Build This Project · Portfolio Guide

Insurance Claims Analytics — Complete Project Guide

SECTION 01Project overview

Here's what you'll build in this project:

• Business problem: An insurance company wants to analyze claims data to detect fraud, optimize processing, and reduce costs.
• Your solution: A claims analytics dashboard that tracks key metrics (claims volume, settlement time, fraud flags) and identifies patterns.
• Tools: Python (pandas, scikit-learn), Tableau/Power BI, or R.
• Outcome: A portfolio-ready project that demonstrates insurance analytics and fraud detection skills.

SECTION 02Data source

Here are the best data sources for this project:

SECTION 03Key metrics and KPIs

Here are the key metrics your claims dashboard should track:

SECTION 04Fraud detection

Here's how to build fraud detection into your project:

• Anomaly detection: Identify claims that deviate significantly from normal patterns.
• ML models: Use classification models (Random Forest, XGBoost) to predict fraud risk.
• Feature engineering: Create features like claim amount vs average, claim frequency, and policyholder history.
• Model evaluation: Use precision and recall — fraud detection is about catching as many fraud cases as possible.

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# Load claims data
df = pd.read_csv('insurance_claims.csv')

# Features for fraud detection
features = ['claim_amount', 'claim_type', 'policy_tenure', 'num_claims']
X = df[features]
y = df['fraud_flag']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

# Feature importance
importance = pd.DataFrame({
    'feature': features,
    'importance': model.feature_importances_
}).sort_values('importance', ascending=False)
print(importance)

# Evaluate fraud detection model
from sklearn.metrics import precision_score, recall_score, f1_score

precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

print(f"Precision: {precision:.2f}")
print(f"Recall: {recall:.2f}")
print(f"F1 Score: {f1:.2f}")

# For fraud detection, recall is often more important
# You want to catch as many fraud cases as possible

SECTION 05Dashboard design

Here's how to design your claims analytics dashboard:

• Top section: KPI cards — total claims, avg settlement time, fraud rate, total payouts.
• Middle section: Trends over time — claims volume, settlement time, fraud rate trends.
• Bottom section: Breakdowns by claim type, region, and fraud status.
• Filters: Date range, claim type, region, fraud status.

SECTION 06Portfolio presentation

Here's how to present this project to employers:

• GitHub: Upload your code, data preparation scripts, and dashboard file.
• README: Write a clear README with project overview, fraud detection methodology, and key insights.
• Executive summary: Include a 1-page summary for business stakeholders.
• Screenshots: Add screenshots of your dashboard and fraud detection results.
• LinkedIn post: Share your project with a brief explanation of the business problem you solved.

SECTION 07Interview Q&A — insurance claims analytics

SECTION 08Test yourself — insurance claims quiz

SECTION 09Frequently asked questions

SECTION 10Related reads