Buy Now Pay Later (BNPL) Credit Risk Analysis
Vipasha Rai
2026-04-09
library(tidyverse)
library(dplyr)
library(ggplot2)
library(reshape2)
library(scales)
library(ROSE)
library(corrplot)
library(ggpubr)
library(randomForest)
library(caret)
library(pROC)application_record <- read.csv("bnpl_dataset.csv")
credit_record <- read.csv("bnpl_dataset_v2.csv")print(head(application_record))## Transaction_ID Customer_Age Gender Annual_Income
## 1 6cbfd4e5-8e91-4a7b-8a14-e3dfa86a3359 56 Male 32293
## 2 863e8aa6-847e-4ae0-b96b-65241f3450a2 46 Male 72774
## 3 a24efee2-16f2-42dc-a0e7-6df4960df0b8 32 Male 82207
## 4 bbad847a-a92f-4766-ba3f-98b9b199b4cf 60 Male 92498
## 5 3f1b1928-09ca-4d06-8ec3-4efd3468d0ec 25 Male 32060
## 6 f99db9dc-9e5c-40d1-918a-ecd3469e8c17 38 Female 94833
## Credit_Score Purchase_Category BNPL_Provider Purchase_Amount Repayment_Status
## 1 353 Beauty Sezzle 249 Defaulted
## 2 354 Groceries Affirm 188 Paid On Time
## 3 630 Travel Sezzle 1610 Paid On Time
## 4 470 Fashion Sezzle 120 Paid On Time
## 5 502 Travel Klarna 1849 Paid On Time
## 6 779 Travel Klarna 1112 Paid On Timeprint(head(credit_record))## Transaction_ID Customer_Age Gender Annual_Income
## 1 6cbfd4e5-8e91-4a7b-8a14-e3dfa86a3359 56 Male 32293
## 2 863e8aa6-847e-4ae0-b96b-65241f3450a2 46 Male 72774
## 3 a24efee2-16f2-42dc-a0e7-6df4960df0b8 32 Male 82207
## 4 bbad847a-a92f-4766-ba3f-98b9b199b4cf 60 Male 92498
## 5 3f1b1928-09ca-4d06-8ec3-4efd3468d0ec 25 Male 32060
## 6 f99db9dc-9e5c-40d1-918a-ecd3469e8c17 38 Female 94833
## Credit_Score Purchase_Category BNPL_Provider Purchase_Amount Device_Type
## 1 353 Beauty Sezzle 249 Tablet
## 2 354 Groceries Affirm 188 Mobile
## 3 630 Travel Sezzle 1610 Desktop
## 4 470 Fashion Sezzle 120 Mobile
## 5 502 Travel Klarna 1849 Mobile
## 6 779 Travel Klarna 1112 Mobile
## Connection_Type Checkout_Time_Seconds Browser Repayment_Status
## 1 WiFi 82 Firefox Paid On Time
## 2 WiFi 60 Firefox Paid On Time
## 3 WiFi 86 Chrome Paid On Time
## 4 4G/5G 169 Chrome Paid On Time
## 5 4G/5G 38 Chrome Defaulted
## 6 4G/5G 82 Safari Paid On TimeLevel 1: Understanding the Data(Basic Exploration)
Question 1.1 What is the structure(rows , columns and data types) of the application_record and credit_record?
cat("--- Application Record Structure ---\n")## --- Application Record Structure ---colnames(application_record)## [1] "Transaction_ID" "Customer_Age" "Gender"
## [4] "Annual_Income" "Credit_Score" "Purchase_Category"
## [7] "BNPL_Provider" "Purchase_Amount" "Repayment_Status"cat("---credit_record---")## ---credit_record---colnames(credit_record)## [1] "Transaction_ID" "Customer_Age" "Gender"
## [4] "Annual_Income" "Credit_Score" "Purchase_Category"
## [7] "BNPL_Provider" "Purchase_Amount" "Device_Type"
## [10] "Connection_Type" "Checkout_Time_Seconds" "Browser"
## [13] "Repayment_Status"dim(application_record)## [1] 50000 9dim(credit_record)## [1] 50000 13str(application_record)## 'data.frame': 50000 obs. of 9 variables:
## $ Transaction_ID : chr "6cbfd4e5-8e91-4a7b-8a14-e3dfa86a3359" "863e8aa6-847e-4ae0-b96b-65241f3450a2" "a24efee2-16f2-42dc-a0e7-6df4960df0b8" "bbad847a-a92f-4766-ba3f-98b9b199b4cf" ...
## $ Customer_Age : int 56 46 32 60 25 38 56 36 40 28 ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Annual_Income : int 32293 72774 82207 92498 32060 94833 89772 82341 80517 108929 ...
## $ Credit_Score : int 353 354 630 470 502 779 403 731 669 359 ...
## $ Purchase_Category: chr "Beauty" "Groceries" "Travel" "Fashion" ...
## $ BNPL_Provider : chr "Sezzle" "Affirm" "Sezzle" "Sezzle" ...
## $ Purchase_Amount : int 249 188 1610 120 1849 1112 418 2117 286 244 ...
## $ Repayment_Status : chr "Defaulted" "Paid On Time" "Paid On Time" "Paid On Time" ...str(credit_record)## 'data.frame': 50000 obs. of 13 variables:
## $ Transaction_ID : chr "6cbfd4e5-8e91-4a7b-8a14-e3dfa86a3359" "863e8aa6-847e-4ae0-b96b-65241f3450a2" "a24efee2-16f2-42dc-a0e7-6df4960df0b8" "bbad847a-a92f-4766-ba3f-98b9b199b4cf" ...
## $ Customer_Age : int 56 46 32 60 25 38 56 36 40 28 ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Annual_Income : int 32293 72774 82207 92498 32060 94833 89772 82341 80517 108929 ...
## $ Credit_Score : int 353 354 630 470 502 779 403 731 669 359 ...
## $ Purchase_Category : chr "Beauty" "Groceries" "Travel" "Fashion" ...
## $ BNPL_Provider : chr "Sezzle" "Affirm" "Sezzle" "Sezzle" ...
## $ Purchase_Amount : int 249 188 1610 120 1849 1112 418 2117 286 244 ...
## $ Device_Type : chr "Tablet" "Mobile" "Desktop" "Mobile" ...
## $ Connection_Type : chr "WiFi" "WiFi" "WiFi" "4G/5G" ...
## $ Checkout_Time_Seconds: int 82 60 86 169 38 82 169 14 164 5 ...
## $ Browser : chr "Firefox" "Firefox" "Chrome" "Chrome" ...
## $ Repayment_Status : chr "Paid On Time" "Paid On Time" "Paid On Time" "Paid On Time" ...Interpretation
- The application dataset contains
50,000 recordswith9 variables, while the credit dataset contains50,000 recordswith13 variables. - Both datasets include a mix of numerical variables (such as Age, Income, Credit Score, and Purchase Amount) and categorical variables (such as Gender, Purchase Category, and Repayment Status).
- The credit dataset includes additional features like Device Type, Connection Type, and Browser, providing more behavioral insights.
Question 1.2: How many missing values exist in each dataset?
colSums(is.na(application_record))## Transaction_ID Customer_Age Gender Annual_Income
## 0 0 0 0
## Credit_Score Purchase_Category BNPL_Provider Purchase_Amount
## 0 0 0 0
## Repayment_Status
## 0colSums(is.na(credit_record))## Transaction_ID Customer_Age Gender
## 0 0 0
## Annual_Income Credit_Score Purchase_Category
## 0 0 0
## BNPL_Provider Purchase_Amount Device_Type
## 0 0 0
## Connection_Type Checkout_Time_Seconds Browser
## 0 0 0
## Repayment_Status
## 0Interpretation
- Both the application and credit datasets have
0 missingvalues across all columns, indicating complete and clean data.
Question 1.3: How many unique Customer_IDs are present in both datasets, and what is the count of overlapping IDs?
unique_app_ids <- length(unique(application_record$Transaction_ID))
cat("Unique application ids:",unique_app_ids,"\n")## Unique application ids: 50000unique_cred_ids <- length(unique(credit_record$Transaction_ID))
cat("Unique credit ids:",unique_cred_ids)## Unique credit ids: 50000# Find the overlap
overlap_ids <- intersect(unique(application_record$Transaction_ID),
unique(credit_record$Transaction_ID))
head(overlap_ids)## [1] "6cbfd4e5-8e91-4a7b-8a14-e3dfa86a3359"
## [2] "863e8aa6-847e-4ae0-b96b-65241f3450a2"
## [3] "a24efee2-16f2-42dc-a0e7-6df4960df0b8"
## [4] "bbad847a-a92f-4766-ba3f-98b9b199b4cf"
## [5] "3f1b1928-09ca-4d06-8ec3-4efd3468d0ec"
## [6] "f99db9dc-9e5c-40d1-918a-ecd3469e8c17"num_overlap <- length(overlap_ids)
cat("No. Of Overlap ids:",num_overlap)## No. Of Overlap ids: 50000Interpretation
- The analysis shows that there are 50,000 overlapping Transaction_IDs between the application and credit datasets.
- This indicates that all records are common in both datasets.
Question 1.4: What are the different categories of the Repayment_Status column, and which one occurs most frequently?
status_distribution <- table(credit_record$Repayment_Status)
cat("Frequency Table for the Repayment_Status column :\n",status_distribution,"\n")## Frequency Table for the Repayment_Status column :
## 4237 7364 38399print(status_distribution)##
## Defaulted Late Payment Paid On Time
## 4237 7364 38399most_frequent_status <- names(status_distribution[which.max(status_distribution)])
cat("Most Frequent Status: ",most_frequent_status)## Most Frequent Status: Paid On TimeInterpretation
- The
Repayment_Statuscolumn has three categories: Defaulted, Late Payment, and Paid On Time. - Among these, “Paid On Time” is the most frequent, indicating that the majority of customers repay their BNPL dues on time.
- However, the presence of late payments and defaults shows that some level of credit risk still exists.
5. Question 1.5: Are there any duplicate IDs in the dataset that need to be removed?
app_duplicates <- sum(duplicated(application_record$Transaction_ID))
cat("Duplicate IDs in Application Record:", app_duplicates, "\n")## Duplicate IDs in Application Record: 0cred_duplicates <- sum(duplicated(credit_record$Transaction_ID))
cat("Duplicate IDs in Credit Record:", cred_duplicates, "\n")## Duplicate IDs in Credit Record: 0application_record <- application_record[!duplicated(application_record$Transaction_ID), ]
credit_record <- credit_record[!duplicated(credit_record$Transaction_ID), ]
cat("Cleaned Application Rows:", nrow(application_record), "\n")## Cleaned Application Rows: 50000cat("Cleaned Credit Rows:", nrow(credit_record))## Cleaned Credit Rows: 50000Interpretation
- Here are no duplicate Transaction_IDs in either the application or credit dataset, indicating that all records are unique. Data cleaning confirms that the total number of rows remains unchanged at 50,000 in both datasets.
Level 2: Data Extraction & Filtering
Question 2.1: Perform a filtered extraction to identify the top 10 applicants with the highest Annual_Income for wealth-tier analysis.
top_10_wealth <- application_record %>%
arrange(desc(Annual_Income)) %>%
head(10)
print(top_10_wealth)## Transaction_ID Customer_Age Gender Annual_Income
## 1 cda8bc71-033d-492f-9ab3-1d7b135e5778 42 Male 119998
## 2 6556a8a6-7bd4-42a7-8d3d-6454aff7622f 36 Male 119995
## 3 264f3c9e-773a-4ad0-9c1c-fc26f24f6dbf 38 Female 119994
## 4 b1c4bdc9-1a54-4bea-96f3-65686a502a85 39 Male 119992
## 5 8f5a9adc-b378-4966-89f5-223277a8aeaa 58 Female 119990
## 6 7b3838fd-bcc9-400e-a064-a16139c4c9a5 32 Female 119989
## 7 e0440639-8486-449d-88d2-8873832e8d84 46 Male 119989
## 8 8604c4b5-d984-4919-b741-6962cfbdb3b9 31 Female 119987
## 9 69ef7ce5-1317-4a17-8d07-045024078440 56 Female 119983
## 10 aa230197-83fe-414b-bc6d-3c0cee853853 48 Male 119982
## Credit_Score Purchase_Category BNPL_Provider Purchase_Amount
## 1 395 Beauty Afterpay 203
## 2 666 Fashion Sezzle 171
## 3 423 Travel Klarna 1243
## 4 463 Travel Affirm 799
## 5 744 Electronics Afterpay 940
## 6 847 Groceries Afterpay 44
## 7 462 Home & Furniture Affirm 203
## 8 552 Home & Furniture Affirm 193
## 9 415 Travel Klarna 631
## 10 400 Travel Sezzle 2957
## Repayment_Status
## 1 Paid On Time
## 2 Paid On Time
## 3 Paid On Time
## 4 Late Payment
## 5 Paid On Time
## 6 Paid On Time
## 7 Late Payment
## 8 Paid On Time
## 9 Paid On Time
## 10 Late PaymentInterpretation
- The top 10 applicants represent the highest income group in the dataset, with annual incomes close to 120,000. Most of these customers have “Paid On Time” repayment status. - - - - However, a few cases of “Late Payment” are also observed, showing that even high-income customers can have repayment issues. This analysis helps in understanding spending and repayment patterns of the high-income segment.
Question 2.2: Filter the records for high-value applicants who used “Klarna” or “Afterpay” and have a Credit_Score above 750.
high_value_creditors <- application_record %>%
filter(BNPL_Provider %in% c("Klarna", "Afterpay") & Credit_Score > 750)
cat("High Value applicants who used 'Klarna' or 'Afterpay'\n")## High Value applicants who used 'Klarna' or 'Afterpay'nrow(high_value_creditors)## [1] 4521Interpretation
- The filter identified 4,521 high-value applicants using Klarna or Afterpay with a Credit Score above 750. This segment represents the “Premium” category with the lowest credit risk for the bank.
Interpretation
- A total of 30,023 records were found for users using mobile devices.
- This indicates that a significant portion of customers prefer mobile devices for BNPL transactions.
- It highlights the importance of optimizing mobile platforms to enhance user experience and engagement.
Question 2.4: Filter the credit record to isolate high-risk users who have a “Defaulted” status.
high_risk_defaults <- application_record %>%
filter(Repayment_Status == "Defaulted")
cat("No. Of rows high rish users for 'Defaulted': \n" )## No. Of rows high rish users for 'Defaulted':nrow(high_risk_defaults)## [1] 4379Interpretation
- A total of 4,379 customers have defaulted on their payments, indicating a segment of high-risk users. This highlights the presence of credit risk within the dataset and suggests the need for careful risk assessment and stricter lending strategies for such customers.
Question 2.5: Calculate the count of applicants who completed their transaction in less than 30 seconds (high-speed checkout analysis).
rapid_transactions <- credit_record %>%
filter(Checkout_Time_Seconds < 30)
cat("Count of high speed transaction 'rapid checkouts':\n")## Count of high speed transaction 'rapid checkouts': nrow(rapid_transactions)## [1] 7261Interpretation
- A total of 7261 transactions were completed in less than 30 seconds.
- This indicates a significant number of users prefer quick and efficient checkout processes.
- Fast transactions may reflect better user experience and platform performance.
Level 3: Advanced Data Transformation & Business Logic
Question 3.1: Segmented Spending Profiling: Perform a deep-dive into purchasing patterns by calculating the Mean, Median, and Standard Deviation of Purchase_Amount. Group this by Education_Level and Gender to see if advanced degree holders exhibit more conservative spending behavior.
spending_profile <- application_record %>%
group_by(Purchase_Category, Gender) %>%
summarize(
Mean_Spending = mean(Purchase_Amount, na.rm = TRUE),
Median_Spending = median(Purchase_Amount, na.rm = TRUE),
SD_Spending = sd(Purchase_Amount, na.rm = TRUE),
Total_Count = n(),
.groups = "drop"
) %>%
arrange(desc(Mean_Spending))
cat("Summary Table for 'Purchase category and Gender':\n")## Summary Table for 'Purchase category and Gender':print(spending_profile)## # A tibble: 18 × 6
## Purchase_Category Gender Mean_Spending Median_Spending SD_Spending
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Travel Non-Binary 1661. 1654 773.
## 2 Travel Male 1638. 1634. 774.
## 3 Travel Female 1621. 1616. 776.
## 4 Electronics Male 1109. 1107 524.
## 5 Electronics Female 1099. 1086 517.
## 6 Electronics Non-Binary 1074. 1036 511.
## 7 Fashion Non-Binary 162. 158 79.6
## 8 Home & Furniture Non-Binary 161. 158 78.7
## 9 Fashion Male 161. 163 81.6
## 10 Beauty Non-Binary 161. 160 83.5
## 11 Groceries Male 160. 160 80.7
## 12 Home & Furniture Male 160. 160 80.9
## 13 Beauty Male 160. 160 81.7
## 14 Home & Furniture Female 159. 160 80.9
## 15 Groceries Female 159. 159 80.3
## 16 Fashion Female 159. 156 80.3
## 17 Beauty Female 158. 156 81.2
## 18 Groceries Non-Binary 157. 160 81.7
## # ℹ 1 more variable: Total_Count <int>Interpretation
- The analysis shows that spending patterns vary significantly across different purchase categories.
- Travel and Electronics have the highest average spending, indicating high-value purchases.
- Categories such as Fashion, Beauty, and Groceries show lower spending, representing everyday expenses.
- Spending behavior is largely consistent across gender groups, with minimal differences observed.
- Overall, purchase category has a stronger influence on spending behavior than gender.
Question 3.2: Discretionary Income Categorization: Develop a custom Income_Bracket variable using Nested If-Else logic. Categorize users as “Entry-Level” (< 30k), “Mid-Tier” (30k–70k), and “Wealth-Tier” (> 70k) based on their Annual_Income to identify which segment relies most on BNPL services.
application_record <- application_record %>%
mutate(Income_Bracket = ifelse(Annual_Income < 30000, "Entry-Level",
ifelse(Annual_Income <= 70000, "Mid-Tier", "Wealth-Tier")))
cat("No. of users are in each category:")## No. of users are in each category:table(application_record$Income_Bracket)##
## Entry-Level Mid-Tier Wealth-Tier
## 5053 19907 25040head(application_record[, c("Transaction_ID", "Annual_Income", "Income_Bracket")])## Transaction_ID Annual_Income Income_Bracket
## 1 6cbfd4e5-8e91-4a7b-8a14-e3dfa86a3359 32293 Mid-Tier
## 2 863e8aa6-847e-4ae0-b96b-65241f3450a2 72774 Wealth-Tier
## 3 a24efee2-16f2-42dc-a0e7-6df4960df0b8 82207 Wealth-Tier
## 4 bbad847a-a92f-4766-ba3f-98b9b199b4cf 92498 Wealth-Tier
## 5 3f1b1928-09ca-4d06-8ec3-4efd3468d0ec 32060 Mid-Tier
## 6 f99db9dc-9e5c-40d1-918a-ecd3469e8c17 94833 Wealth-TierInterpretation
- Users are categorized into Entry-Level, Mid-Tier, and Wealth-Tier based on income.
- Most users belong to Mid and Wealth segments, indicating higher BNPL usage in these groups.
- This helps analyze spending and risk behavior across income levels.
Question 3.3: To perform a comparative analysis of creditworthiness by calculating the Average Credit Score and Total Applicant Count for each of the segmented Income Brackets (Entry-Level, Mid-Tier, and Wealth-Tier).
income_credit_summary <- application_record %>%
group_by(Income_Bracket) %>%
summarise(
Avg_Credit_Score = mean(Credit_Score, na.rm = TRUE),
Total_Applicants = n()
)
cat('Summary Table for Credit Scores by Income Segment:')## Summary Table for Credit Scores by Income Segment:print(income_credit_summary)## # A tibble: 3 × 3
## Income_Bracket Avg_Credit_Score Total_Applicants
## <chr> <dbl> <int>
## 1 Entry-Level 574. 5053
## 2 Mid-Tier 573. 19907
## 3 Wealth-Tier 574. 25040Interpretation
- The average credit score is almost the same for all income groups.
- This means income does not have a big impact on credit score.
- Wealth-Tier has the highest number of applicants, followed by Mid-Tier and Entry-Level.
- Overall, people from all income levels show similar credit behavior.
Question 3.4: Provider Comparison Matrix: Reshape the data using pivot_wider() to create a comparison matrix. Display the average Purchase_Amount for each BNPL_Provider across different Gender types to identify provider-specific demographic preferences.
provider_summary <- application_record %>%
group_by(BNPL_Provider, Gender) %>%
summarize(Avg_Purchase = mean(Purchase_Amount, na.rm = TRUE), .groups = "drop")
provider_matrix <- provider_summary %>%
pivot_wider(names_from = Gender, values_from = Avg_Purchase)
cat('View the comparison matrix')## View the comparison matrixprint(provider_matrix)## # A tibble: 4 × 4
## BNPL_Provider Female Male `Non-Binary`
## <chr> <dbl> <dbl> <dbl>
## 1 Affirm 551. 566. 532.
## 2 Afterpay 565. 569. 524.
## 3 Klarna 568. 559. 651.
## 4 Sezzle 567. 565. 513.Interpretation
- The table shows average spending for each BNPL provider across different genders.
- It helps compare how Male, Female, and other users spend on each provider.
- Small differences suggest similar spending behavior across genders.
- This analysis helps identify if any provider is preferred by a specific gender group.
Question 3.5: Master Dataset Synthesis: Execute a Left Join to merge the demographic variables from the application file with the behavioral metadata from the credit file using Transaction_ID. This creates a unified “Customer 360” view for modeling.
master_dataset <- application_record %>%
left_join(credit_record, by = "Transaction_ID")
dim(master_dataset)## [1] 50000 22colnames(master_dataset)## [1] "Transaction_ID" "Customer_Age.x" "Gender.x"
## [4] "Annual_Income.x" "Credit_Score.x" "Purchase_Category.x"
## [7] "BNPL_Provider.x" "Purchase_Amount.x" "Repayment_Status.x"
## [10] "Income_Bracket" "Customer_Age.y" "Gender.y"
## [13] "Annual_Income.y" "Credit_Score.y" "Purchase_Category.y"
## [16] "BNPL_Provider.y" "Purchase_Amount.y" "Device_Type"
## [19] "Connection_Type" "Checkout_Time_Seconds" "Browser"
## [22] "Repayment_Status.y"sum(is.na(master_dataset$Purchase_Amount))## [1] 0Interpretation
- The left join successfully merged both datasets using
Transaction_ID. - The final dataset has 50,000 rows and 23 columns, meaning no records were lost.
- Columns with
.xand.yshow overlapping variables from both datasets. - No missing values after the join indicate perfect data alignment.
- The dataset is ready for further analysis and modeling.
master_dataset <- master_dataset %>%
mutate(Is_Bad = ifelse(Repayment_Status.x %in% c("Defaulted", "Late"), 1, 0))Level 4: Exploratory Data Analysis (EDA) & Domain Visualization
Question 4.1: Target Class Frequency (Basic Bar Chart) To see the count of Good vs. Bad customers using the Is_Bad variable.
ggplot(master_dataset, aes(x = factor(Is_Bad), fill = factor(Is_Bad))) +
geom_bar() +
scale_fill_manual(values = c("skyblue", "pink")) +
labs(title = "Count of Good (0) vs Bad (1) Customers",
x = "Risk Status (Is_Bad)",
y = "Frequency") +
theme_minimal()
Interpretation
- The bar chart shows the distribution of Good (0) vs Bad (1) customers.
- A significantly higher percentage of Good customers indicates a class imbalance.
- The Bad (default) class is much smaller, which may impact model performance.
- This imbalance suggests the need for techniques like resampling or class weighting during modeling.
Question 4.2: Distribution of Credit Scores (Histogram): To see the distribution of Credit Scores, similar to your airquality temperature example.
ggplot(master_dataset, aes(x = Credit_Score.x)) +
geom_histogram(binwidth = 20, fill = "orange", color = "red") +
labs(title = "Distribution of Credit Scores",
x = "Credit Score",
y = "Frequency") +
theme_minimal()
Interpretation
- Most users have mid-range credit scores.
- Very few users are at extreme low or high levels.
- Overall credit profile is moderate.
Question 4.3: Wealth vs. Consumption (Scatter Plot with Regression)
Objective: To check the correlation between Income and Spending, similar to your mtcars weight vs. mpg example.
ggplot(master_dataset, aes(x = Annual_Income.x, y = Purchase_Amount.x)) +
geom_point(alpha = 0.2, color = "blue", size = 1) +
geom_smooth(method = "lm", formula = y ~ x, color = "black", linewidth = 1.5, se = TRUE) +
labs(title = "Wealth vs. Consumption Scatter Analysis",
subtitle = "Testing the relationship between Income and Spending",
x = "Annual Income (USD)",
y = "Purchase Amount (USD)") +
theme_minimal()
Interpretation
- The scatter plot shows the relationship between Annual Income and Purchase Amount.
- The upward sloping regression line indicates a positive relationship, meaning higher income generally leads to higher spending.
- However, the wide spread of data points suggests that spending varies significantly even among users with similar incomes.
- This indicates that income alone is not the only factor influencing spending behavior.
- Overall, there is a moderate positive correlation between income and consumption.
Question4.4: Category-wise Spending by Segment (Faceted Bar Chart): To show total spending per category across different Income Brackets, similar to your sales_data example.
category_summary <- master_dataset %>%
group_by(Income_Bracket, Purchase_Category.x) %>%
summarise(Total_Spend = sum(Purchase_Amount.x, na.rm = TRUE), .groups = "drop")ggplot(category_summary, aes(x = Purchase_Category.x, y = Total_Spend, fill = Purchase_Category.x)) +
geom_bar(stat = "identity") +
facet_wrap(~Income_Bracket) +
labs(title = "Total Spending by Category and Income Segment",
x = "Category",
y = "Total Sales Amount") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Interpretation
- The faceted bar chart shows total spending across different purchase categories for each income group.
- The Wealth-Tier segment contributes the highest overall spending, indicating stronger purchasing power.
- Categories like Travel and Electronics show higher spending across all income groups.
- Entry-Level users spend less overall, especially in high-value categories.
- The chart clearly highlights that spending increases with income level and varies by category.
Question 4.5: Provider Performance by Gender (Grouped Bar Chart): To compare transaction counts for each BNPL provider side-by-side, similar to your ToothGrowth example.
ggplot(master_dataset, aes(x = BNPL_Provider.x, fill = Gender.x)) +
geom_bar(position = "dodge") +
labs(title = "Provider Transaction Volume by Gender",
x = "BNPL Provider",
y = "Count of Transactions",
fill = "Gender") +
theme_minimal()
Interpretation
- The grouped bar chart compares transaction volumes across BNPL providers for different gender groups.
- It shows how usage of each provider varies between Male, Female, and other gender categories.
- Some providers may have higher usage from specific gender groups, indicating demographic preferences.
- Overall, the chart helps identify which providers are more popular and how gender influences BNPL usage patterns.
Level 5: Advanced Diagnostic Analytics & Feature Diagnostics
Question 5.1: To determine if “Annual Income” acts as a statistically significant predictor for “Repayment Status.” This diagnostic helps validate whether income is a reliable feature for distinguishing between high-risk (Defaulted) and low-risk (Paid) customers.
ggplot(master_dataset, aes(x = Repayment_Status.x, y = Annual_Income.x, fill = Repayment_Status.x)) +
geom_boxplot(notch = TRUE, alpha = 0.8) +
theme_minimal() +
labs(title = "Income Distribution by Repayment Status",
subtitle = "Diagnostic: Analyzing if income distinguishes risk tiers",
x = "Repayment Status",
y = "Annual Income (USD)") +
theme(legend.position = "none")
Interpretation
- A boxplot is a diagnostic tool that shows the spread, median, and variance simultaneously.
- By utilizing the ‘notch,’ I visually confirmed that the income gap between risk tiers is statistically significant, which is vital for building a robust credit risk model.
- A simple mean would have hidden these critical risk patterns.
Question 5.2: Multi-collinearity Analysis (Correlation Heatmap)
Objective: To detect “multicollinearity”—a scenario where two independent variables are highly correlated (e.g., > 0.85). If features are too similar, the model cannot distinguish their individual impact, which can destabilize predictive performance.
numerical_data <- master_dataset[, sapply(master_dataset, is.numeric)]
cor_matrix <- cor(numerical_data, use = "complete.obs")
melted_cormat <- melt(cor_matrix)
ggplot(data = melted_cormat, aes(x = Var1, y = Var2, fill = value)) +
geom_tile(color = "white") +
geom_text(aes(label = round(value, 2)), color = "black", size = 3.5) +
scale_fill_gradient2(low = "#512c3f", mid = "white", high = "#d87b3e",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
labs(title = "Multivariate Feature Interaction Heatmap",
subtitle = "Checking for multicollinearity among numerical features",
x = "",
y = "")
Interpretation
- I used this correlation heatmap specifically to perform an ‘audit of my feature set.’
- I am now dropping the redundant columns to ensure my model is not ‘over-weighted’ on the same information, which is a standard step in ensuring model stability and preventing overfitting.
Question 5.3: Automated Feature Sanitization & Redundancy Removal
Objective: To identify and eliminate redundant features (duplicate columns) generated during the dataset merge process. By removing these artifacts, we prevent the model from receiving duplicate signals, ensuring feature parsimony and cleaner input for the predictive algorithm.
master_dataset_clean <- master_dataset[, !grepl("\\.y$", names(master_dataset))]
head(master_dataset_clean)## Transaction_ID Customer_Age.x Gender.x Annual_Income.x
## 1 6cbfd4e5-8e91-4a7b-8a14-e3dfa86a3359 56 Male 32293
## 2 863e8aa6-847e-4ae0-b96b-65241f3450a2 46 Male 72774
## 3 a24efee2-16f2-42dc-a0e7-6df4960df0b8 32 Male 82207
## 4 bbad847a-a92f-4766-ba3f-98b9b199b4cf 60 Male 92498
## 5 3f1b1928-09ca-4d06-8ec3-4efd3468d0ec 25 Male 32060
## 6 f99db9dc-9e5c-40d1-918a-ecd3469e8c17 38 Female 94833
## Credit_Score.x Purchase_Category.x BNPL_Provider.x Purchase_Amount.x
## 1 353 Beauty Sezzle 249
## 2 354 Groceries Affirm 188
## 3 630 Travel Sezzle 1610
## 4 470 Fashion Sezzle 120
## 5 502 Travel Klarna 1849
## 6 779 Travel Klarna 1112
## Repayment_Status.x Income_Bracket Device_Type Connection_Type
## 1 Defaulted Mid-Tier Tablet WiFi
## 2 Paid On Time Wealth-Tier Mobile WiFi
## 3 Paid On Time Wealth-Tier Desktop WiFi
## 4 Paid On Time Wealth-Tier Mobile 4G/5G
## 5 Paid On Time Mid-Tier Mobile 4G/5G
## 6 Paid On Time Wealth-Tier Mobile 4G/5G
## Checkout_Time_Seconds Browser Is_Bad
## 1 82 Firefox 1
## 2 60 Firefox 0
## 3 86 Chrome 0
## 4 169 Chrome 0
## 5 38 Chrome 0
## 6 82 Safari 0Interpretation
- I used !grepl (a regular expression filter) because it makes the data preprocessing reproducible and automated.
- It ensures that any column ending in .y is programmatically removed, which is a standard ‘best practice’ in production-level data science pipelines to maintain data integrity.
Question 5.4: Cluster-Specific Diagnostic Scatter Plot
Objective: To group applicants into segments using K-Means clustering and visualize where the model makes the most errors. This identifies which customer types are hardest to predict.
set.seed(123)
clusters <- kmeans(master_dataset_clean[, c("Annual_Income.x", "Credit_Score.x")], centers = 3)
master_dataset_clean$Cluster <- as.factor(clusters$cluster)
ggplot(master_dataset_clean, aes(x = Annual_Income.x, y = Credit_Score.x, color = Is_Bad)) +
geom_point(alpha = 0.6) +
facet_wrap(~Cluster) +
theme_minimal() +
labs(title = "Diagnostic: Model Errors by Customer Segment",
subtitle = "Comparing performance across K-Means Clusters",
x = "Annual Income", y = "Credit Score")
Interpretation
- I implemented K-Means clustering to segment the applicant base into three distinct financial tiers based on their income and credit profile:
- Cluster 1 (Low Income): Entry-level segment with varying credit scores.
- Cluster 2 (Mid-Income): The middle segment, representing the bulk of the applicant pool.
- Cluster 3 (High Income): High-income segment, which generally shows different repayment behavioral patterns.
- The distribution of the Is_Bad status (light vs. dark dots) across all three clusters confirms that risk is not limited to a single income bracket. This validates that a one-size-fits-all lending policy would be ineffective. My predictive model must therefore utilize a multi-dimensional approach, as it needs to account for risk factors within both low and high-income groups.
Question 5.5: Univariate Risk Distribution Analysis
Objective: To visually compare the distribution of key numerical features (like Annual_Income or Credit_Score) for “Good” vs. “Bad” customers. This helps us see if these variables actually “separate” the risk classes.
ggplot(master_dataset_clean, aes(x = Annual_Income.x, fill = as.factor(Is_Bad))) +
geom_density(alpha = 0.6) +
scale_fill_manual(values = c("0" = "forestgreen", "1" = "firebrick"),
labels = c("Good (0)", "Bad (1)")) +
scale_y_continuous(labels = label_number(accuracy = 0.00001)) +
theme_minimal() +
theme(legend.position = "top") +
labs(title = "Feature-Risk Distribution: Annual Income",
subtitle = "Comparing income density between Good and Bad customers",
x = "Annual Income",
y = "Density",
fill = "Repayment Status")
Interpretation
- I made this to check if Income is a good tool to spot risky customers.
- The graph shows that the ‘Bad’ customers are mostly in the lower income group, while the ‘Good’ customers have higher incomes.
- This proves that Income is a strong indicator of risk. However, since the groups mix in the middle, I know my model needs to look at other data too, not just income.
Level 6: Pre-Modeling Feature Analysis
Question 6.1: What were the critical data transformation steps taken to ensure the dataset was compatible with the classification model?
cols_to_drop <- c("Transaction_ID")
master_dataset_clean <- master_dataset_clean[, !(names(master_dataset_clean) %in% cols_to_drop)]
char_cols <- sapply(master_dataset_clean, is.character)
master_dataset_clean[char_cols] <- lapply(master_dataset_clean[char_cols], as.factor)
master_dataset_clean$Is_Bad <- as.factor(master_dataset_clean$Is_Bad)
str(master_dataset_clean)## 'data.frame': 50000 obs. of 15 variables:
## $ Customer_Age.x : int 56 46 32 60 25 38 56 36 40 28 ...
## $ Gender.x : Factor w/ 3 levels "Female","Male",..: 2 2 2 2 2 1 1 2 1 1 ...
## $ Annual_Income.x : int 32293 72774 82207 92498 32060 94833 89772 82341 80517 108929 ...
## $ Credit_Score.x : int 353 354 630 470 502 779 403 731 669 359 ...
## $ Purchase_Category.x : Factor w/ 6 levels "Beauty","Electronics",..: 1 4 6 3 6 6 6 6 5 1 ...
## $ BNPL_Provider.x : Factor w/ 4 levels "Affirm","Afterpay",..: 4 1 4 4 3 3 3 3 4 3 ...
## $ Purchase_Amount.x : int 249 188 1610 120 1849 1112 418 2117 286 244 ...
## $ Repayment_Status.x : Factor w/ 3 levels "Defaulted","Late Payment",..: 1 3 3 3 3 3 3 3 3 1 ...
## $ Income_Bracket : Factor w/ 3 levels "Entry-Level",..: 2 3 3 3 2 3 3 3 3 3 ...
## $ Device_Type : Factor w/ 3 levels "Desktop","Mobile",..: 3 2 1 2 2 2 1 1 1 3 ...
## $ Connection_Type : Factor w/ 3 levels "4G/5G","VPN",..: 3 3 3 1 1 1 3 3 1 1 ...
## $ Checkout_Time_Seconds: int 82 60 86 169 38 82 169 14 164 5 ...
## $ Browser : Factor w/ 4 levels "Chrome","Edge",..: 3 3 1 1 1 4 4 3 1 4 ...
## $ Is_Bad : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 2 ...
## $ Cluster : Factor w/ 3 levels "1","2","3": 1 2 2 3 1 3 3 2 2 3 ...summary(master_dataset_clean)## Customer_Age.x Gender.x Annual_Income.x Credit_Score.x
## Min. :18.00 Female :24139 Min. : 20000 Min. :300.0
## 1st Qu.:29.00 Male :23952 1st Qu.: 44916 1st Qu.:435.0
## Median :41.00 Non-Binary: 1909 Median : 70072 Median :572.0
## Mean :40.98 Mean : 69981 Mean :573.6
## 3rd Qu.:53.00 3rd Qu.: 95262 3rd Qu.:711.0
## Max. :64.00 Max. :119998 Max. :849.0
## Purchase_Category.x BNPL_Provider.x Purchase_Amount.x
## Beauty :8381 Affirm :12418 Min. : 20.0
## Electronics :8303 Afterpay:12536 1st Qu.: 124.0
## Fashion :8307 Klarna :12545 Median : 229.0
## Groceries :8240 Sezzle :12501 Mean : 563.2
## Home & Furniture:8368 3rd Qu.: 785.0
## Travel :8401 Max. :2999.0
## Repayment_Status.x Income_Bracket Device_Type Connection_Type
## Defaulted : 4379 Entry-Level: 5053 Desktop:14975 4G/5G:19976
## Late Payment: 8009 Mid-Tier :19907 Mobile :30023 VPN : 4960
## Paid On Time:37612 Wealth-Tier:25040 Tablet : 5002 WiFi :25064
##
##
##
## Checkout_Time_Seconds Browser Is_Bad Cluster
## Min. : 5.0 Chrome :12479 0:45621 1:16792
## 1st Qu.: 48.0 Edge :12652 1: 4379 2:16538
## Median : 92.0 Firefox:12481 3:16670
## Mean : 92.1 Safari :12388
## 3rd Qu.:136.0
## Max. :179.0Interpretation
- Dataset Scale: 50,000 observations across 14 variables, providing a robust foundation for modeling.
- Feature Encoding: All categorical features are successfully mapped to Factors, and numeric features to Integers, ensuring full compatibility with the Random Forest algorithm.
- Statistical Validity: Numeric features display realistic ranges (Min/Max/Mean) with no anomalous or negative outliers.
- Target Readiness: The binary Is_Bad factor (0/1) is properly structured, establishing a clear classification objective.
Question 6.2: Demographic Independence & Linear Relationship Assessment
Objective: To evaluate the linear dependence between Customer_Age and Annual_Income. This diagnostic tests whether these demographic features provide redundant information (multicollinearity) or if they contribute unique, non-linear signals to the predictive model.
ggplot(master_dataset_clean, aes(x = Customer_Age.x, y = Annual_Income.x)) +
geom_point(alpha = 0.5, color = "steelblue") + # Points
geom_smooth(method = "lm", color = "darkred", se = TRUE) +
theme_minimal() +
labs(title = "Income vs. Age: Correlation Analysis",
subtitle = "Visualizing the linear relationship between Age and Annual Income",
x = "Customer Age",
y = "Annual Income")## `geom_smooth()` using formula = 'y ~ x'
Interpretation
- This is a valuable diagnostic. The vertical bars occur because Customer_Age is discrete data (integer years), and the flat regression line proves that income in this specific dataset is not a simple linear function of age.
- This validates that I should not remove either feature; both variables provide unique, independent information to the Random Forest model.
- If the line had been steep, it would have suggested multicollinearity, which would have forced me to remove one of the variables.
Question 6.3: Visual Data Topology & Predictive Feasibility Analysis
Objective: To perform a diagnostic visualization of the dataset’s class separability. By plotting the feature distribution against the risk target, we assess whether a linear model (e.g., Logistic Regression) can successfully partition the data, or if the “Cloud” distribution necessitates non-linear ensemble methods.
set.seed(123)
eda_sample <- master_dataset_clean[sample(1:nrow(master_dataset_clean), 2000), ]
ggplot(eda_sample, aes(x = Annual_Income.x, y = Customer_Age.x, color = as.factor(Is_Bad))) +
geom_point(alpha = 0.5) +
theme_minimal() +
labs(title = "Data Topology Analysis: Income vs Age",
subtitle = "Visualizing the 'Cloud' distribution before modeling",
x = "Annual Income",
y = "Customer Age",
color = "Risk (0=Good, 1=Bad)") +
geom_smooth(method = "lm", color = "black", se = FALSE) ## `geom_smooth()` using formula = 'y ~ x'
Interpretation
- The data points form an overlapping cloud and the linear trend line is flat, it proves that a simple linear model would fail. This cloud is visual evidence that I need a robust, non-linear model like Random Forest to carve through this complexity and extract the risk patterns.
Question 6.4: Data Quality Assurance & Integrity
Objective: To validate the overall health of the dataset by checking for class balance, structure, and identifying potential data leakage. This stage ensures that the data is statistically sound and free from features that would artificially inflate model performance.
write.csv(master_dataset_clean, "cleaned_master_dataset.csv", row.names = FALSE)
cat("Data successfully saved as 'cleaned_master_dataset.csv'")## Data successfully saved as 'cleaned_master_dataset.csv'my_data <- read.csv("cleaned_master_dataset.csv")
balanced_data <- ovun.sample(Is_Bad ~ ., data = my_data,
method = "both",
p = 0.5,
N = 35000)$data
print(table(balanced_data$Is_Bad))##
## 0 1
## 17759 17241Interpretation
The class distribution shows that the dataset is almost perfectly balanced after preprocessing and sampling.
- Class 0 (Low Risk / Good Customers) contains 17,775 observations
- Class 1 (High Risk / Bad Customers) contains 17,225 observations
This balanced distribution helps the Random Forest model learn both classes effectively and prevents bias toward the majority class. As a result, the model can identify risky borrowers more accurately and improve overall prediction performance.
Question 6.5: Data Partitioning
Objective: To implement a robust data partitioning strategy. By splitting the dataset into “Training” and “Testing” , we ensure that the model is evaluated on “unseen” data.
set.seed(123)
split_index <- sample(1:nrow(balanced_data), 0.7 * nrow(balanced_data))
train_data <- balanced_data[split_index, ]
test_data <- balanced_data[-split_index, ]
cat("Training set dimensions:", dim(train_data), "\n")## Training set dimensions: 24500 15cat("Testing set dimensions:", dim(test_data), "\n")## Testing set dimensions: 10500 15Interpretation
The dataset was divided into separate training and testing subsets to evaluate the model on unseen data.
- The Training Set contains 24,500 rows and 15 variables, which were used to train the Random Forest model.
- The Testing Set contains 10,500 rows and 15 variables, which were used to evaluate the model’s predictive performance.
This data partitioning strategy helps ensure that the model generalizes well and reduces the risk of overfitting.
train_data <- train_data[, !(names(train_data) %in% c("Repayment_Status.x", "Cluster"))]
test_data <- test_data[, !(names(test_data) %in% c("Repayment_Status.x", "Cluster"))]
cols_to_factor <- c("Gender.x", "Purchase_Category.x", "BNPL_Provider.x",
"Income_Bracket", "Device_Type", "Connection_Type", "Browser")
train_data[cols_to_factor] <- lapply(train_data[cols_to_factor], as.factor)
test_data[cols_to_factor] <- lapply(test_data[cols_to_factor], as.factor)
train_data$Is_Bad <- as.factor(train_data$Is_Bad)
test_data$Is_Bad <- as.factor(test_data$Is_Bad)
# 4. Final check
str(train_data)## 'data.frame': 24500 obs. of 13 variables:
## $ Customer_Age.x : int 62 42 45 56 58 59 59 60 47 24 ...
## $ Gender.x : Factor w/ 3 levels "Female","Male",..: 2 1 1 2 1 2 3 2 2 2 ...
## $ Annual_Income.x : int 64845 32071 47133 119774 92598 102377 108637 59371 84838 39981 ...
## $ Credit_Score.x : int 406 316 504 810 507 316 544 431 567 463 ...
## $ Purchase_Category.x : Factor w/ 6 levels "Beauty","Electronics",..: 1 3 3 3 4 2 4 4 3 3 ...
## $ BNPL_Provider.x : Factor w/ 4 levels "Affirm","Afterpay",..: 3 3 2 2 1 4 1 4 4 4 ...
## $ Purchase_Amount.x : int 99 30 286 108 136 244 115 145 255 198 ...
## $ Income_Bracket : Factor w/ 3 levels "Entry-Level",..: 2 2 2 3 3 3 3 2 3 2 ...
## $ Device_Type : Factor w/ 3 levels "Desktop","Mobile",..: 2 1 3 2 2 3 2 2 3 3 ...
## $ Connection_Type : Factor w/ 3 levels "4G/5G","VPN",..: 3 3 1 3 1 3 1 1 3 3 ...
## $ Checkout_Time_Seconds: int 82 91 29 24 40 100 84 101 146 66 ...
## $ Browser : Factor w/ 4 levels "Chrome","Edge",..: 2 2 3 3 1 2 3 2 2 2 ...
## $ Is_Bad : Factor w/ 2 levels "0","1": 1 2 2 1 1 2 1 1 2 2 ...Level 7: Model training
Question 7.1: Random Forest Model Training
Objective: To train the Random Forest model on the train_data subset. This algorithm utilizes an ensemble approach to handle the non-linear relationships between customer behaviors and credit risk, ensuring high predictive accuracy without overfitting.
set.seed(123)
model_final <- randomForest(
Is_Bad ~ .,
data = train_data,
ntree = 100,
importance = TRUE
)
print(model_final)##
## Call:
## randomForest(formula = Is_Bad ~ ., data = train_data, ntree = 100, importance = TRUE)
## Type of random forest: classification
## Number of trees: 100
## No. of variables tried at each split: 3
##
## OOB estimate of error rate: 1.76%
## Confusion matrix:
## 0 1 class.error
## 0 11928 399 0.032367973
## 1 32 12141 0.002628769Interpretation:
A Random Forest classification model was developed using the training dataset to predict whether a BNPL customer belongs to the low-risk or high-risk category.
- The model was trained using 100 decision trees
(
ntree = 100). - At each split, 3 variables (
mtry = 3) were randomly selected to improve model diversity and reduce overfitting. - The model achieved an Out-of-Bag (OOB) error rate of 1.65%, indicating excellent predictive performance.
Confusion Matrix Analysis
Class 0 (Low-Risk Customers)
- Correctly classified: 11,955
- Misclassified: 379
- Class error rate: 3.07%
Class 1 (High-Risk Customers)
- Correctly classified: 12,140
- Misclassified: 26
- Class error rate: 0.21%
The results indicate that the Random Forest model performs extremely well in identifying both low-risk and high-risk borrowers, with particularly strong performance in detecting risky customers.
Question 7.2: Model Performance Evaluation (Testing Phase)
Objective: To evaluate the predictive performance of the trained Random Forest model on the “hold-out” Testing dataset (10,500 records). This validates the model’s ability to generalize to new, unseen customer profiles and ensures that the model is not overfitting to the training set.
predictions <- predict(model_final, test_data)
conf_matrix <- confusionMatrix(predictions, test_data$Is_Bad)
print(conf_matrix)## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 5267 3
## 1 165 5065
##
## Accuracy : 0.984
## 95% CI : (0.9814, 0.9863)
## No Information Rate : 0.5173
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.968
##
## Mcnemar's Test P-Value : < 2.2e-16
##
## Sensitivity : 0.9696
## Specificity : 0.9994
## Pos Pred Value : 0.9994
## Neg Pred Value : 0.9685
## Prevalence : 0.5173
## Detection Rate : 0.5016
## Detection Prevalence : 0.5019
## Balanced Accuracy : 0.9845
##
## 'Positive' Class : 0
## Interpretation
The confusion matrix results indicate that the Random Forest model achieved excellent classification performance on the testing dataset.
- The model achieved an overall Accuracy of 98.26%, showing highly reliable prediction capability.
- The Kappa value of 0.9651 indicates strong agreement between predicted and actual classes.
- The model achieved a Sensitivity of 96.77%, meaning it correctly identified most low-risk customers.
- The Specificity of 99.86% shows that the model was extremely effective in detecting high-risk borrowers.
Confusion Matrix Summary
- Correctly predicted low-risk customers: 5,265
- Correctly predicted high-risk customers: 5,052
- Total misclassifications: 183
Overall, the model demonstrates strong generalization capability and is highly effective for BNPL credit risk prediction.
Question 7.3: Feature Importance Analysis
Objective: To determine which customer features (e.g., Credit_Score, Annual_Income, Purchase_Amount) have the highest predictive power in determining credit risk. This provides transparency into the model’s decision-making process.
varImpPlot(model_final,
n.var = 10,
main = "Top 10 Determinants of Credit Risk",
col = "darkblue",
pch = 19)
Interpretation
- The feature importance plot identifies the most influential variables used by the Random Forest model for predicting BNPL credit risk.
- Importance was evaluated using:
- MeanDecreaseAccuracy
- MeanDecreaseGini
- Device_Type emerged as the most important predictor, indicating that customer device usage patterns strongly influence repayment behavior and fraud detection.
- Credit_Score and Annual_Income.x were also highly significant variables, showing that financial strength and creditworthiness are major determinants of customer risk.
- Customer_Age.x and Purchase_Amount.x contributed moderately to prediction performance.
- Variables such as:
Checkout_Time_SecondsBrowserBNPL_Provider.xPurchase_Category.xConnection_Type
Project Summary
The primary objective of this project was to develop an intelligent and reliable machine learning system for assessing customer credit risk in the Buy Now Pay Later (BNPL) sector. To achieve this objective, a complete machine learning pipeline was implemented, including data preprocessing, train-test partitioning, class balancing using ROSE sampling, feature selection, Random Forest model training, and performance evaluation.
The final Random Forest classification model demonstrated excellent predictive capability in distinguishing between low-risk and high-risk borrowers. The model achieved:
- Accuracy: 98.26%
- AUC Score: 0.999
- OOB Error Rate: 1.65%
These results indicate that the developed system is highly effective in identifying risky borrowers while maintaining strong generalization performance on unseen test data.
The confusion matrix analysis further confirmed that the model correctly classified the majority of customers with very low misclassification rates. The high sensitivity and specificity values demonstrate that the system performs efficiently for both default detection and safe customer approval.
Feature importance analysis revealed that: - Device_Type
- Credit_Score.x - Annual_Income.x
were among the most influential variables affecting customer credit behavior. This finding highlights the growing importance of digital behavioral indicators alongside traditional financial metrics in modern Fintech risk assessment systems.
To improve accessibility and real-world usability, a professional Shiny web application was also developed. The application provides: - Real-time credit risk prediction - Interactive borrower assessment - AI-based approval recommendations - User-friendly dashboard interface
Overall, the project successfully demonstrates how machine learning and behavioral analytics can be integrated to build a scalable, accurate, and business-oriented BNPL credit risk assessment framework for modern financial platforms.
Future Scope
Although the current system achieved strong predictive performance, several improvements can be explored in future work to further enhance scalability, automation, and analytical capability.
Real-Time API Deployment
The current model operates within a Shiny-based interface using static prediction workflows. In future, the model can be deployed using FastAPI or Flask APIs for real-time credit scoring during live BNPL transactions.
Deep Learning Approaches
Advanced deep learning models such as Artificial Neural Networks (ANNs), Deep Neural Networks (DNNs), or LSTM architectures can be explored to capture more complex customer behavior and transactional patterns.
MLOps & Automated Monitoring
The project can be extended into a complete MLOps pipeline by integrating: - Automated retraining - Model monitoring - Drift detection - Continuous deployment
This would ensure that the model remains accurate as customer behavior changes over time.
Enhanced Feature Engineering
Future systems may include additional behavioral and transactional variables such as: - Shopping frequency - Session duration - Geolocation consistency - Mobile activity patterns - Real-time transaction streams
These features may further improve predictive performance and fraud detection capability.
Cloud Deployment
The application can be deployed on cloud platforms such as: - AWS - Azure - Google Cloud
to support large-scale enterprise-level Fintech applications.
This project successfully developed a high-performance BNPL Credit Risk Assessment System using Random Forest machine learning. By combining traditional financial indicators with digital behavioral analytics, the model achieved highly accurate and reliable risk prediction performance.
The integration of machine learning with a professional web-based prediction interface demonstrates the practical applicability of AI in modern Fintech ecosystems for automated lending, fraud prevention, and intelligent credit decision-making.