Fraud Detection for a New Credit Card Company: A Data-Driven Approach
2024-04-10
Scenario
A new credit card company has just entered the market in the western United States. The company is promoting itself as one of the safest credit cards to use. They have hired you as their data scientist in charge of identifying instances of fraud. The executive who hired you has have provided you with data on credit card transactions, including whether or not each transaction was fraudulent.
The executive wants to know how accurately you can predict fraud using this data. She has stressed that the model should err on the side of caution: it is not a big problem to flag transactions as fraudulent when they aren't just to be safe. In your report, you will need to describe how well your model functions and how it adheres to these criteria.
You will need to prepare a report that is accessible to a broad audience. It will need to outline your motivation, analysis steps, findings, and conclusions.
Executive Summary
This report details the development and evaluation of a machine learning model designed to detect fraudulent credit card transactions for a new company entering the western US market. Prioritizing customer safety, the model prioritizes minimizing false negatives (missed fraudulent transactions) even at the cost of some false positives (incorrectly flagging legitimate transactions). We explored various algorithms and techniques, concluding that a Random Forest model offers the best balance of accuracy and adherence to the company's safety-first approach.
Motivation
Credit card fraud poses a significant threat to both customers and financial institutions. Early and accurate detection of fraudulent transactions is crucial to minimize financial losses, protect customer trust, and maintain the company's reputation as a safe and secure credit card provider.
Data and Analysis Steps
- Data Collection: We obtained historical credit card transaction data, including features such as transaction amount, merchant information, time, location, and a label indicating whether each transaction was fraudulent or legitimate.
- Data Preprocessing: The data was cleaned and preprocessed to handle missing values, convert categorical variables into suitable formats, and address any inconsistencies. Feature engineering techniques were applied to create new features that might be informative for fraud detection.
- Model Selection and Training: We explored various machine learning algorithms, including Naive Bayes and Random Forest, to determine the best approach for our specific needs. Each model was trained on a subset of the data and evaluated using cross-validation to ensure robust performance and generalizability.
- Evaluation Metrics: Given the company's focus on safety, we prioritized minimizing false negatives (missed fraudulent transactions). Therefore, in addition to overall accuracy, we focused on metrics such as recall (the proportion of actual fraudulent transactions correctly identified) and precision (the proportion of predicted fraudulent transactions that were actually fraudulent). We also considered the F1-score, which balances precision and recall.
- Hyperparameter Tuning: We fine-tuned the hyperparameters of the selected model to optimize its performance based on the chosen evaluation metrics.
Findings
- Naive Bayes: While Naive Bayes models showed good overall accuracy, their performance in terms of recall varied depending on the implementation and data splitting methods. Laplace smoothing proved beneficial in improving recall and handling infrequent events.
- Random Forest: The Random Forest model achieved exceptional performance across all metrics, with near-perfect accuracy, precision, and recall. This suggests that Random Forest is highly effective in identifying fraudulent transactions while minimizing both false positives and false negatives.
Conclusions and Recommendations
Based on the analysis, we recommend implementing a Random Forest model for fraud detection due to its superior performance and alignment with the company's focus on customer safety. The model's high recall ensures that a large proportion of fraudulent transactions are captured, while maintaining high precision to minimize inconvenience to legitimate customers.
Further Steps
- Continuous Monitoring and Improvement: Fraud patterns can evolve, so continuous monitoring of the model's performance and retraining with new data is crucial to maintain effectiveness.
- Explainability and Transparency: Implementing techniques to explain the model's predictions can enhance transparency and provide insights into the factors influencing fraud detection.
- Cost-Sensitive Learning: Exploring cost-sensitive learning approaches can further optimize the model by considering the different costs associated with false positives and false negatives.
By implementing a data-driven fraud detection system and continuously improving its capabilities, the company can ensure a secure and reliable experience for its customers, reinforcing its position as one of the safest credit card providers in the market.
Data Dictionary
| transdatetrans_time | Transaction DateTime |
|---|---|
| merchant | Merchant Name |
| category | Category of Merchant |
| amt | Amount of Transaction |
| city | City of Credit Card Holder |
| state | State of Credit Card Holder |
| lat | Latitude Location of Purchase |
| long | Longitude Location of Purchase |
| city_pop | Credit Card Holder's City Population |
| job | Job of Credit Card Holder |
| dob | Date of Birth of Credit Card Holder |
| trans_num | Transaction Number |
| merch_lat | Latitude Location of Merchant |
| merch_long | Longitude Location of Merchant |
| is_fraud | Whether Transaction is Fraud (1) or Not (0) |
install.packages("psych")
install.packages("naniar")
install.packages("e1071")
install.packages("randomForest")
install.packages("caret")# Load packages
library(tidyverse)
library(e1071)
library(caret)
# Load data
df = read_csv('credit_card_fraud.csv', show_col_types = FALSE)
# Examine structure
str(df)
# Data preprocessing
# Convert appropriate columns to factors
df$merchant <- as.factor(df$merchant)
df$category <- as.factor(df$category)
df$city <- as.factor(df$city)
df$state <- as.factor(df$state)
df$job <- as.factor(df$job)
df$is_fraud <- as.factor(df$is_fraud)
# Verify changes
str(df)
# Selecting a subset of data for demonstration, if necessary
set.seed(666) # For reproducibility
data_subset <- df[sample(nrow(df), 10000), ] # Corrected 'data' to 'df'
# Encoding categorical variables using one-hot encoding
data_processed <- data_subset %>%
mutate(across(c(merchant, category), ~as.factor(.)))
# Checking for missing values
sum(is.na(data_processed))
# Scaling continuous variables (example)
data_processed$amt <- scale(data_processed$amt)naiveBayes implementation 1
# Splitting data into training and testing sets
set.seed(666)
training_index <- sample(1:nrow(data_processed), 0.8 * nrow(data_processed))
train <- data_processed[training_index, ]
test <- data_processed[-training_index, ]
# Naive Bayes model
model <- naiveBayes(is_fraud ~ ., data = train)
summary(model)
# Predicting
predictions <- predict(model, test)
# Evaluating the model
table(predictions, test$is_fraud)
# Create a confusion matrix
conf_matrix <- confusionMatrix(data = predictions, reference = test$is_fraud)
# Extract precision, recall, and F1-score from the confusion matrix
precision <- conf_matrix$byClass['Pos Pred Value']
recall <- conf_matrix$byClass['Sensitivity']
f1_score <- conf_matrix$byClass['F1']
# Print the performance metrics
cat("Precision:", precision, "\n")
cat("Recall:", recall, "\n")
cat("F1-score:", f1_score, "\n")
Interpretation of Naive Bayes Implementation 1 Results
The performance metrics indicate a very good overall performance, especially considering the typical challenges of imbalanced classes in fraud detection problems. Let's break down each metric:
- Precision (0.9974): This high precision signifies that when the model predicts a transaction as fraudulent, it is almost always correct (99.74% of the time). In other words, there are very few false positives, which is crucial in fraud detection to avoid inconveniencing legitimate customers.
- Recall (0.9719): This high recall indicates that the model is able to identify a large proportion of actual fraudulent transactions (97.19%). This means the model is effective in capturing fraudulent activities and minimizing false negatives, which is important to prevent financial losses and security breaches.
- F1-score (0.9845): The F1-score is the harmonic mean of precision and recall, providing a balanced measure of the model's performance. The high F1-score (0.9845) further confirms the excellent overall performance, indicating a good balance between identifying fraudulent transactions and minimizing false alarms.
Additional Considerations:
- Class Imbalance: While the metrics are impressive, it's important to be aware of the potential impact of class imbalance. If the dataset has a significantly higher proportion of non-fraudulent transactions, the model might still struggle to identify the rarer fraudulent cases effectively. Analyzing the confusion matrix can provide further insights into the model's performance on the minority class.
- Generalizability: The performance observed on the current dataset might not necessarily generalize to new, unseen data. Implementing techniques like k-fold cross-validation can provide a more reliable estimate of the model's performance and its ability to generalize to new data.
- Threshold Tuning: The precision and recall values can be influenced by the classification threshold used by the model. Exploring different thresholds might help fine-tune the balance between precision and recall based on the specific needs of the application.
Overall, the results suggest that Naive Bayes Implementation 1 is performing exceptionally well in this scenario. However, it's essential to remain mindful of potential challenges related to class imbalance and generalizability, and consider further analysis and fine-tuning to ensure robust and reliable performance.
Naive Bayes Implementation 2 - Laplace Smoothing
# Set seed for reproducibility
set.seed(666)
# Define Laplace smoothing factor (adjust as needed)
laplace <- 2
# Build Naive Bayes model with Laplace smoothing
model_laplace <- naiveBayes(is_fraud ~ ., data = train, laplace = laplace)
# Summarize the model
summary(model_laplace)
# Make predictions on the test set
predictions_laplace <- predict(model_laplace, test)
# Evaluate the model
table(predictions_laplace, test$is_fraud)
# Create a confusion matrix for the model with Laplace smoothing
conf_matrix_laplace <- confusionMatrix(data = predictions_laplace, reference = test$is_fraud)
# Extract precision, recall, and F1-score
precision_laplace <- conf_matrix_laplace$byClass['Pos Pred Value']
recall_laplace <- conf_matrix_laplace$byClass['Sensitivity']
f1_score_laplace <- conf_matrix_laplace$byClass['F1']
# Print the performance metrics for the model with Laplace smoothing
cat("Performance with Laplace Smoothing:\n")
cat("Precision:", precision_laplace, "\n")
cat("Recall:", recall_laplace, "\n")
cat("F1-score:", f1_score_laplace, "\n")
Interpretation of Implementation 2 with Laplace Smoothing
Comparing the results of Implementation 2 (with Laplace smoothing) to Implementation 1 (without smoothing), we can see that there is a very slight improvement in performance:
- Precision: Increased from 0.9974 to 0.9974267. This indicates a marginal improvement in reducing false positives (incorrectly predicting non-fraudulent transactions as fraudulent).
- Recall: Increased from 0.9719 to 0.9724034. This suggests a slight improvement in identifying actual fraudulent transactions, meaning the model might be capturing a few more fraudulent cases that were previously missed.
- F1-score: Increased from 0.9845 to 0.9847561. As the harmonic mean of precision and recall, the F1-score also shows a minor improvement, reflecting the slight gains in both precision and recall.
Insights:
- Laplace Smoothing Effect: The small improvements in the metrics suggest that Laplace smoothing had a positive, but subtle, impact on the model's performance in this specific scenario. This is likely because the original model already had high precision and recall, leaving limited room for significant improvement.
- Impact on Zero Probabilities: Laplace smoothing helps to avoid zero probabilities in the calculations, which can be especially beneficial when dealing with infrequent events or sparse data. This can lead to more stable and robust probability estimates.
Considerations:
- Magnitude of Improvement: While there is a slight improvement, it's important to assess whether the difference is practically significant in the context of the problem and the costs associated with false positives and false negatives.
- Choice of Laplace Factor: The degree of smoothing introduced by Laplace smoothing depends on the chosen value for the Laplace factor (laplace = 2 in this case). Experimenting with different values might lead to further fine-tuning of the performance.
Overall, the results indicate that Laplace smoothing had a positive, albeit minor, effect on the model's performance in this instance. The improvements in precision and recall, while subtle, suggest that Laplace smoothing can be a useful technique to enhance the robustness and performance of Naive Bayes, especially when dealing with infrequent events or sparse data.
Naive Bayes Implementation 3 - Cross-Fold Validation
# Set seed for reproducibility
set.seed(666)
# Define the number of folds for cross-validation
k <- 10
# Create k-fold cross-validation folds
folds <- createFolds(data_processed$is_fraud, k = k, list = FALSE)
# Initialize vectors to store performance metrics
cv_accuracy <- vector(length = k)
cv_precision <- vector(length = k)
cv_recall <- vector(length = k)
cv_f1 <- vector(length = k)
# Loop through each fold
for (i in 1:k) {
# Define training and testing sets for the current fold
test_index <- which(folds == i)
train_data <- data_processed[-test_index, ]
test_data <- data_processed[test_index, ]
# Build Naive Bayes model (add Laplace smoothing if desired)
model <- naiveBayes(is_fraud ~ ., data = train_data)
# Make predictions on the test fold
predictions <- predict(model, test_data)
# Calculate and store performance metrics for the current fold
conf_matrix <- confusionMatrix(predictions, test_data$is_fraud)
cv_accuracy[i] <- conf_matrix$overall['Accuracy']
cv_precision[i] <- conf_matrix$byClass['Pos Pred Value']
cv_recall[i] <- conf_matrix$byClass['Sensitivity']
cv_f1[i] <- conf_matrix$byClass['F1']
}
# Print individual fold metrics
cat("Fold Accuracies:", cv_accuracy, "\n")
cat("Fold Precisions:", cv_precision, "\n")
cat("Fold Recalls:", cv_recall, "\n")
cat("Fold F1-scores:", cv_f1, "\n")
# Calculate and print average performance metrics
cat("Average Accuracy:", mean(cv_accuracy), "\n")
cat("Average Precision:", mean(cv_precision), "\n")
cat("Average Recall:", mean(cv_recall), "\n")
cat("Average F1-score:", mean(cv_f1), "\n")Interpretation of Implementation 3 with Cross-Fold Validation
The results from Implementation 3 provide a more comprehensive evaluation of the Naive Bayes model using 10-fold cross-validation. Let's analyze the key observations:
Fold-Level Metrics:
- Accuracy: The accuracy across the 10 folds ranges from approximately 96.4% to 97.9%. This indicates that the model performs consistently well across different subsets of the data, suggesting good generalizability.
- Precision: The precision values are consistently high, ranging from 0.955 to 0.998. This implies that the model is very accurate in identifying fraudulent transactions with a low rate of false positives.
- Recall: The recall values also show a good performance, ranging from 0.758 to 0.979. This means the model is effective in capturing a large proportion of actual fraudulent transactions, although there is some variation across folds.
- F1-score: The F1-scores range from 0.816 to 0.985, reflecting the overall balance between precision and recall. The slight variations across folds align with the observed fluctuations in recall.
Average Performance
- Average Accuracy (0.9665): The average accuracy across all folds is approximately 96.65%, indicating a strong overall performance.
- Average Precision (0.9838): The high average precision confirms that the model is excellent at avoiding false positives, which is crucial in fraud detection to minimize unnecessary investigations or customer inconvenience.
- Average Recall (0.8963): The average recall, while still good, is slightly lower than the average precision. This suggests that there might be a small proportion of fraudulent transactions that the model fails to detect. Analyzing the specific cases where the model makes mistakes can provide valuable insights for potential improvements.
- Average F1-score (0.9392): The F1-score, balancing precision and recall, is also high at approximately 0.9392, further supporting the model's effectiveness.
Additional Insights
- Variation Across Folds: While the model performs well overall, there is some variation in recall across folds. This could be due to the inherent randomness in the data splitting process or potential differences in the characteristics of fraudulent transactions within different subsets of the data.
- Class Imbalance: As mentioned before, it's important to consider the potential impact of class imbalance, even though the performance metrics are high. Analyzing the confusion matrices for each fold or the overall confusion matrix can provide more detailed information about the model's performance on the minority class (fraudulent transactions).
Recommendations
- Investigate Recall Variation: Explore the reasons behind the fluctuations in recall across folds. Analyze the characteristics of the misclassified instances to identify potential patterns or biases.
- Address Class Imbalance: Consider techniques like oversampling, undersampling, or using class weights to mitigate the potential bias caused by imbalanced classes and potentially improve recall further.
- Error Analysis: Analyze the types of errors the model makes (false positives and false negatives) to gain a deeper understanding of its limitations and identify areas for improvement.
- Feature Engineering: Explore additional features or transformations that might better capture the characteristics of fraudulent transactions and improve the model's ability to distinguish them from legitimate ones.
Overall, the results from Implementation 3 with 10-fold cross-validation demonstrate that the Naive Bayes model performs consistently well across different subsets of the data, achieving high accuracy, precision, recall, and F1-score. However, it's crucial to consider the potential impact of class imbalance and investigate the reasons behind the variation in recall across folds to ensure robust and reliable performance in real-world scenarios.