Credit Card Fraud Detection
Uma Srinivas Majji
10/27/2020
Loading Packages
library(caret) # for sampling
library(caTools) # for train/test split
library(dplyr) # for data manipulation
library(stringr) # for data manipulation
library(ggplot2) # for data visualization
library(corrplot) # for correlation
library(Rtsne) # for tsne plotting
library(DMwR) # for smote implementation
library(ROSE) # for ROSE sampling
library(rpart) # for decision tree model
library(Rborist) # for random forest model
library(xgboost) # for xgboost modelData Exploration
# load data
data <- read.csv("./data/creditcard.csv")
## Basic Exploration
dim(data)## [1] 284807 31head(data)## Time V1 V2 V3 V4 V5 V6
## 1 0 -1.3598071 -0.07278117 2.5363467 1.3781552 -0.33832077 0.46238778
## 2 0 1.1918571 0.26615071 0.1664801 0.4481541 0.06001765 -0.08236081
## 3 1 -1.3583541 -1.34016307 1.7732093 0.3797796 -0.50319813 1.80049938
## 4 1 -0.9662717 -0.18522601 1.7929933 -0.8632913 -0.01030888 1.24720317
## 5 2 -1.1582331 0.87773675 1.5487178 0.4030339 -0.40719338 0.09592146
## 6 2 -0.4259659 0.96052304 1.1411093 -0.1682521 0.42098688 -0.02972755
## V7 V8 V9 V10 V11 V12
## 1 0.23959855 0.09869790 0.3637870 0.09079417 -0.5515995 -0.61780086
## 2 -0.07880298 0.08510165 -0.2554251 -0.16697441 1.6127267 1.06523531
## 3 0.79146096 0.24767579 -1.5146543 0.20764287 0.6245015 0.06608369
## 4 0.23760894 0.37743587 -1.3870241 -0.05495192 -0.2264873 0.17822823
## 5 0.59294075 -0.27053268 0.8177393 0.75307443 -0.8228429 0.53819555
## 6 0.47620095 0.26031433 -0.5686714 -0.37140720 1.3412620 0.35989384
## V13 V14 V15 V16 V17 V18
## 1 -0.9913898 -0.3111694 1.4681770 -0.4704005 0.20797124 0.02579058
## 2 0.4890950 -0.1437723 0.6355581 0.4639170 -0.11480466 -0.18336127
## 3 0.7172927 -0.1659459 2.3458649 -2.8900832 1.10996938 -0.12135931
## 4 0.5077569 -0.2879237 -0.6314181 -1.0596472 -0.68409279 1.96577500
## 5 1.3458516 -1.1196698 0.1751211 -0.4514492 -0.23703324 -0.03819479
## 6 -0.3580907 -0.1371337 0.5176168 0.4017259 -0.05813282 0.06865315
## V19 V20 V21 V22 V23 V24
## 1 0.40399296 0.25141210 -0.018306778 0.277837576 -0.11047391 0.06692807
## 2 -0.14578304 -0.06908314 -0.225775248 -0.638671953 0.10128802 -0.33984648
## 3 -2.26185710 0.52497973 0.247998153 0.771679402 0.90941226 -0.68928096
## 4 -1.23262197 -0.20803778 -0.108300452 0.005273597 -0.19032052 -1.17557533
## 5 0.80348692 0.40854236 -0.009430697 0.798278495 -0.13745808 0.14126698
## 6 -0.03319379 0.08496767 -0.208253515 -0.559824796 -0.02639767 -0.37142658
## V25 V26 V27 V28 Amount Class
## 1 0.1285394 -0.1891148 0.133558377 -0.02105305 149.62 0
## 2 0.1671704 0.1258945 -0.008983099 0.01472417 2.69 0
## 3 -0.3276418 -0.1390966 -0.055352794 -0.05975184 378.66 0
## 4 0.6473760 -0.2219288 0.062722849 0.06145763 123.50 0
## 5 -0.2060096 0.5022922 0.219422230 0.21515315 69.99 0
## 6 -0.2327938 0.1059148 0.253844225 0.08108026 3.67 0str(data)## 'data.frame': 284807 obs. of 31 variables:
## $ Time : num 0 0 1 1 2 2 4 7 7 9 ...
## $ V1 : num -1.36 1.192 -1.358 -0.966 -1.158 ...
## $ V2 : num -0.0728 0.2662 -1.3402 -0.1852 0.8777 ...
## $ V3 : num 2.536 0.166 1.773 1.793 1.549 ...
## $ V4 : num 1.378 0.448 0.38 -0.863 0.403 ...
## $ V5 : num -0.3383 0.06 -0.5032 -0.0103 -0.4072 ...
## $ V6 : num 0.4624 -0.0824 1.8005 1.2472 0.0959 ...
## $ V7 : num 0.2396 -0.0788 0.7915 0.2376 0.5929 ...
## $ V8 : num 0.0987 0.0851 0.2477 0.3774 -0.2705 ...
## $ V9 : num 0.364 -0.255 -1.515 -1.387 0.818 ...
## $ V10 : num 0.0908 -0.167 0.2076 -0.055 0.7531 ...
## $ V11 : num -0.552 1.613 0.625 -0.226 -0.823 ...
## $ V12 : num -0.6178 1.0652 0.0661 0.1782 0.5382 ...
## $ V13 : num -0.991 0.489 0.717 0.508 1.346 ...
## $ V14 : num -0.311 -0.144 -0.166 -0.288 -1.12 ...
## $ V15 : num 1.468 0.636 2.346 -0.631 0.175 ...
## $ V16 : num -0.47 0.464 -2.89 -1.06 -0.451 ...
## $ V17 : num 0.208 -0.115 1.11 -0.684 -0.237 ...
## $ V18 : num 0.0258 -0.1834 -0.1214 1.9658 -0.0382 ...
## $ V19 : num 0.404 -0.146 -2.262 -1.233 0.803 ...
## $ V20 : num 0.2514 -0.0691 0.525 -0.208 0.4085 ...
## $ V21 : num -0.01831 -0.22578 0.248 -0.1083 -0.00943 ...
## $ V22 : num 0.27784 -0.63867 0.77168 0.00527 0.79828 ...
## $ V23 : num -0.11 0.101 0.909 -0.19 -0.137 ...
## $ V24 : num 0.0669 -0.3398 -0.6893 -1.1756 0.1413 ...
## $ V25 : num 0.129 0.167 -0.328 0.647 -0.206 ...
## $ V26 : num -0.189 0.126 -0.139 -0.222 0.502 ...
## $ V27 : num 0.13356 -0.00898 -0.05535 0.06272 0.21942 ...
## $ V28 : num -0.0211 0.0147 -0.0598 0.0615 0.2152 ...
## $ Amount: num 149.62 2.69 378.66 123.5 69.99 ...
## $ Class : int 0 0 0 0 0 0 0 0 0 0 ...summary(data)## Time V1 V2 V3
## Min. : 0 Min. :-56.40751 Min. :-72.71573 Min. :-48.3256
## 1st Qu.: 54202 1st Qu.: -0.92037 1st Qu.: -0.59855 1st Qu.: -0.8904
## Median : 84692 Median : 0.01811 Median : 0.06549 Median : 0.1799
## Mean : 94814 Mean : 0.00000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.:139321 3rd Qu.: 1.31564 3rd Qu.: 0.80372 3rd Qu.: 1.0272
## Max. :172792 Max. : 2.45493 Max. : 22.05773 Max. : 9.3826
## V4 V5 V6 V7
## Min. :-5.68317 Min. :-113.74331 Min. :-26.1605 Min. :-43.5572
## 1st Qu.:-0.84864 1st Qu.: -0.69160 1st Qu.: -0.7683 1st Qu.: -0.5541
## Median :-0.01985 Median : -0.05434 Median : -0.2742 Median : 0.0401
## Mean : 0.00000 Mean : 0.00000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.74334 3rd Qu.: 0.61193 3rd Qu.: 0.3986 3rd Qu.: 0.5704
## Max. :16.87534 Max. : 34.80167 Max. : 73.3016 Max. :120.5895
## V8 V9 V10 V11
## Min. :-73.21672 Min. :-13.43407 Min. :-24.58826 Min. :-4.79747
## 1st Qu.: -0.20863 1st Qu.: -0.64310 1st Qu.: -0.53543 1st Qu.:-0.76249
## Median : 0.02236 Median : -0.05143 Median : -0.09292 Median :-0.03276
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.32735 3rd Qu.: 0.59714 3rd Qu.: 0.45392 3rd Qu.: 0.73959
## Max. : 20.00721 Max. : 15.59500 Max. : 23.74514 Max. :12.01891
## V12 V13 V14 V15
## Min. :-18.6837 Min. :-5.79188 Min. :-19.2143 Min. :-4.49894
## 1st Qu.: -0.4056 1st Qu.:-0.64854 1st Qu.: -0.4256 1st Qu.:-0.58288
## Median : 0.1400 Median :-0.01357 Median : 0.0506 Median : 0.04807
## Mean : 0.0000 Mean : 0.00000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.6182 3rd Qu.: 0.66251 3rd Qu.: 0.4931 3rd Qu.: 0.64882
## Max. : 7.8484 Max. : 7.12688 Max. : 10.5268 Max. : 8.87774
## V16 V17 V18
## Min. :-14.12985 Min. :-25.16280 Min. :-9.498746
## 1st Qu.: -0.46804 1st Qu.: -0.48375 1st Qu.:-0.498850
## Median : 0.06641 Median : -0.06568 Median :-0.003636
## Mean : 0.00000 Mean : 0.00000 Mean : 0.000000
## 3rd Qu.: 0.52330 3rd Qu.: 0.39968 3rd Qu.: 0.500807
## Max. : 17.31511 Max. : 9.25353 Max. : 5.041069
## V19 V20 V21
## Min. :-7.213527 Min. :-54.49772 Min. :-34.83038
## 1st Qu.:-0.456299 1st Qu.: -0.21172 1st Qu.: -0.22839
## Median : 0.003735 Median : -0.06248 Median : -0.02945
## Mean : 0.000000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.458949 3rd Qu.: 0.13304 3rd Qu.: 0.18638
## Max. : 5.591971 Max. : 39.42090 Max. : 27.20284
## V22 V23 V24
## Min. :-10.933144 Min. :-44.80774 Min. :-2.83663
## 1st Qu.: -0.542350 1st Qu.: -0.16185 1st Qu.:-0.35459
## Median : 0.006782 Median : -0.01119 Median : 0.04098
## Mean : 0.000000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.528554 3rd Qu.: 0.14764 3rd Qu.: 0.43953
## Max. : 10.503090 Max. : 22.52841 Max. : 4.58455
## V25 V26 V27
## Min. :-10.29540 Min. :-2.60455 Min. :-22.565679
## 1st Qu.: -0.31715 1st Qu.:-0.32698 1st Qu.: -0.070840
## Median : 0.01659 Median :-0.05214 Median : 0.001342
## Mean : 0.00000 Mean : 0.00000 Mean : 0.000000
## 3rd Qu.: 0.35072 3rd Qu.: 0.24095 3rd Qu.: 0.091045
## Max. : 7.51959 Max. : 3.51735 Max. : 31.612198
## V28 Amount Class
## Min. :-15.43008 Min. : 0.00 Min. :0.000000
## 1st Qu.: -0.05296 1st Qu.: 5.60 1st Qu.:0.000000
## Median : 0.01124 Median : 22.00 Median :0.000000
## Mean : 0.00000 Mean : 88.35 Mean :0.001728
## 3rd Qu.: 0.07828 3rd Qu.: 77.17 3rd Qu.:0.000000
## Max. : 33.84781 Max. :25691.16 Max. :1.000000# check for missing values
colSums(is.na(data))## Time V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
## 0 0 0 0 0 0 0 0 0 0 0
## V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21
## 0 0 0 0 0 0 0 0 0 0 0
## V22 V23 V24 V25 V26 V27 V28 Amount Class
## 0 0 0 0 0 0 0 0 0# check class
table(data$Class)##
## 0 1
## 284315 492prop.table(table(data$Class))##
## 0 1
## 0.998272514 0.001727486common_theme <- theme(plot.title = element_text(hjust = 0.5, face = "bold"))
# distribution of class labels
ggplot(data = data, aes(x = factor(Class),
y = prop.table(stat(count)),
fill = factor(Class),
label = scales::percent(prop.table(stat(count)), accuracy = 0.1))) +
geom_bar(position = "dodge") +
geom_text(stat = 'count',
position = position_dodge(0.90),
vjust = -0.5,
size = 3) +
scale_x_discrete(labels = c("no fraud", "fraud")) +
scale_y_continuous(labels = scales::percent) +
labs(x = 'Class', y = 'Percentage') +
ggtitle("Distribution of class labels") +
common_theme
Data Visualization
Distribution of time of transaction by class
ggplot(data = data, aes(x = Time, fill = factor(Class))) +
geom_histogram(bins = 100) +
facet_grid(Class~., scales = "free_y") +
labs(x = "Time in seconds since first transaction", y = "No. of transactions") +
ggtitle("Distribution of time of transaction by class") +
common_theme
Distribution of variable ‘Amount’ by Class
ggplot(data = data, aes(x = factor(Class), y = Amount)) +
geom_boxplot() +
labs(x = "Class", y = "Amount") +
ggtitle("Distribution of transaction amount by class") +
common_theme
Correlation
correlations <- cor(data[,-1], method = "pearson")
corrplot(correlations, number.cex = 0.9, method = "circle",
type = "full", tl.cex = 0.8, tl.col = "black")
Visualization of transactions using t-SNE
# Use 10% data to compute t-SNE
tsne_subset <- 1:as.integer(0.1*nrow(data))
tsne <- Rtsne(data[tsne_subset,-c(1,31)], perplexity = 20, theta = 0.5,
pca = FALSE, max_iter = 500, verbose = FALSE, check_duplicates = FALSE)
classes <- as.factor(data$Class[tsne_subset])
tsne_mat <- as.data.frame(tsne$Y)
ggplot(tsne_mat, aes(x = V1, y = V2)) +
geom_point(aes(color = classes)) +
theme_minimal() +
ggtitle("t-SNE visualization of transactions") +
scale_color_manual(values = c("#E69F00", "#56B4E9")) +
common_theme
Modeling Approach
SMOTE - synthetic minority oversampling technique ROSE - random over-sampling examples
Data preparation
# remove 'Time' variable
data <- data[,-1]
data$Class <- as.factor(data$Class)
levels(data$Class) <- c("Not Fraud", "Fraud")
# scale numeric variables
data[,-30] <- scale(data[,-30])
head(data)## V1 V2 V3 V4 V5 V6
## 1 -0.6942411 -0.04407485 1.6727706 0.9733638 -0.245116153 0.34706734
## 2 0.6084953 0.16117564 0.1097969 0.3165224 0.043483276 -0.06181986
## 3 -0.6934992 -0.81157640 1.1694664 0.2682308 -0.364571146 1.35145121
## 4 -0.4933240 -0.11216923 1.1825144 -0.6097256 -0.007468867 0.93614819
## 5 -0.5913287 0.53154012 1.0214099 0.2846549 -0.295014918 0.07199846
## 6 -0.2174742 0.58167387 0.7525841 -0.1188331 0.305008424 -0.02231344
## V7 V8 V9 V10 V11 V12
## 1 0.1936786 0.08263713 0.3311272 0.08338540 -0.5404061 -0.6182946
## 2 -0.0637001 0.07125336 -0.2324938 -0.15334936 1.5800001 1.0660867
## 3 0.6397745 0.20737237 -1.3786729 0.19069928 0.6118286 0.0661365
## 4 0.1920703 0.31601704 -1.2625010 -0.05046786 -0.2218912 0.1783707
## 5 0.4793014 -0.22650983 0.7443250 0.69162382 -0.8061452 0.5386257
## 6 0.3849353 0.21795429 -0.5176177 -0.34110050 1.3140441 0.3601815
## V13 V14 V15 V16 V17 V18
## 1 -0.9960972 -0.3246096 1.6040110 -0.5368319 0.24486302 0.03076988
## 2 0.4914173 -0.1499822 0.6943592 0.5294328 -0.13516973 -0.21876220
## 3 0.7206986 -0.1731136 2.5629017 -3.2982296 1.30686559 -0.14478974
## 4 0.5101678 -0.3003600 -0.6898362 -1.2092939 -0.80544323 2.34530040
## 5 1.3522420 -1.1680315 0.1913231 -0.5152042 -0.27908030 -0.04556892
## 6 -0.3597909 -0.1430569 0.5655061 0.4584589 -0.06844494 0.08190778
## V19 V20 V21 V22 V23 V24
## 1 0.49628116 0.32611744 -0.02492332 0.382853766 -0.17691102 0.1105067
## 2 -0.17908573 -0.08961071 -0.30737626 -0.880075209 0.16220090 -0.5611296
## 3 -2.77855597 0.68097378 0.33763110 1.063356404 1.45631719 -1.1380901
## 4 -1.51420227 -0.26985475 -0.14744304 0.007266895 -0.30477601 -1.9410237
## 5 0.98703556 0.52993786 -0.01283920 1.100009340 -0.22012301 0.2332497
## 6 -0.04077658 0.11021522 -0.28352172 -0.771425648 -0.04227277 -0.6132723
## V25 V26 V27 V28 Amount Class
## 1 0.2465850 -0.3921697 0.33089104 -0.06378104 0.24496383 Not Fraud
## 2 0.3206933 0.2610690 -0.02225564 0.04460744 -0.34247394 Not Fraud
## 3 -0.6285356 -0.2884462 -0.13713661 -0.18102051 1.16068389 Not Fraud
## 4 1.2419015 -0.4602165 0.15539593 0.18618826 0.14053401 Not Fraud
## 5 -0.3952009 1.0416095 0.54361884 0.65181477 -0.07340321 Not Fraud
## 6 -0.4465828 0.2196368 0.62889938 0.24563577 -0.33855582 Not FraudSplit data into train and test sets
set.seed(123)
split <- sample.split(data$Class, SplitRatio = 0.7)
train <- subset(data, split == TRUE)
test <- subset(data, split == FALSE)Let us create different versions of the training set as per sampling technique
table(train$Class)##
## Not Fraud Fraud
## 199020 344# under sampling
set.seed(1951)
down_train <- downSample(x = train[,-ncol(train)], y = train$Class)
table(down_train$Class)##
## Not Fraud Fraud
## 344 344# over sampling
set.seed(1951)
up_train <- upSample(x = train[,-ncol(train)], y = train$Class)
table(up_train$Class)##
## Not Fraud Fraud
## 199020 199020# Synthetic data generation
# SMOTE
set.seed(1951)
smote_train <- SMOTE(Class~., train)
table(smote_train$Class)##
## Not Fraud Fraud
## 1376 1032# ROSE
set.seed(1951)
rose_train <- ROSE(Class~., train)$data
table(rose_train$Class)##
## Not Fraud Fraud
## 99451 99913# CART model performance on imbalanced data
set.seed(1591)
orig_fit <- rpart(Class~., data = train)
# evaluate model performance on test set
pred_orig <- predict(orig_fit, newdata = test, method = "class")
roc.curve(test$Class, pred_orig[,2], plotit = TRUE) # AUC : 0.912
## Area under the curve (AUC): 0.912# down sampled model
set.seed(1591)
down_fit <- rpart(Class~., data = down_train)
pred_down <- predict(down_fit, newdata = test)
roc.curve(test$Class, pred_down[,2], plotit = FALSE) # AUC : 0.942## Area under the curve (AUC): 0.949# up sampled model
set.seed(1591)
up_fit <- rpart(Class~., data = up_train)
pred_up <- predict(up_fit, newdata = test)
roc.curve(test$Class, pred_up[,2], plotit = FALSE) # AUC : 0.943## Area under the curve (AUC): 0.937# SMOTE
set.seed(1591)
smote_fit <- rpart(Class~., data = smote_train)
pred_smote <- predict(smote_fit, newdata = test)
roc.curve(test$Class, pred_smote[,2], plotit = FALSE) # AUC : 0.934## Area under the curve (AUC): 0.943# ROSE
set.seed(1591)
rose_fit <- rpart(Class~., data = rose_train)
pred_rose <- predict(rose_fit, newdata = test)
roc.curve(test$Class, pred_rose[,2], plotit = FALSE) # AUC : 0.942## Area under the curve (AUC): 0.942Logistic Regression (GLM) Fit
glm_fit <- glm(Class~., data = up_train, family = "binomial")
pred_glm <- predict(glm_fit, newdata = test, type = "response")
roc.curve(test$Class, pred_glm, plotit = TRUE) # AUC : 0.971
## Area under the curve (AUC): 0.971Random Forest Fit
x <- up_train[,-30]
y <- up_train[,30]
rf_fit <- Rborist(x, y, nTree = 1000, minNode = 20, maxLeaf = 13)
pred_rf <- predict(rf_fit, newdata = test[,-30], ctgCensus = "prob")
roc.curve(test$Class, pred_rf$prob[,2], plotit = TRUE) # AUC : 0.973
## Area under the curve (AUC): 0.973XGB fit
# convert class labels from factor to numeric
labels <- up_train$Class
y <- recode(labels, "Not Fraud" = 0, "Fraud" = 1)
xgb_fit <- xgboost(data = data.matrix(up_train[,-30]),
label = y,
eta = 0.1,
gamma = 0.1,
max_depth = 10,
nrounds = 300,
objective = "binary:logistic",
colsample_bytree = 0.6,
verbose = 0,
nthread = 7,
seed = 42)
pred_xgb <- predict(xgb_fit, data.matrix(test[,-30]))
roc.curve(test$Class, pred_xgb, plotit = TRUE) # AUC : 0.974
## Area under the curve (AUC): 0.974Important features
names <- dimnames(data.matrix(up_train[,-30]))[[2]]
# Compute feature importance matrix
importance_matrix <- xgb.importance(names, model = xgb_fit)
# graph
xgb.plot.importance(importance_matrix[1:10,])