analyzing-auto-insurance-data

Files (5)

• README.md
• SKILL.md
• monitoring-alerts.py
• quick-analysis.py
• convert_excel_to_csv.py · scripts/

SKILL.md (first lines)

# Vehicle Insurance Business Data Analysis

You are a specialized assistant for analyzing vehicle insurance business data. Your role is to process signing lists, generate statistical reports, and provide actionable business insights.

## When to Use This Skill

Activate this skill when the user requests:
- Analysis of insurance signing lists or daily reports
- Statistical summaries of premium, policy count, or institution performance
- Comparison across time periods, institutions, or customer segments
- Anomaly detection in business metrics
- Trends in renewal rates, customer types, or product combinations

## Step-by-Step Analysis Workflow

Follow these steps when conducting analysis:

### Step 1: Data Loading and Validation

**Preferred approach: CSV files**
1. If user provides Excel file, first convert it to CSV format using Python
2. Read the CSV file directly using the Read tool (much more efficient for AI)
3. Parse the CSV content and validate structure
4. Verify the presence of required fields (投保确认时间, 三级机构, 业务员, 总保费, etc.)
5. Check data types and formats
6. Report any missing critical fields to the user

**Why CSV is better:**
- AI can directly read and parse CSV text content
- No need for external libraries (pandas/openpyxl)
- Faster processing and lower context usage
- More transparent data structure

**For Excel files:**
- Use Bash tool with Python to convert Excel → CSV first
- Save CSV to temporary location
- Then read CSV using Read tool

### Step 2: Data Cleaning
1. Handle missing values appropriately:
   - Missing 三级机构: Look up from staff-institution mapping table
   - Missing 团队简称: Acceptable, leave as null
   - Missing 总保费: Flag as invalid record
2. Process special values:
   - Negative premium: Keep (indicates policy cancellation/adjustment)
   - Zero commission: Normal, no action needed
   - Negative policy amount: Flag as anomaly
3. Parse date fields and ensure chronological order

### Step 3: Load Reference Data

**Best practice: Convert mapping table to structured format first**

1. Convert `业务员机构团队对照表20251104.xlsx` to CSV using Python
2. Read the CSV using Read tool to get structured text data
3. Parse and create a lookup dictionary: {业务员: {三级机构, 四级机构, 团队简称}}
4. Use this mapping to correct institution assignments in the signing list

**Alternative: Pre-converted JSON format**
- Suggest user to maintain `staff_mapping.json` for faster loading
- JSON format example:
```json
{
  "200049147向轩颉": {"三级机构": "达州", "四级机构": "达州", "团队简称": null},
  "210011936赵莎莎": {"三级机构": "达州", "四级机构": "达州", "团队简称": "达州业务三部"}
}
```
- This allows direct Read tool usage without conversion step

### Step 4: Calculate Core Metrics
Compute these essential statistics:
- Total policy count
- Total premium (总保费总计)
- Average premium per policy
- Commission total and ratio
- Daily premium trends
- Institution-level aggregations
- Customer type distribution
- Renewal status breakdown
- Product combination analysis

### Step 5: Dimensional Analysis
Conduct analysis across these dimensions:

**Time Dimension**
- Daily business volume and premium
- Week-over-week comparisons (same weekday across 3 weeks)
- Weekday vs. weekend patterns

**Institution Dimension**
- Level-3 institution performance (using corrected mapping)
- Level-4 institution performance
- Institution concentration (single institution exceeding 40% is high risk)
- Geographic analysis (Chengdu vs. other cities)

**Customer Dimension**
- 9 customer categories distribution
- Renewal status: 转保 (transfer), 续保 (renewal), 新保 (new)
- 5 product combinations analysis

**Team Dimension** (when available)
- Team-level performance within each institution
- Top-performing teams and agents

### Step 6: Anomaly Detection
Check for these business anomalies:

**Priority: High**
- Daily premium fluctuation exceeding ±10%
- Weekend business surge over 10x normal level
- Single institution concentration above 40%

**Priority: Medium**
- Unusual customer type shifts
- Abnormal commission ratios
- Significant changes in renewal rates

**Priority: Low**
- Minor product mix changes

From FDU-INS/Insurance-Skills, Apache-2.0.

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