Programming lesson
Mastering Credit Risk Prediction with R: A Statistical Learning Guide for the MATH 60603A Assignment
Learn how to approach the MATH 60603A Statistical Learning credit risk assignment using R. This tutorial covers data exploration, feature engineering, model building, and profit optimization strategies inspired by real-world lending scenarios.
Introduction to Credit Risk Prediction in Statistical Learning
Credit risk assessment is a cornerstone of modern banking, and the MATH 60603A assignment puts you in the role of a data scientist tasked with maximizing loan profits. Using R, you'll analyze borrower data to decide which loan applications to approve. This tutorial walks you through a systematic approach, from data cleaning to profit optimization, without giving away the exact solution. Whether you're a student or a professional, understanding these techniques is essential for careers in finance, data science, and AI-driven lending.
Understanding the Business Problem
Your goal is to maximize profit, not just minimize defaults. The dataset includes features like income, employment status, delinquency history, and loan details. The bank provides historical data (CreditGame_TRAIN.csv) and current applications (CreditGame_Applications.csv). You must submit a CSV with accepted IDs. The baseline profit is when all applications are approved; you need to beat that. Think of this like a fantasy sports league where you draft borrowers based on their stats to maximize your team's returns.
Key Variables to Focus On
- DEFAULT: Whether the borrower defaulted (1) or not (0).
- PROFIT_LOSS: Actual profit or loss from the loan.
- NB_DEL_30, NB_DEL_60, NB_DEL_90: Delinquency counts – strong predictors of default.
- R_ATD: Debt-to-income ratio – higher values indicate risk.
- MNT_DEMANDE: Loan amount – larger loans can yield higher profit but also higher risk.
Step 1: Data Exploration and Cleaning
Load the data into R using read.csv(). Check for missing values, outliers, and distributions. Use summary() and str() to understand the data. For example, NB_ER_6MS and NB_ER_12MS might be highly correlated – consider combining or dropping one. Visualize default rates by TYP_FIN (Car, Mortgage, Credit) using ggplot2. Mortgage loans might have lower default rates but longer durations. This step is like scouting players in esports – you need to know their strengths and weaknesses.
train <- read.csv('CreditGame_TRAIN.csv')
summary(train)
# Check for missing values
colSums(is.na(train))Step 2: Feature Engineering
Create new features that capture risk better. For instance, compute a delinquency score by summing weighted delinquency counts: score <- NB_DEL_30 + 2*NB_DEL_60 + 3*NB_DEL_90. Also, calculate the utilization rate of revolving credit: MNT_UTIL_REN / MNT_AUT_REN. High utilization often indicates financial stress. Another idea: create a binary flag for self-employed borrowers (ST_EMPL == 'T'), as they may have variable income. Think of this as building a player rating system in sports analytics – combining multiple stats into one metric.
train$del_score <- train$NB_DEL_30 + 2*train$NB_DEL_60 + 3*train$NB_DEL_90
train$util_rate <- train$MNT_UTIL_REN / train$MNT_AUT_REN
train$self_emp <- ifelse(train$ST_EMPL == 'T', 1, 0)Step 3: Model Building
You can use logistic regression, decision trees, random forests, or gradient boosting. Since the goal is profit, consider a cost-sensitive model that weights false negatives (approving a defaulter) higher than false positives (rejecting a good borrower). Use the PROFIT_LOSS variable to derive a custom loss function. Alternatively, build a model to predict DEFAULT and then use the predicted probability to rank applicants. Approve only those with probability below a threshold that maximizes profit on the training set. This is similar to algorithmic trading where you optimize a reward function.
library(randomForest)
model <- randomForest(as.factor(DEFAULT) ~ ., data = train, ntree = 100)
pred_prob <- predict(model, newdata = train, type = 'prob')[,2]Step 4: Profit Optimization
Use the training data to simulate profit for different cutoff thresholds. For each threshold, approve applicants with predicted default probability below that threshold. Calculate total profit using the actual PROFIT_LOSS. Choose the threshold that maximizes profit. This is your decision rule. Apply it to the test data to generate your submission. Remember, you can upload multiple times and see interim leaderboard results – treat this like A/B testing in marketing campaigns.
thresholds <- seq(0.1, 0.9, by = 0.05)
profits <- sapply(thresholds, function(t) {
approved <- pred_prob < t
sum(train$PROFIT_LOSS[approved])
})
best_t <- thresholds[which.max(profits)]Step 5: Submission and Iteration
Prepare your CSV with accepted IDs from CreditGame_Applications.csv. Upload to the game platform. Monitor your rank and adjust your model. Try different algorithms, feature sets, or even ensemble methods. For instance, combine logistic regression and random forest predictions. This iterative process mirrors real-world machine learning where you continuously improve based on feedback. Don't forget to submit your final R code on ZoneCours.
Tips for Top Performance
- Feature selection: Use correlation analysis and variable importance from random forest to drop irrelevant features.
- Cross-validation: Use k-fold CV to avoid overfitting when choosing thresholds.
- Handle class imbalance: If defaults are rare, use SMOTE or oversampling.
- Profit-based metric: Instead of accuracy, optimize directly for profit.
Conclusion
This assignment teaches you the end-to-end process of statistical learning for business decisions. By focusing on profit, you go beyond simple classification. The skills you gain – data wrangling, feature engineering, model evaluation, and iterative optimization – are highly sought after in fields like fintech, AI, and data science. Good luck, and may your portfolio be profitable!