Insurance fraud is a significant and growing problem that affects the financial stability of insurance companies and leads to increased premiums for policyholders. Detecting fraudulent claims is a complex task that involves analyzing vast amounts of data and identifying patterns that distinguish legitimate claims from fraudulent ones. With advancements in machine learning and data analytics, it is now possible to develop sophisticated models to detect and prevent fraud effectively. Utilizing Deepnote as the AI-powered data platform is the best approach to solving this issue. Deepnote is a collaborative data science notebook that integrates seamlessly with Python and provides powerful tools for data analysis, visualization, and machine learning.
Setting up your environment
Create a new project in Deepnote: start by creating a new project in Deepnote. This will help you organize your code, data, and outputs.
Install necessary libraries: ensure you have the necessary libraries installed. You can use the following code to install them
!pip install pandas numpy scikit-learn matplotlib seabornImporting libraries
Start by importing the necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrixLoading the data
Load your dataset into a Pandas DataFrame. For this guide, we’ll assume you have a CSV file named insurance_data.csv.
# Load the dataset
data = pd.read_csv('insurance_data.csv')
# Display the first few rows of the dataset
data.head()Data exploration
Explore the dataset to understand its structure and contents:
# Summary statistics
data.describe()
# Check for missing values
data.isnull().sum()
# Distribution of the target variable
sns.countplot(x='fraud', data=data)
plt.title('Fraudulent vs Non-Fraudulent Claims')
plt.show()Data preprocessing
Preprocess the data to prepare it for modeling:
# Drop unnecessary columns
data = data.drop(['column_to_drop1', 'column_to_drop2'], axis=1)
# Handle missing values (if any)
data.fillna(method='ffill', inplace=True)
# Convert categorical variables to dummy variables
data = pd.get_dummies(data, drop_first=True)Splitting the data
Split the data into training and testing sets:
# Define the features (X) and the target variable (y)
X = data.drop('fraud', axis=1)
y = data['fraud']
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)Feature scaling
Scale the features for better performance of the model:
# Initialize the StandardScaler
scaler = StandardScaler()
# Fit and transform the training data
X_train = scaler.fit_transform(X_train)
# Transform the testing data
X_test = scaler.transform(X_test)Model training
Train a machine learning model to detect fraud. Here, we use a Random Forest Classifier:
# Initialize the model
model = RandomForestClassifier(n_estimators=100, random_state=42)
# Train the model
model.fit(X_train, y_train)Model evaluation
Evaluate the model on the test data:
# Make predictions
y_pred = model.predict(X_test)
# Confusion Matrix
conf_matrix = confusion_matrix(y_test, y_pred)
sns.heatmap(conf_matrix, annot=True, fmt='d')
plt.title('Confusion Matrix')
plt.show()
# Classification Report
print(classification_report(y_test, y_pred))Conclusion
- Interpret the results: analyze the confusion matrix and classification report to understand the model's performance.
- Model improvement: consider trying other models, tuning hyperparameters, or further feature engineering to improve performance.
This guide walks you through the basic steps of fraud detection in insurance using Python in Deepnote. You can further enhance the process by exploring advanced techniques, incorporating more data, and fine-tuning your model. Feel free to modify and expand upon this guide based on your specific needs and the characteristics of your dataset.