK_FACTOR",
  "display_expression": "age_factor = IF age > 70 THEN 1.5 ELSE IF age > 60 THEN 1.2 ELSE IF age > 50 THEN 1.1 ELSE 1.0",
  "expression": {
    "if": [
      {">": [{"var": "customer_age"}, 70]}, 1.5,
      {">": [{"var": "customer_age"}, 60]}, 1.2,
      {">": [{"var": "customer_age"}, 50]}, 1.1,
      1.0
    ]
  },
  "inputs": ["customer_age"],
  "outputs": ["age_factor"]
}

Rule 3: Smoker Factor

{
  "rule_type": "RISK_FACTOR",
  "display_expression": "smoker_factor = IF smoker THEN 1.5 ELSE 1.0",
  "expression": {
    "if": [{"var": "smoker"}, 1.5, 1.0]
  },
  "inputs": ["smoker"],
  "outputs": ["smoker_factor"]
}

Rule 4: BMI Factor

{
  "rule_type": "RISK_FACTOR",
  "display_expression": "bmi_factor = IF bmi > 35 THEN 1.3 ELSE IF bmi > 30 THEN 1.15 ELSE 1.0",
  "expression": {
    "if": [
      {">": [{"var": "bmi"}, 35]}, 1.3,
      {">": [{"var": "bmi"}, 30]}, 1.15,
      1.0
    ]
  },
  "inputs": ["bmi"],
  "outputs": ["bmi_factor"]
}

Rule 5: Coverage Type Multiplier

{
  "rule_type": "PRODUCT_CONFIG",
  "display_expression": "type_multiplier based on coverage_type enum",
  "expression": {
    "if": [
      {"==": [{"var": "coverage_type"}, "COMPREHENSIVE"]}, 1.5,
      {"==": [{"var": "coverage_type"}, "STANDARD"]}, 1.2,
      {"==": [{"var": "coverage_type"}, "BASIC"]}, 1.0,
      1.0
    ]
  },
  "inputs": ["coverage_type"],
  "outputs": ["type_multiplier"]
}

Rule 6: Multi-Policy Discount

{
  "rule_type": "DISCOUNT",
  "display_expression": "discount = IF policies >= 3 THEN 0.85 ELSE IF policies >= 2 THEN 0.90 ELSE 1.0",
  "expression": {
    "if": [
      {">=": [{"var": "policy_count"}, 3]}, 0.85,
      {">=": [{"var": "policy_count"}, 2]}, 0.90,
      1.0
    ]
  },
  "inputs": ["policy_count"],
  "outputs": ["discount_factor"]
}

Rule 7: Final Premium

{
  "rule_type": "FINAL_CALCULATION",
  "display_expression": "final_premium = base × age × smoker × bmi × type × discount",
  "expression": {
    "*": [
      {"var": "base_premium"},
      {"var": "age_factor"},
      {"var": "smoker_factor"},
      {"var": "bmi_factor"},
      {"var": "type_multiplier"},
      {"var": "discount_factor"}
    ]
  },
  "inputs": ["base_premium", "age_factor", "smoker_factor", "bmi_factor", "type_multiplier", "discount_factor"],
  "outputs": ["final_premium"]
}

Execution DAG

flowchart TB
    subgraph Inputs["📥 Input Variables"]
        direction LR
        In1["coverage"]
        In2["customer_age"]
        In3["smoker"]
        In4["bmi"]
        In5["coverage_type"]
        In6["policy_count"]
    end

    subgraph L0["⚡ Level 0 - Parallel Execution"]
        direction LR
        R1["Rule 1<br/><b>base</b>"]
        R2["Rule 2<br/><b>age</b>"]
        R3["Rule 3<br/><b>smoker</b>"]
        R4["Rule 4<br/><b>bmi</b>"]
        R5["Rule 5<br/><b>type</b>"]
        R6["Rule 6<br/><b>disc</b>"]
    end

    subgraph L1["🔗 Level 1"]
        R7["Rule 7<br/><b>final</b>"]
    end

    subgraph Output["📤 Output"]
        Out1["final_premium"]
    end

    In1 --> R1
    In2 --> R2
    In3 --> R3
    In4 --> R4
    In5 --> R5
    In6 --> R6

    R1 --> R7
    R2 --> R7
    R3 --> R7
    R4 --> R7
    R5 --> R7
    R6 --> R7
    R7 --> Out1

    style L0 fill:#065f46,stroke:#10b981,color:#fff
    style L1 fill:#4c1d95,stroke:#8b5cf6,color:#fff
    style Output fill:#1e3a5f,stroke:#3b82f6,color:#fff

Example Evaluation

Input:

{
  "customer_age": 65,
  "coverage": 250000,
  "smoker": false,
  "bmi": 28,
  "coverage_type": "STANDARD",
  "policy_count": 2
}

Execution:

Level Rule Calculation Result
0 (parallel) Rule 1 250000 × 0.02 base_premium = 5000
0 Rule 2 age 65 > 60 age_factor = 1.2
0 Rule 3 smoker false smoker_factor = 1.0
0 Rule 4 bmi 28 ≤ 30 bmi_factor = 1.0
0 Rule 5 STANDARD type_multiplier = 1.2
0 Rule 6 2 policies discount_factor = 0.90
1 Rule 7 5000 × 1.2 × 1.0 × 1.0 × 1.2 × 0.90 final_premium = 6480

Output:

{
  "base_premium": 5000,
  "age_factor": 1.2,
  "smoker_factor": 1.0,
  "bmi_factor": 1.0,
  "type_multiplier": 1.2,
  "discount_factor": 0.90,
  "final_premium": 6480
}

Trading Signal Generation

Generate buy/sell signals based on technical indicators and market conditions.

Business Requirements

  • RSI-based oversold/overbought detection
  • Moving average trend confirmation
  • Volume spike detection
  • Multi-condition entry/exit signals
  • Position sizing based on risk

Data Model

flowchart LR
    subgraph Strategy["📈 TRADING STRATEGY"]
        direction TB
        subgraph MarketData["📊 MARKET DATA"]
            M1["price: DECIMAL"]
            M2["rsi_14: DECIMAL"]
            M3["sma_50: DECIMAL"]
            M4["sma_200: DECIMAL"]
            M5["volume: INT"]
            M6["avg_volume: INT"]
            M7["account_size: DECIMAL"]
        end

        subgraph Calculated["⚙️ CALCULATED"]
            C1["oversold: BOOL"]
            C2["overbought: BOOL"]
            C3["above_trend: BOOL"]
            C4["volume_spike: BOOL"]
            C5["risk_pct: DECIMAL"]
        end

        subgraph Outputs["📤 OUTPUTS"]
            O1["entry_signal: ENUM<br/>(BUY, SELL, HOLD)"]
            O2["position_size: DECIMAL"]
        end
    end

    MarketData --> Calculated --> Outputs

    style Strategy fill:#0f172a,stroke:#3b82f6,color:#fff
    style MarketData fill:#1e3a5f,stroke:#3b82f6,color:#fff
    style Calculated fill:#4c1d95,stroke:#8b5cf6,color:#fff
    style Outputs fill:#065f46,stroke:#10b981,color:#fff

Rules Configuration

Rule 1: Oversold Detection

{
  "rule_type": "INDICATOR",
  "display_expression": "oversold = RSI < 30",
  "expression": {
    "<": [{"var": "rsi_14"}, 30]
  },
  "inputs": ["rsi_14"],
  "outputs": ["oversold"]
}

Rule 2: Overbought Detection

{
  "rule_type": "INDICATOR",
  "display_expression": "overbought = RSI > 70",
  "expression": {
    ">": [{"var": "rsi_14"}, 70]
  },
  "inputs": ["rsi_14"],
  "outputs": ["overbought"]
}

Rule 3: Trend Confirmation

{
  "rule_type": "INDICATOR",
  "display_expression": "above_trend = price > SMA50 AND SMA50 > SMA200",
  "expression": {
    "and": [
      {">": [{"var": "price"}, {"var": "sma_50"}]},
      {">": [{"var": "sma_50"}, {"var": "sma_200"}]}
    ]
  },
  "inputs": ["price", "sma_50", "sma_200"],
  "outputs": ["above_trend"]
}

Rule 4: Volume Spike

{
  "rule_type": "INDICATOR",
  "display_expression": "volume_spike = volume > 1.5 × avg_volume",
  "expression": {
    ">": [
      {"var": "volume"},
      {"*": [{"var": "avg_volume"}, 1.5]}
    ]
  },
  "inputs": ["volume", "avg_volume"],
  "outputs": ["volume_spike"]
}

Rule 5: Entry Signal

{
  "rule_type": "SIGNAL",
  "display_expression": "BUY if oversold AND above_trend AND volume_spike, SELL if overbought AND NOT above_trend, else HOLD",
  "expression": {
    "if": [
      {"and": [
        {"var": "oversold"},
        {"var": "above_trend"},
        {"var": "volume_spike"}
      ]}, "BUY",
      {"and": [
        {"var": "overbought"},
        {"!": [{"var": "above_trend"}]}
      ]}, "SELL",
      "HOLD"
    ]
  },
  "inputs": ["oversold", "overbought", "above_trend", "volume_spike"],
  "outputs": ["entry_signal"]
}

Rule 6: Position Size

{
  "rule_type": "RISK_MANAGEMENT",
  "display_expression": "position_size = IF signal = BUY THEN account × 2% / price ELSE 0",
  "expression": {
    "if": [
      {"==": [{"var": "entry_signal"}, "BUY"]},
      {"/": [
        {"*": [{"var": "account_size"}, 0.02]},
        {"var": "price"}
      ]},
      0
    ]
  },
  "inputs": ["entry_signal", "account_size", "price"],
  "outputs": ["position_size"]
}

Example Evaluation

Input:

{
  "price": 150.50,
  "rsi_14": 28,
  "sma_50": 148.00,
  "sma_200": 145.00,
  "volume": 2500000,
  "avg_volume": 1500000,
  "account_size": 100000
}

Output:

{
  "oversold": true,
  "overbought": false,
  "above_trend": true,
  "volume_spike": true,
  "entry_signal": "BUY",
  "position_size": 13.29
}

Loan Eligibility Assessment

Evaluate loan applications based on credit scores, income, and debt ratios.

Business Requirements

  • Credit score thresholds
  • Income requirements
  • Debt-to-income ratio limits
  • Employment verification
  • Maximum loan amount calculation

Rules Configuration

Rule 1: Credit Score Check

{
  "rule_type": "ELIGIBILITY",
  "expression": {
    "if": [
      {">=": [{"var": "credit_score"}, 750]}, "EXCELLENT",
      {">=": [{"var": "credit_score"}, 700]}, "GOOD",
      {">=": [{"var": "credit_score"}, 650]}, "FAIR",
      {">=": [{"var": "credit_score"}, 600]}, "POOR",
      "REJECTED"
    ]
  },
  "inputs": ["credit_score"],
  "outputs": ["credit_tier"]
}

Rule 2: Income Verification

{
  "rule_type": "ELIGIBILITY",
  "expression": {
    "and": [
      {">=": [{"var": "annual_income"}, 30000]},
      {">=": [{"var": "employment_months"}, 24]}
    ]
  },
  "inputs": ["annual_income", "employment_months"],
  "outputs": ["income_verified"]
}

Rule 3: Debt-to-Income Ratio

{
  "rule_type": "CALCULATION",
  "expression": {
    "/": [
      {"var": "monthly_debt"},
      {"/": [{"var": "annual_income"}, 12]}
    ]
  },
  "inputs": ["monthly_debt", "annual_income"],
  "outputs": ["dti_ratio"]
}

Rule 4: DTI Check

{
  "rule_type": "ELIGIBILITY",
  "expression": {
    "<=": [{"var": "dti_ratio"}, 0.43]
  },
  "inputs": ["dti_ratio"],
  "outputs": ["dti_acceptable"]
}

Rule 5: Final Eligibility

{
  "rule_type": "DECISION",
  "expression": {
    "and": [
      {"!=": [{"var": "credit_tier"}, "REJECTED"]},
      {"var": "income_verified"},
      {"var": "dti_acceptable"}
    ]
  },
  "inputs": ["credit_tier", "income_verified", "dti_acceptable"],
  "outputs": ["eligible"]
}

Rule 6: Max Loan Amount

{
  "rule_type": "CALCULATION",
  "expression": {
    "if": [
      {"var": "eligible"},
      {"*": [
        {"var": "annual_income"},
        {"if": [
          {"==": [{"var": "credit_tier"}, "EXCELLENT"]}, 5,
          {"==": [{"var": "credit_tier"}, "GOOD"]}, 4,
          {"==": [{"var": "credit_tier"}, "FAIR"]}, 3,
          2
        ]}
      ]},
      0
    ]
  },
  "inputs": ["eligible", "annual_income", "credit_tier"],
  "outputs": ["max_loan_amount"]
}

Example Evaluation

Input:

{
  "credit_score": 720,
  "annual_income": 75000,
  "employment_months": 36,
  "monthly_debt": 2000
}

Output:

{
  "credit_tier": "GOOD",
  "income_verified": true,
  "dti_ratio": 0.32,
  "dti_acceptable": true,
  "eligible": true,
  "max_loan_amount": 300000
}

E-commerce Pricing Engine

Dynamic pricing based on customer segments, inventory, and promotions.

Rules Configuration

Rule 1: Customer Segment Discount

{
  "rule_type": "DISCOUNT",
  "expression": {
    "if": [
      {"==": [{"var": "customer_tier"}, "PLATINUM"]}, 0.20,
      {"==": [{"var": "customer_tier"}, "GOLD"]}, 0.15,
      {"==": [{"var": "customer_tier"}, "SILVER"]}, 0.10,
      0.0
    ]
  },
  "inputs": ["customer_tier"],
  "outputs": ["tier_discount"]
}

Rule 2: Inventory-Based Pricing

{
  "rule_type": "PRICING",
  "expression": {
    "if": [
      {"<": [{"var": "stock_level"}, 10]}, 1.10,
      {"<": [{"var": "stock_level"}, 50]}, 1.05,
      {">": [{"var": "stock_level"}, 200]}, 0.95,
      1.0
    ]
  },
  "inputs": ["stock_level"],
  "outputs": ["inventory_factor"]
}

Rule 3: Promotional Price

{
  "rule_type": "PRICING",
  "expression": {
    "*": [
      {"var": "base_price"},
      {"var": "inventory_factor"},
      {"-": [1, {"var": "tier_discount"}]},
      {"-": [1, {"var": "promo_discount"}]}
    ]
  },
  "inputs": ["base_price", "inventory_factor", "tier_discount", "promo_discount"],
  "outputs": ["final_price"]
}

Healthcare Risk Scoring

Calculate patient risk scores for preventive care.

Rules Configuration

Rule 1: Age Risk

{
  "rule_type": "RISK_FACTOR",
  "expression": {
    "if": [
      {">": [{"var": "age"}, 70]}, 30,
      {">": [{"var": "age"}, 60]}, 20,
      {">": [{"var": "age"}, 50]}, 10,
      5
    ]
  },
  "inputs": ["age"],
  "outputs": ["age_risk_score"]
}

Rule 2: Condition Risk

{
  "rule_type": "RISK_FACTOR",
  "expression": {
    "+": [
      {"if": [{"var": "has_diabetes"}, 25, 0]},
      {"if": [{"var": "has_hypertension"}, 20, 0]},
      {"if": [{"var": "has_heart_disease"}, 30, 0]},
      {"if": [{"var": "smoker"}, 15, 0]}
    ]
  },
  "inputs": ["has_diabetes", "has_hypertension", "has_heart_disease", "smoker"],
  "outputs": ["condition_risk_score"]
}

Rule 3: Total Risk Score

{
  "rule_type": "CALCULATION",
  "expression": {
    "min": [
      {"+": [
        {"var": "age_risk_score"},
        {"var": "condition_risk_score"},
        {"var": "lifestyle_risk_score"}
      ]},
      100
    ]
  },
  "inputs": ["age_risk_score", "condition_risk_score", "lifestyle_risk_score"],
  "outputs": ["total_risk_score"]
}

Rule 4: Risk Category

{
  "rule_type": "CLASSIFICATION",
  "expression": {
    "if": [
      {">=": [{"var": "total_risk_score"}, 75]}, "HIGH",
      {">=": [{"var": "total_risk_score"}, 50]}, "MODERATE",
      {">=": [{"var": "total_risk_score"}, 25]}, "LOW",
      "MINIMAL"
    ]
  },
  "inputs": ["total_risk_score"],
  "outputs": ["risk_category"]
}

Custom Use Cases

Product-FARM’s domain-agnostic design supports any rule-based business logic:

Potential Applications

Industry Use Case Key Features
Real Estate Property valuation Location factors, market trends, comparable analysis
HR Salary calculation Experience, skills, market rates, performance
Manufacturing Quality scoring Defect rates, process metrics, tolerances
Logistics Shipping cost Weight, distance, zones, speed
Gaming Player matchmaking Skill ratings, latency, preferences
Marketing Lead scoring Engagement, demographics, behavior

Building Your Own Use Case

  1. Define Inputs: What data do you have?
  2. Define Outputs: What decisions/calculations do you need?
  3. Map Dependencies: Which outputs depend on which inputs?
  4. Write Rules: Express logic as JSON Logic
  5. Test with Simulation: Verify with sample data
  6. Deploy: Use REST or gRPC API to integrate

JSON Logic Quick Reference

// Arithmetic
{"*": [{"var": "a"}, {"var": "b"}]}           // a × b
{"+": [{"var": "a"}, {"var": "b"}]}           // a + b
{"/": [{"var": "a"}, {"var": "b"}]}           // a ÷ b

// Comparison
{">": [{"var": "a"}, 10]}                     // a > 10
{">=": [{"var": "a"}, {"var": "b"}]}          // a >= b
{"==": [{"var": "a"}, "value"]}               // a == "value"

// Logic
{"and": [{"var": "a"}, {"var": "b"}]}         // a AND b
{"or": [{"var": "a"}, {"var": "b"}]}          // a OR b
{"!": [{"var": "a"}]}                         // NOT a

// Conditional
{"if": [condition, then, else]}               // IF-THEN-ELSE
{"if": [cond1, val1, cond2, val2, default]}   // Multi-branch

// Arrays
{"in": ["value", {"var": "array"}]}           // value in array
{"all": [{"var": "array"}, {">": [{"var": ""}, 0]}]}  // All > 0

Summary

Product-FARM excels at:

  • Complex Calculations: Multi-step formulas with dependencies
  • Decision Trees: Multi-branch conditional logic
  • Risk Assessment: Factor-based scoring systems
  • Dynamic Pricing: Context-aware price calculations
  • Eligibility Checks: Multi-criteria qualification

The visual DAG editor and AI assistant make it easy to create and maintain these rule systems without writing code.