data-analysis

name	data-analysis
description	Analyze insurance data — claims trends, loss ratios, premium comparisons, coverage utilization, and risk metrics
enabled	true

Instructions

When the user asks for claims analysis, loss ratio calculations, premium comparisons, coverage utilization reports, or risk metrics, follow this workflow:

1. Data Ingestion

• If the user has data from the crm skill (customer/policy records), use that as input
• If the user provides CSV/JSON data inline, write it to /tmp first using code_interpreter
• If no data is available, offer to generate realistic sample insurance data for demonstration

2. Insurance-Specific Analyses

Claims Analysis

import pandas as pd

df = pd.DataFrame(claims_data)
print(f"Total Claims: {len(df)}")
print(f"Open Claims: {len(df[df['status'] == 'Open'])}")
print(f"Average Claim Amount: ${df['amount'].mean():,.2f}")
print(f"Total Incurred: ${df['amount'].sum():,.2f}")
print(f"\nClaims by Status:\n{df['status'].value_counts()}")
print(f"\nClaims by Product Line:\n{df.groupby('product_type')['amount'].agg(['count', 'sum', 'mean'])}")

Loss Ratio Calculation

def loss_ratio(incurred_losses, earned_premiums):
    """Loss ratio = Incurred Losses / Earned Premiums"""
    ratio = incurred_losses / earned_premiums * 100
    status = "Profitable" if ratio < 70 else "Borderline" if ratio < 100 else "Unprofitable"
    return round(ratio, 1), status

ratio, status = loss_ratio(incurred_losses=850000, earned_premiums=1200000)
print(f"Loss Ratio: {ratio}% — {status}")

Premium Comparison

# Compare coverage options side by side
options = pd.DataFrame([
    {"Plan": "Basic", "Premium": 120, "Deductible": 2500, "Limit": 100000},
    {"Plan": "Standard", "Premium": 200, "Deductible": 1000, "Limit": 250000},
    {"Plan": "Premium", "Premium": 350, "Deductible": 500, "Limit": 500000},
])
print(options.to_string(index=False))

Coverage Utilization

• Claims frequency by policy type
• Average severity (claim amount) by product line
• Utilization rate (claims filed / policies in force)

3. Visualizations

Generate charts with matplotlib and save to /tmp:

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

# Claims by product line
fig, ax = plt.subplots(figsize=(10, 6))
claims_by_type.plot(kind="bar", ax=ax)
ax.set_title("Claims by Product Line")
ax.set_ylabel("Number of Claims")
plt.tight_layout()
plt.savefig("/tmp/claims_by_product.png", dpi=150)
plt.close()
print("Chart saved: /tmp/claims_by_product.png")

Common insurance charts:

• Bar chart: Claims count/amount by product line or status
• Line chart: Monthly claims trend, loss ratio over time
• Pie chart: Claims distribution by category (≤6 categories)
• Stacked bar: Open vs closed claims by month
• Heatmap: Claims by region and product type

4. Risk Metrics

Provide insights based on the data:

• Loss ratio trend: Is it improving or deteriorating?
• Claims frequency: Number of claims per 1,000 policies
• Average severity: Mean claim amount by product line
• Large loss identification: Flag claims significantly above average
• Concentration risk: Are claims concentrated in specific regions, products, or time periods?

5. Output

• Print key findings as text
• Save charts to /tmp with descriptive names
• Reference file paths for download
• If analysis produces a transformed dataset, save as CSV to /tmp

Chaining

• code_interpreter — runs the Python code
• crm — provides source customer and policy data
• rag-search — provides policy wording context for claims analysis
• file-sharing — delivers charts and exports

Constraints

• Pre-installed libraries: pandas, numpy, matplotlib. Additional libraries can be installed at runtime via pip.
• Files must be written to /tmp
• Max execution time: 30 seconds per code block
• Always include appropriate caveats on actuarial estimates