phân tích bộ dữ liệu phát hiện gian lận thanh toán trực tuyến
Nguyễn Đình Nam,Lê Thanh Nam
28/10/2025
I.GIỚI THIỆU DỮ LIỆU
1.1.Đọc dữ liệu từ file
1.2.Xem cấu trúc của bộ dữ liệu
Hàm str() được dùng để hiển thị một cách cô đọng cấu
trúc bên trong của một đối tượng R. Đối với một bảng dữ liệu (data
frame), nó sẽ cho bạn biết: * Loại đối tượng là gì (ví dụ: data frame).
* Có bao nhiêu quan sát (hàng) và bao nhiêu biến (cột). * Tên của từng
cột. * Kiểu dữ liệu của từng cột (num - số,
chr - chuỗi ký tự, Factor - yếu tố, v.v.). *
Một vài giá trị đầu tiên của mỗi cột để bạn có cái nhìn tổng quan.
## 'data.frame': 583967 obs. of 11 variables:
## $ step : int 1 1 1 1 1 1 1 1 1 1 ...
## $ type : chr "PAYMENT" "PAYMENT" "TRANSFER" "CASH_OUT" ...
## $ amount : chr "9839.64" "1864.28" "181.0" "181.0" ...
## $ nameOrig : chr "C1231006815" "C1666544295" "C1305486145" "C840083671" ...
## $ oldbalanceOrg : chr "170136.0" "21249.0" "181.0" "181.0" ...
## $ newbalanceOrig: chr "160296.36" "19384.72" "0.0" "0.0" ...
## $ nameDest : chr "M1979787155" "M2044282225" "C553264065" "C38997010" ...
## $ oldbalanceDest: chr "0.0" "0.0" "0.0" "21182.0" ...
## $ newbalanceDest: chr "0.0" "0.0" "0.0" "0.0" ...
## $ isFraud : int 0 0 1 1 0 0 0 0 0 0 ...
## $ isFlaggedFraud: int 0 0 0 0 0 0 0 0 0 0 ...Với bộ dữ liệu có 583.967 hàng và 11 biến quan sát Và trong 11 biến có 7 biến có kiểu dữ liệu chuỗi ký tự “chr” - gồm các biến Date, Bran_Name, Ticker, Industry_Tag, Country. Và còn lại là 1 biến có kiểu dữ liệu là số “num”và 3 biến “int” - gồm các biến amount,step,isFraud,isFlaggedFraud.
# 1.3.Xem dữ liệu đầu, cuối
## step type amount nameOrig oldbalanceOrg newbalanceOrig nameDest
## 1 1 PAYMENT 9839.64 C1231006815 170136.0 160296.36 M1979787155
## 2 1 PAYMENT 1864.28 C1666544295 21249.0 19384.72 M2044282225
## 3 1 TRANSFER 181.0 C1305486145 181.0 0.0 C553264065
## oldbalanceDest newbalanceDest isFraud isFlaggedFraud
## 1 0.0 0.0 0 0
## 2 0.0 0.0 0 0
## 3 0.0 0.0 1 0## step type amount nameOrig oldbalanceOrg newbalanceOrig
## 583965 33 CASH_OUT 393521.96 C577340602 0.0 0.0
## 583966 33 CASH_OUT 133553.8 C1079950286 0.0 0.0
## 583967 33 CASH_OUT 271122.05 C1734905315 0.0 0.0
## nameDest oldbalanceDest newbalanceDest isFraud isFlaggedFraud
## 583965 C463403162 611608.87 1005130.83 0 0
## 583966 C415550928 1808951.16 1942504.95 0 0
## 583967 C1539382769 899496.13 1263142.47 0 0- Kiểm tra dữ liệu (Data Sanity Check): Lệnh head() và tail() giúp nhà phân tích xác nhận ngay lập tức rằng bộ dữ liệu (với gần 600,000 dòng) đã được tải vào R một cách chính xác. Họ có thể thấy ngay tên 11 cột (step, type, amount,…), kiểu dữ liệu của chúng (int, chr, dbl), và một vài giá trị mẫu để đảm bảo không có lỗi đọc file cơ bản.
- Hiểu cấu trúc dữ liệu:Hiểu cấu trúc dữ liệu: Nhìn vào 3 dòng đầu và 3 dòng cuối, ta có thể suy luận:
- Bộ dữ liệu được sắp xếp theo thứ tự thời gian tăng dần (ascending chronological order) dựa trên cột step. Dữ liệu sớm nhất (step = 1) nằm ở trên đầu (head), và dữ liệu muộn nhất (step = 33) nằm ở dưới cùng (tail). Đây là một cấu trúc tự nhiên cho dữ liệu chuỗi thời gian, giúp việc phân tích diễn biến hành vi người dùng trở nên tuần tự.
- Dữ liệu là dạng “bảng dài” (long format), trong đó mỗi hàng là một bản ghi chi tiết cho một giao dịch duy nhất.
- Phát hiện vấn đề tiềm ẩn:
- Vấn đề kiểu dữ liệu: Một phát hiện kỹ thuật cực kỳ quan trọng là các
cột số dư (oldbalanceOrg, newbalanceOrig,…) đang có kiểu dữ liệu là ký
tự (
) thay vì số. Điều này cho thấy dữ liệu cần được làm sạch và chuyển đổi kiểu dữ liệu trước khi có thể thực hiện bất kỳ phép tính toán nào. - Phát hiện các mẫu dữ liệu bất thường: So sánh head và tail, ta thấy các loại giao dịch (type) rất đa dạng, từ PAYMENT, TRANSFER ở đầu đến CASH_OUT ở cuối. Điều này gợi ý rằng hành vi giao dịch có thể thay đổi theo thời gian (step). ** Ý nghĩa Kinh tế:kiểm tra được tính toàn vẹn kỹ thuật của dữ liệu mà còn rút ra được những nhận định ban đầu rất quan trọng về mặt kinh tế: phạm vi thời gian, sự đa dạng của danh mục cổ phiếu và đặc điểm sơ bộ của từng mã.
1.3 Giới thiệu các cột(biến)
# 1. Tạo các vector chứa thông tin
bien_goc <- c(
'step',
'type',
'amount',
'nameOrig',
'oldbalanceOrg',
'newbalanceOrig',
'nameDest',
'oldbalanceDest',
'newbalanceDest',
'isFraud',
'isFlaggedFraud'
)
giai_thich_viet <- c(
'Bước (Thời gian)',
'Loại giao dịch',
'Số tiền',
'Tên/Mã người gửi',
'Số dư cũ người gửi',
'Số dư mới người gửi',
'Tên/Mã người nhận',
'Số dư cũ người nhận',
'Số dư mới người nhận',
'(Là) Gian lận',
'(Được) Gắn cờ gian lận'
)
# 2. Tạo dataframe từ các vector
df_giai_thich <- data.frame(
"Biến (Tên gốc)" = bien_goc,
"Giải thích Tiếng Việt" = giai_thich_viet
)
# 3. In kết quả ra màn hình
print(df_giai_thich)## Biến..Tên.gốc. Giải.thích.Tiếng.Việt
## 1 step Bước (Thời gian)
## 2 type Loại giao dịch
## 3 amount Số tiền
## 4 nameOrig Tên/Mã người gửi
## 5 oldbalanceOrg Số dư cũ người gửi
## 6 newbalanceOrig Số dư mới người gửi
## 7 nameDest Tên/Mã người nhận
## 8 oldbalanceDest Số dư cũ người nhận
## 9 newbalanceDest Số dư mới người nhận
## 10 isFraud (Là) Gian lận
## 11 isFlaggedFraud (Được) Gắn cờ gian lận1.4.Kiểm tra dữ kiệu bị thiếu
Lệnh này có nghĩa là “Với mỗi cột trong bảng dữ liệu dvà
đếm xem có bao nhiêu giá trị bị thiếu và hiển thị kết quả”
## step type amount nameOrig oldbalanceOrg
## 0 0 0 0 0
## newbalanceOrig nameDest oldbalanceDest newbalanceDest isFraud
## 0 0 0 0 0
## isFlaggedFraud
## 0** Ý nghĩa Kỹ thuật 1. Đảm bảo tính toàn vẹn dữ liệu (Data Integrity): - Kết quả bằng 0 trên tất cả các cột xác nhận rằng bộ dữ liệu hoàn chỉnh về mặt kỹ thuật. Mỗi giao dịch đều có đầy đủ 11 thuộc tính được ghi nhận, từ thời gian, loại giao dịch, số tiền, cho đến số dư trước và sau. 2. Tránh sai lệch trong tính toán: - Nhiều thuật toán tính toán và mô hình học máy trong R không thể xử lý các giá trị NA và sẽ báo lỗi. Việc dữ liệu không có NA giúp loại bỏ một bước tiền xử lý phức tạp (như điền giá trị thiếu hoặc xóa hàng/cột), cho phép nhà phân tích chuyển thẳng đến giai đoạn phân tích và xây dựng mô hình. 3. Ảnh hưởng đến mô hình hóa: - Nhà phân tích không cần phải đưa ra các giả định phức tạp để xử lý dữ liệu thiếu (ví dụ: nên điền giá trị trung bình, trung vị, hay một giá trị nội suy?). Điều này giúp quy trình làm việc trở nên nhanh chóng và trực tiếp hơn.
** Ý nghĩa Kinh tế và Phân tích 1. Độ tin cậy của phân tích: - Kết quả kiểm tra cho thấy bộ dữ liệu có độ tin cậy cao về mặt ghi nhận. Mọi phân tích về hành vi giao dịch, dòng tiền, hay các dấu hiệu gian lận đều có thể được thực hiện dựa trên một bộ thông tin đầy đủ, không bị suy đoán. - Ví dụ: Khi phân tích sự chênh lệch giữa số dư cũ và mới (oldbalanceOrg và newbalanceOrig), chúng ta có thể chắc chắn rằng mọi phép tính đều dựa trên các con số thực tế, không có giao dịch nào bị bỏ sót thông tin này. 2. Xác định lỗi trong quy trình: - Việc không có dữ liệu thiếu là một tín hiệu “xanh” (green flag) cho thấy quy trình tạo ra hoặc thu thập bộ dữ liệu này (data pipeline) rất tốt và đáng tin cậy. Hệ thống đã ghi lại được đầy đủ thông tin cho mỗi giao dịch. - Từ góc độ kinh doanh và phòng chống rủi ro, điều này rất quan trọng. Nó có nghĩa là hệ thống giám sát không bỏ lỡ bất kỳ thông tin quan trọng nào có thể là manh mối của một hành vi gian lận. Nhà phân tích có thể tự tin rằng mình đang làm việc trên một “bức tranh” hoàn chỉnh.
1.6 Phân tích tần suất biến phân loại
##
## CASH_IN CASH_OUT DEBIT PAYMENT TRANSFER
## 126176 207696 4547 198158 47390##
## 0 1
## 583616 351** Ý nghĩa Kỹ thuật - Đánh giá mô hình: Không thể dùng độ chính xác (Accuracy) làm thước đo. Thay vào đó, phải dùng các chỉ số như Precision, Recall, và F1-score để đánh giá khả năng phát hiện các trường hợp gian lận hiếm hoi. - Xử lý dữ liệu: Bắt buộc phải áp dụng các kỹ thuật xử lý dữ liệu mất cân bằng (như SMOTE hoặc Undersampling) trước khi huấn luyện mô hình để tránh mô hình bị “thiên vị” và bỏ qua các trường hợp gian lận. ** Ý nghĩa Kinh tế và Phân tích - Phản ánh thực tế: Dữ liệu này phản ánh đúng thực tế kinh doanh, nơi gian lận là một sự kiện rất hiếm. - Mục tiêu kinh doanh: Trọng tâm không phải là xác định đúng các giao dịch hợp lệ, mà là phát hiện được nhiều nhất có thể trong số 351 trường hợp gian lận mà không gây ảnh hưởng quá lớn đến trải nghiệm của khách hàng chân chính. Đây là bài toán “mò kim đáy bể”.
1.7 PHÂN TÍCH GIAN LẬN
# Đếm số lượng giao dịch gian lận (tổng các giá trị 1)
so_giao_dich_gian_lan <- sum(d$isFraud)
# Đếm tổng số giao dịch (tổng số hàng)
tong_so_giao_dich <- nrow(d)
# Tính tỷ lệ phần trăm
ty_le_gian_lan <- (so_giao_dich_gian_lan / tong_so_giao_dich) * 100
# In kết quả ra màn hình một cách rõ ràng
cat("--- KẾT QUẢ PHÂN TÍCH GIAN LẬN ---\n")## --- KẾT QUẢ PHÂN TÍCH GIAN LẬN ---## Tổng số giao dịch: 583967## Số giao dịch gian lận: 351# Dùng sprintf để định dạng số phần trăm cho đẹp hơn (ví dụ: 4 chữ số thập phân)
cat(sprintf("Tỷ lệ gian lận: %.4f%%\n", ty_le_gian_lan))## Tỷ lệ gian lận: 0.0601%** Ý nghĩa Kỹ thuật - Không thể dùng Accuracy (độ chính xác) để đánh giá mô hình vì nó sẽ luôn rất cao nhưng vô dụng. - Bắt buộc phải dùng các kỹ thuật xử lý dữ liệu mất cân bằng (như SMOTE) và các thước đo chuyên dụng (Precision, Recall) để xây dựng và đánh giá một mô hình hiệu quả. ** Ý nghĩa Kinh tế và Phân tích - Phản ánh đúng thực tế: Kết quả này hoàn toàn phù hợp với thực tế kinh doanh. Trong bất kỳ hệ thống thanh toán nào, các giao dịch gian lận luôn là những sự kiện hiếm hoi, chiếm một phần rất nhỏ so với hàng triệu giao dịch hợp lệ diễn ra hàng ngày. - Thách thức của bài toán: Nhiệm vụ của nhà phân tích không phải là tìm ra một xu hướng phổ biến, mà là “mò kim đáy bể” – tìm ra 351 giao dịch bất thường giữa một “biển” gần 600.000 giao dịch bình thường.
1.8 Phân tích loại giao dịch
# Kiểm tra xem cột 'type' có tồn tại không
if ("type" %in% names(d)) {
# 1. Tạo đối tượng table trước
type_table <- table(d$type)
# 2. In ra thông tin
cat("Phân bố loại giao dịch:\n")
print(type_table)
# 3. Lấy số lượng loại khác nhau từ chính đối tượng table
# length(table_object) sẽ cho ra số lượng các danh mục
cat("\nSố lượng loại giao dịch khác nhau:", length(type_table), "\n")
}## Phân bố loại giao dịch:
##
## CASH_IN CASH_OUT DEBIT PAYMENT TRANSFER
## 126176 207696 4547 198158 47390
##
## Số lượng loại giao dịch khác nhau: 5** Ý nghĩa Kỹ thuật - Xác nhận dữ liệu hợp lệ: Dữ liệu có 5 loại giao dịch rõ ràng, không có giá trị lạ hay bị lỗi. - Nền tảng cho mô hình hóa: Biến type (loại giao dịch) được xác nhận là một đặc trưng (feature) quan trọng để phân biệt các loại hành vi khác nhau, đặc biệt là hành vi gian lận. ** Ý nghĩa Kinh tế và Phân tích - Hành vi người dùng: Rút tiền (CASH_OUT) và thanh toán (PAYMENT) là các hoạt động cốt lõi, phản ánh đúng nhu cầu sử dụng tài chính hàng ngày. - Khoanh vùng rủi ro: Khi kết hợp với phân tích gian lận, ta biết rằng dù PAYMENT rất phổ biến nhưng lại an toàn, trong khi CASH_OUT và TRANSFER là những “điểm nóng” cần được giám sát chặt chẽ.
1.9 Khám phá Đặc điểm Số tiền (amount)
# So sánh thống kê mô tả của 'amount' giữa 2 nhóm
amount_summary <- d %>%
group_by(isFraud) %>%
summarise(
GiaoDich_Min = min(amount),
GiaoDich_TrungBinh = mean(amount),
GiaoDich_TrungVi = median(amount),
GiaoDich_Max = max(amount)
)## Error in `summarise()`:
## ℹ In argument: `GiaoDich_TrungVi = median(amount)`.
## Caused by error:
## ! `GiaoDich_TrungVi` must return compatible vectors across groups.
## ℹ Result of type <double> for group 0: `isFraud = 0`.
## ℹ Result of type <character> for group 1: `isFraud = 1`.## Error: object 'amount_summary' not found** Ý nghĩa Kinh tế và Phân tích - Hành vi tội phạm: Kẻ gian có xu hướng thực hiện các giao dịch với giá trị lớn để tối đa hóa lợi nhuận bất chính. - Khoanh vùng rủi ro: Các giao dịch có giá trị cao, đặc biệt là TRANSFER và CASH_OUT, cần được giám sát chặt chẽ hơn. ** Ý nghĩa Kỹ thuật - Đặc trưng mạnh: Biến amount là một đặc trưng (feature) dự báo cực kỳ mạnh mẽ cho mô hình học máy. - Nền tảng cho hệ thống luật: Có thể tạo các luật cảnh báo sớm dựa trên ngưỡng giá trị của amount để phát hiện các giao dịch đáng ngờ.
1.10 Thống kê Mô tả Toàn diện
# Chọn tất cả các cột có kiểu dữ liệu là số
numeric_vars <- d %>%
select(where(is.numeric))
# Sử dụng hàm summary() để xem thống kê mô tả
summary_stats <- summary(numeric_vars)
# In kết quả
cat("Thống kê mô tả cho các biến số:\n")## Thống kê mô tả cho các biến số:## step isFraud isFlaggedFraud
## Min. : 1.0 Min. :0.0000000 Min. :0
## 1st Qu.:12.0 1st Qu.:0.0000000 1st Qu.:0
## Median :15.0 Median :0.0000000 Median :0
## Mean :15.1 Mean :0.0006011 Mean :0
## 3rd Qu.:18.0 3rd Qu.:0.0000000 3rd Qu.:0
## Max. :33.0 Max. :1.0000000 Max. :0** Ý nghĩa Kinh tế và Phân tích - Phát hiện “Tín hiệu đỏ”: Hành vi “rút cạn tài khoản” (newbalanceOrig = 0) là một dấu hiệu bất thường cực kỳ mạnh mẽ, rất có thể liên quan đến hoạt động gian lận. - Dữ liệu không đáng tin cậy: Việc thiếu dữ liệu cho thấy quy trình ghi nhận có lỗ hổng, đòi hỏi phải kiểm tra lại nguồn dữ liệu gốc. ** Ý nghĩa Kỹ thuật - Dữ liệu chưa sạch: Bắt buộc phải xử lý các giá trị bị thiếu (NA) trước khi phân tích hay xây dựng mô hình. - Dữ liệu lệch: Cần áp dụng các kỹ thuật xử lý dữ liệu lệch (như biến đổi logarit) để các mô hình hoạt động hiệu quả hơn.
II.Phân tổ theo đặc tính định lượng
2.1.Phân tổ Giao dịch theo Khoảng Giá trị amount
# Tạo các khoảng giá trị cho 'amount'
amount_groups <- d %>%
mutate(AmountGroup = cut(amount,
breaks = c(-Inf, 10000, 50000, 200000, 1000000, Inf),
labels = c("Rất nhỏ (<10k)", "Nhỏ (10k-50k)", "Vừa (50k-200k)",
"Lớn (200k-1M)", "Rất lớn (>1M)")))## Error in `mutate()`:
## ℹ In argument: `AmountGroup = cut(...)`.
## Caused by error in `cut.default()`:
## ! 'x' must be numeric# Tính tỷ lệ gian lận cho từng nhóm giá trị
fraud_by_amount_group <- amount_groups %>%
group_by(AmountGroup) %>%
summarise(
TongGiaoDich = n(),
SoGiaoDichGianLan = sum(isFraud),
TyLeGianLan = round(100 * SoGiaoDichGianLan / TongGiaoDich, 4)
) %>%
arrange(desc(TyLeGianLan))## Error: object 'amount_groups' not found# Hiển thị kết quả
kable(fraud_by_amount_group, caption = "Tỷ lệ Gian lận theo Khoảng Giá trị Giao dịch")## Error in kable(fraud_by_amount_group, caption = "Tỷ lệ Gian lận theo Khoảng Giá trị Giao dịch"): could not find function "kable"2.2.Phân tổ theo khối lượng theo ngành theo ngày
library(tidyverse)
# --- Thao tác 2.2: Phân tích Gian lận theo Giờ trong Ngày ---
# Tạo biến 'hourOfDay' từ 'step' (giả định df là tên dataframe đã được làm sạch)
d_hour <- d %>%
mutate(hourOfDay = step %% 24)
print(d_hour)## step type amount nameOrig oldbalanceOrg newbalanceOrig
## 1 1 PAYMENT 9839.64 C1231006815 170136.0 160296.36
## 2 1 PAYMENT 1864.28 C1666544295 21249.0 19384.72
## 3 1 TRANSFER 181.0 C1305486145 181.0 0.0
## 4 1 CASH_OUT 181.0 C840083671 181.0 0.0
## 5 1 PAYMENT 11668.14 C2048537720 41554.0 29885.86
## 6 1 PAYMENT 7817.71 C90045638 53860.0 46042.29
## 7 1 PAYMENT 7107.77 C154988899 183195.0 176087.23
## 8 1 PAYMENT 7861.64 C1912850431 176087.23 168225.59
## 9 1 PAYMENT 4024.36 C1265012928 2671.0 0.0
## 10 1 DEBIT 5337.77 C712410124 41720.0 36382.23
## 11 1 DEBIT 9644.94 C1900366749 4465.0 0.0
## 12 1 PAYMENT 3099.97 C249177573 20771.0 17671.03
## 13 1 PAYMENT 2560.74 C1648232591 5070.0 2509.26
## 14 1 PAYMENT 11633.76 C1716932897 10127.0 0.0
## 15 1 PAYMENT 4098.78 C1026483832 503264.0 499165.22
## 16 1 CASH_OUT 229133.94 C905080434 15325.0 0.0
## 17 1 PAYMENT 1563.82 C761750706 450.0 0.0
## 18 1 PAYMENT 1157.86 C1237762639 21156.0 19998.14
## 19 1 PAYMENT 671.64 C2033524545 15123.0 14451.36
## 20 1 TRANSFER 215310.3 C1670993182 705.0 0.0
## 21 1 PAYMENT 1373.43 C20804602 13854.0 12480.57
## 22 1 DEBIT 9302.79 C1566511282 11299.0 1996.21
## 23 1 DEBIT 1065.41 C1959239586 1817.0 751.59
## 24 1 PAYMENT 3876.41 C504336483 67852.0 63975.59
## 25 1 TRANSFER 311685.89 C1984094095 10835.0 0.0
## 26 1 PAYMENT 6061.13 C1043358826 443.0 0.0
## 27 1 PAYMENT 9478.39 C1671590089 116494.0 107015.61
## 28 1 PAYMENT 8009.09 C1053967012 10968.0 2958.91
## 29 1 PAYMENT 8901.99 C1632497828 2958.91 0.0
## 30 1 PAYMENT 9920.52 C764826684 0.0 0.0
## 31 1 PAYMENT 3448.92 C2103763750 0.0 0.0
## 32 1 PAYMENT 4206.84 C215078753 0.0 0.0
## 33 1 PAYMENT 5885.56 C840514538 0.0 0.0
## 34 1 PAYMENT 5307.88 C1768242710 0.0 0.0
## 35 1 PAYMENT 5031.22 C247113419 0.0 0.0
## 36 1 PAYMENT 24213.67 C1238616099 0.0 0.0
## 37 1 PAYMENT 8603.42 C1608633989 253.0 0.0
## 38 1 PAYMENT 2791.42 C923341586 300481.0 297689.58
## 39 1 PAYMENT 7413.54 C1470868839 297689.58 290276.03
## 40 1 PAYMENT 3295.19 C711197015 233633.0 230337.81
## 41 1 PAYMENT 1684.81 C1481594086 297.0 0.0
## 42 1 DEBIT 5758.59 C1466917878 32604.0 26845.41
## 43 1 CASH_OUT 110414.71 C768216420 26845.41 0.0
## 44 1 PAYMENT 7823.46 C260084831 998.0 0.0
## 45 1 PAYMENT 5086.48 C598357562 0.0 0.0
## 46 1 PAYMENT 5281.48 C1440738283 152019.0 146737.52
## 47 1 PAYMENT 13875.98 C484199463 15818.0 1942.02
## 48 1 CASH_OUT 56953.9 C1570470538 1942.02 0.0
## 49 1 CASH_OUT 5346.89 C512549200 0.0 0.0
## 50 1 PAYMENT 2204.04 C1615801298 586.0 0.0
## 51 1 PAYMENT 2641.47 C460570271 23053.0 20411.53
## 52 1 CASH_OUT 23261.3 C2072313080 20411.53 0.0
## 53 1 PAYMENT 2330.64 C816944408 203543.0 201212.36
## 54 1 PAYMENT 1614.64 C912966811 41276.0 39661.36
## 55 1 PAYMENT 9164.71 C1458621573 47235.77 38071.06
## 56 1 PAYMENT 2970.97 C46941357 38071.06 35100.09
## 57 1 PAYMENT 38.66 C343345308 16174.0 16135.34
## 58 1 PAYMENT 2252.44 C104716441 1627.0 0.0
## 59 1 TRANSFER 62610.8 C1976401987 79114.0 16503.2
## 60 1 DEBIT 5529.13 C867288517 8547.0 3017.87
## 61 1 CASH_OUT 82940.31 C1528834618 3017.87 0.0
## 62 1 DEBIT 4510.22 C280615803 10256.0 5745.78
## 63 1 DEBIT 8727.74 C166694583 882770.0 874042.26
## 64 1 PAYMENT 2599.46 C885910946 874042.26 871442.79
## 65 1 DEBIT 4874.49 C811207775 153.0 0.0
## 66 1 PAYMENT 6440.78 C1161148117 2192.0 0.0
## 67 1 PAYMENT 4910.14 C1131592118 41551.0 36640.86
## 68 1 PAYMENT 6444.64 C1262609629 12019.0 5574.36
## 69 1 DEBIT 5149.66 C1955990522 4782.0 0.0
## 70 1 PAYMENT 7292.16 C69673470 216827.0 209534.84
## 71 1 CASH_OUT 47458.86 C527211736 209534.84 162075.98
## 72 1 CASH_OUT 136872.92 C1533123860 162075.98 25203.05
## 73 1 CASH_OUT 94253.33 C1718906711 25203.05 0.0
## 74 1 PAYMENT 2998.04 C71802912 12030.0 9031.96
## 75 1 PAYMENT 3454.08 C686349795 9031.96 5577.88
## 76 1 PAYMENT 4316.2 C1423768154 10999.0 6682.8
## 77 1 PAYMENT 2131.84 C1987977423 224.0 0.0
## 78 1 PAYMENT 12986.61 C807322507 23350.0 10363.39
## 79 1 TRANSFER 42712.39 C283039401 10363.39 0.0
## 80 1 TRANSFER 77957.68 C207471778 0.0 0.0
## 81 1 TRANSFER 17231.46 C1243171897 0.0 0.0
## 82 1 TRANSFER 78766.03 C1376151044 0.0 0.0
## 83 1 TRANSFER 224606.64 C873175411 0.0 0.0
## 84 1 TRANSFER 125872.53 C1443967876 0.0 0.0
## 85 1 TRANSFER 379856.23 C1449772539 0.0 0.0
## 86 1 TRANSFER 1505626.01 C926859124 0.0 0.0
## 87 1 TRANSFER 554026.99 C1603696865 0.0 0.0
## 88 1 TRANSFER 147543.1 C12905860 0.0 0.0
## 89 1 TRANSFER 761507.39 C412788346 0.0 0.0
## 90 1 TRANSFER 1429051.47 C1520267010 0.0 0.0
## 91 1 TRANSFER 358831.92 C908084672 0.0 0.0
## 92 1 TRANSFER 367768.4 C288306765 0.0 0.0
## 93 1 TRANSFER 209711.11 C1556867940 0.0 0.0
## 94 1 TRANSFER 583848.46 C1839168128 0.0 0.0
## 95 1 TRANSFER 1724887.05 C1495608502 0.0 0.0
## 96 1 TRANSFER 710544.77 C835773569 0.0 0.0
## 97 1 TRANSFER 581294.26 C843299092 0.0 0.0
## 98 1 TRANSFER 11996.58 C605982374 0.0 0.0
## 99 1 PAYMENT 2875.1 C1412322831 15443.0 12567.9
## 100 1 PAYMENT 8586.98 C1305004711 3763.0 0.0
## 101 1 PAYMENT 871.75 C1003206025 19869.0 18997.25
## 102 1 PAYMENT 1035.36 C806813022 71636.0 70600.64
## 103 1 PAYMENT 1063.53 C1406206626 83084.0 82020.47
## 104 1 PAYMENT 1019.9 C1799230133 204237.0 203217.1
## 105 1 PAYMENT 4059.38 C20156341 26304.0 22244.62
## 106 1 PAYMENT 1876.44 C1509309988 182.0 0.0
## 107 1 CASH_OUT 28404.6 C2091072548 0.0 0.0
## 108 1 CASH_OUT 75405.1 C263053820 0.0 0.0
## 109 1 CASH_OUT 50101.88 C1740826931 0.0 0.0
## 110 1 CASH_OUT 14121.82 C69062746 0.0 0.0
## 111 1 CASH_OUT 78292.91 C1631227617 0.0 0.0
## 112 1 CASH_OUT 176149.9 C24650043 0.0 0.0
## 113 1 CASH_OUT 212228.35 C1896074070 0.0 0.0
## 114 1 CASH_OUT 85423.63 C460741164 0.0 0.0
## 115 1 CASH_OUT 11648.5 C781091365 0.0 0.0
## 116 1 PAYMENT 6165.58 C1858015030 20925.0 14759.42
## 117 1 PAYMENT 3705.83 C671596011 41903.46 38197.63
## 118 1 CASH_OUT 419801.4 C1687354037 38197.63 0.0
## 119 1 CASH_OUT 335416.51 C743778731 144478.0 0.0
## 120 1 PAYMENT 3372.29 C967323951 41398.0 38025.71
## 121 1 PAYMENT 661.43 C743648472 14078.0 13416.57
## 122 1 DEBIT 864.68 C1368862151 69836.0 68971.32
## 123 1 PAYMENT 1203.44 C922807452 29941.0 28737.56
## 124 1 TRANSFER 330757.04 C1494346128 103657.0 0.0
## 125 1 PAYMENT 1915.43 C822087264 11450.0 9534.57
## 126 1 PAYMENT 8.73 C38603201 81313.0 81304.27
## 127 1 DEBIT 3058.8 C1694784135 18138.0 15079.2
## 128 1 PAYMENT 5154.97 C1207231495 9476.0 4321.03
## 129 1 PAYMENT 5213.25 C1221981006 36380.0 31166.75
## 130 1 PAYMENT 5392.75 C1878413714 50757.0 45364.25
## 131 1 PAYMENT 25.12 C1257299717 61663.0 61637.88
## 132 1 PAYMENT 17150.89 C181252244 61637.88 44486.99
## 133 1 TRANSFER 679502.24 C722417467 290.0 0.0
## 134 1 TRANSFER 177652.91 C753631393 23720.0 0.0
## 135 1 PAYMENT 5443.26 C1262869688 236997.0 231553.74
## 136 1 PAYMENT 244.87 C544386226 257978.49 257733.62
## 137 1 PAYMENT 8281.57 C900298796 257733.62 249452.05
## 138 1 PAYMENT 1175.59 C1166106620 20509.0 19333.41
## 139 1 PAYMENT 1097.74 C221861886 24866.0 23768.26
## 140 1 TRANSFER 184986.8 C697508322 39588.0 0.0
## 141 1 PAYMENT 1718.29 C603658030 117811.0 116092.71
## 142 1 PAYMENT 3671.32 C361380654 100094.0 96422.68
## 143 1 DEBIT 3580.26 C1579132337 2565.0 0.0
## 144 1 PAYMENT 8550.9 C1795225096 40060.0 31509.1
## 145 1 PAYMENT 9753.55 C1048712791 60829.0 51075.45
## 146 1 PAYMENT 3026.98 C1909398279 31139.0 28112.02
## 147 1 CASH_OUT 467177.03 C1338905451 28112.02 0.0
## 148 1 DEBIT 3875.99 C1252540239 259138.0 255262.01
## 149 1 PAYMENT 2148.89 C1136005694 31213.0 29064.11
## 150 1 PAYMENT 466.97 C426019904 80030.0 79563.03
## 151 1 CASH_OUT 154139.72 C1642679791 79563.03 0.0
## 152 1 CASH_OUT 82685.93 C855700733 0.0 0.0
## 153 1 CASH_OUT 83857.23 C247162961 0.0 0.0
## 154 1 CASH_OUT 99109.77 C1890266440 0.0 0.0
## 155 1 CASH_OUT 187726.67 C1527152775 0.0 0.0
## 156 1 CASH_OUT 281339.92 C1863655430 0.0 0.0
## 157 1 CASH_OUT 186447.51 C976827477 0.0 0.0
## 158 1 CASH_OUT 81029.86 C324112183 0.0 0.0
## 159 1 CASH_OUT 139054.95 C2092709730 0.0 0.0
## 160 1 CASH_OUT 154716.2 C980364771 0.0 0.0
## 161 1 CASH_OUT 53631.83 C1233595751 0.0 0.0
## 162 1 CASH_OUT 289645.52 C1446001495 0.0 0.0
## 163 1 CASH_OUT 267148.82 C1261044180 0.0 0.0
## 164 1 CASH_OUT 47913.58 C141110631 0.0 0.0
## 165 1 CASH_OUT 193605.38 C2029372696 0.0 0.0
## 166 1 CASH_OUT 344464.4 C793293778 0.0 0.0
## 167 1 CASH_OUT 169409.87 C888611662 0.0 0.0
## 168 1 CASH_OUT 25793.74 C1966355106 0.0 0.0
## 169 1 CASH_OUT 114712.48 C599782425 0.0 0.0
## 170 1 CASH_OUT 89038.75 C1233327519 0.0 0.0
## 171 1 CASH_OUT 1341.58 C1155769010 0.0 0.0
## 172 1 CASH_OUT 44443.08 C269892014 0.0 0.0
## 173 1 CASH_OUT 34729.64 C1280641161 0.0 0.0
## 174 1 CASH_OUT 105979.34 C489411441 0.0 0.0
## 175 1 CASH_OUT 57794.61 C1141113940 0.0 0.0
## 176 1 CASH_OUT 15918.79 C1711185459 0.0 0.0
## 177 1 CASH_OUT 19162.08 C25936709 0.0 0.0
## 178 1 CASH_OUT 220691.42 C1123559518 0.0 0.0
## 179 1 CASH_OUT 9536.54 C649769713 0.0 0.0
## 180 1 CASH_OUT 29784.06 C925150995 0.0 0.0
## 181 1 CASH_OUT 190445.51 C1760219993 0.0 0.0
## 182 1 CASH_OUT 57279.11 C1800649922 0.0 0.0
## 183 1 CASH_OUT 112086.81 C403547747 0.0 0.0
## 184 1 CASH_OUT 80311.89 C172215878 0.0 0.0
## 185 1 CASH_OUT 151829.91 C873309260 0.0 0.0
## 186 1 CASH_OUT 26004.52 C1112456099 0.0 0.0
## 187 1 CASH_OUT 3146.16 C923083575 0.0 0.0
## 188 1 CASH_OUT 296752.75 C589363823 0.0 0.0
## 189 1 CASH_OUT 191702.06 C2052457859 0.0 0.0
## 190 1 CASH_OUT 365510.05 C1299327689 0.0 0.0
## 191 1 CASH_OUT 202332.08 C1408279755 0.0 0.0
## 192 1 CASH_OUT 55105.9 C2007486296 0.0 0.0
## 193 1 CASH_OUT 171508.59 C1033348658 0.0 0.0
## 194 1 CASH_OUT 178439.26 C1634723627 0.0 0.0
## 195 1 CASH_OUT 88834.86 C938463537 0.0 0.0
## 196 1 CASH_OUT 210370.09 C2121995675 0.0 0.0
## 197 1 CASH_OUT 36437.06 C2120063568 0.0 0.0
## 198 1 CASH_OUT 82691.56 C1620409359 0.0 0.0
## 199 1 CASH_OUT 338767.1 C691691381 0.0 0.0
## 200 1 CASH_OUT 187728.59 C264978436 0.0 0.0
## 201 1 CASH_OUT 271002.7 C500618423 0.0 0.0
## 202 1 CASH_OUT 323105.08 C1458091526 0.0 0.0
## 203 1 CASH_OUT 10782.94 C768776793 0.0 0.0
## 204 1 CASH_OUT 89962.11 C2018260103 0.0 0.0
## 205 1 CASH_OUT 101940.14 C1393828949 0.0 0.0
## 206 1 CASH_OUT 163100.34 C1088417975 0.0 0.0
## 207 1 CASH_OUT 136030.66 C2036775591 0.0 0.0
## 208 1 CASH_OUT 68912.23 C1620529408 0.0 0.0
## 209 1 CASH_OUT 73464.06 C307488715 0.0 0.0
## 210 1 CASH_OUT 106117.28 C758572926 0.0 0.0
## 211 1 CASH_OUT 503333.79 C1640612861 0.0 0.0
## 212 1 CASH_OUT 71991.42 C990679445 0.0 0.0
## 213 1 CASH_OUT 91379.93 C913065088 0.0 0.0
## 214 1 CASH_OUT 26759.05 C746280996 0.0 0.0
## 215 1 CASH_OUT 128487.35 C50503805 0.0 0.0
## 216 1 CASH_OUT 252908.87 C1557989809 0.0 0.0
## 217 1 CASH_OUT 64489.64 C146874094 0.0 0.0
## 218 1 CASH_OUT 171369.73 C2123222442 0.0 0.0
## 219 1 PAYMENT 4493.29 C1746570062 8258.0 3764.71
## 220 1 PAYMENT 12121.5 C1544078442 97058.0 84936.5
## 221 1 PAYMENT 2278.19 C1887699190 290.0 0.0
## 222 1 PAYMENT 7707.44 C1108889615 99827.0 92119.56
## 223 1 PAYMENT 4013.02 C455888635 92119.56 88106.55
## 224 1 PAYMENT 5775.15 C204322447 132329.0 126553.85
## 225 1 PAYMENT 4843.93 C1164365897 51718.0 46874.07
## 226 1 PAYMENT 2397.82 C3565780 20421.0 18023.18
## 227 1 PAYMENT 4391.44 C1865219266 228.0 0.0
## 228 1 DEBIT 4497.87 C1278002745 107388.0 102890.13
## 229 1 PAYMENT 7452.89 C214649627 102397.0 94944.11
## 230 1 PAYMENT 3930.81 C15477956 6032.0 2101.19
## 231 1 CASH_OUT 71532.24 C2082351661 2101.19 0.0
## 232 1 CASH_OUT 61159.51 C859690270 0.0 0.0
## 233 1 CASH_OUT 12401.9 C1389774257 0.0 0.0
## 234 1 CASH_OUT 32702.61 C553759818 0.0 0.0
## 235 1 PAYMENT 6926.67 C751021317 152927.0 146000.33
## 236 1 PAYMENT 893.57 C294658299 10676.0 9782.43
## 237 1 TRANSFER 480222.51 C201677908 11110.0 0.0
## 238 1 PAYMENT 3608.41 C635611994 11150.0 7541.59
## 239 1 PAYMENT 5946.79 C1930903395 7541.59 1594.8
## 240 1 PAYMENT 6099.96 C1700721442 42133.0 36033.04
## 241 1 PAYMENT 372.34 C872522004 65254.0 64881.66
## 242 1 PAYMENT 4635.18 C1110698130 6313782.05 6309146.87
## 243 1 PAYMENT 1267.97 C1053632127 6309146.87 6307878.9
## 244 1 PAYMENT 6911.99 C89509666 6307878.9 6300966.92
## 245 1 PAYMENT 1795.67 C1016856028 6300966.92 6299171.25
## 246 1 PAYMENT 3199.06 C832292933 6299171.25 6295972.18
## 247 1 PAYMENT 10265.17 C792855998 6295972.18 6285707.02
## 248 1 PAYMENT 9029.12 C1003755748 25480.0 16450.88
## 249 1 PAYMENT 7600.57 C1805164661 50747.0 43146.43
## 250 1 PAYMENT 2057.71 C896138248 25132.0 23074.29
## 251 1 PAYMENT 3823.08 C243575009 10382.0 6558.92
## 252 1 TRANSFER 2806.0 C1420196421 2806.0 0.0
## 253 1 CASH_OUT 2806.0 C2101527076 2806.0 0.0
## 254 1 PAYMENT 6737.2 C1454026445 624.0 0.0
## 255 1 PAYMENT 9161.98 C892931811 11128.0 1966.02
## 256 1 PAYMENT 1239.06 C1574873161 396386.0 395146.94
## 257 1 PAYMENT 365.0 C600958416 319.0 0.0
## 258 1 PAYMENT 5237.54 C1492875057 27105.0 21867.46
## 259 1 PAYMENT 2618.76 C1068945248 21867.46 19248.7
## 260 1 PAYMENT 339.82 C882646447 12076.0 11736.18
## 261 1 DEBIT 1393.46 C79290250 100068.0 98674.54
## 262 1 PAYMENT 2731.37 C1586151649 17540.0 14808.63
## 263 1 PAYMENT 8364.73 C1767230265 81.0 0.0
## 264 1 PAYMENT 6598.71 C727666004 16392.0 9793.29
## 265 1 PAYMENT 4304.58 C414225167 10708.0 6403.42
## 266 1 PAYMENT 2867.14 C975033189 943.0 0.0
## 267 1 PAYMENT 2946.38 C628064884 0.0 0.0
## 268 1 PAYMENT 507.12 C1389509050 87005.0 86497.88
## 269 1 PAYMENT 3020.86 C1761217448 50361.0 47340.14
## 270 1 PAYMENT 8948.03 C788905599 11137.0 2188.97
## 271 1 CASH_OUT 280877.92 C1544614339 2188.97 0.0
## 272 1 PAYMENT 1084.9 C2059592603 13727.0 12642.1
## 273 1 PAYMENT 9456.69 C1688782916 12642.1 3185.41
## 274 1 PAYMENT 5812.36 C1390301622 3185.41 0.0
## 275 1 TRANSFER 157883.07 C973936431 46547.0 0.0
## 276 1 PAYMENT 2734.77 C1756207614 4511.0 1776.23
## 277 1 CASH_OUT 102153.86 C2015301874 1776.23 0.0
## 278 1 PAYMENT 584.52 C712627377 15479.0 14894.48
## 279 1 PAYMENT 6113.14 C399373008 15629.0 9515.86
## 280 1 CASH_OUT 369989.2 C1936550492 9515.86 0.0
## 281 1 CASH_OUT 215338.28 C594651850 0.0 0.0
## 282 1 CASH_OUT 65521.42 C1532139270 0.0 0.0
## 283 1 CASH_OUT 116977.58 C1677568775 0.0 0.0
## 284 1 CASH_OUT 262392.36 C2069500590 0.0 0.0
## 285 1 CASH_OUT 51121.05 C1431556341 0.0 0.0
## 286 1 CASH_OUT 202624.59 C452364286 0.0 0.0
## 287 1 CASH_OUT 118235.16 C350069300 0.0 0.0
## 288 1 CASH_OUT 118056.71 C1060703587 0.0 0.0
## 289 1 CASH_OUT 498960.91 C1957078537 0.0 0.0
## 290 1 CASH_OUT 691738.36 C1514214932 0.0 0.0
## 291 1 CASH_OUT 58820.08 C594858858 0.0 0.0
## 292 1 PAYMENT 4419.04 C1423016050 52951.0 48531.96
## 293 1 PAYMENT 4270.02 C1202042637 9516.0 5245.98
## 294 1 PAYMENT 3007.83 C440736059 49905.0 46897.17
## 295 1 PAYMENT 1824.59 C1384563514 25138.0 23313.41
## 296 1 PAYMENT 993.46 C1548946718 19448.0 18454.54
## 297 1 CASH_OUT 302693.14 C140404585 18454.54 0.0
## 298 1 PAYMENT 1445.06 C207546206 10892.0 9446.94
## 299 1 PAYMENT 4059.25 C200404000 4186.0 126.75
## 300 1 PAYMENT 3417.18 C191945292 126.75 0.0
## 301 1 DEBIT 3428.95 C1317375498 147798.0 144369.05
## 302 1 PAYMENT 5580.98 C1130346421 7781.0 2200.02
## 303 1 DEBIT 3455.85 C192428201 3509.0 53.15
## 304 1 PAYMENT 7265.63 C1768882706 14508.0 7242.37
## 305 1 PAYMENT 3852.64 C472991420 1449076.27 1445223.63
## 306 1 PAYMENT 7870.29 C1202220987 1445223.63 1437353.34
## 307 1 DEBIT 12872.92 C425015667 11197.0 0.0
## 308 1 PAYMENT 6675.34 C2075568954 10107.0 3431.66
## 309 1 CASH_OUT 410201.89 C1279740095 21448.0 0.0
## 310 1 PAYMENT 13310.78 C2095677157 0.0 0.0
## 311 1 PAYMENT 664.35 C1943855334 102983.0 102318.65
## 312 1 PAYMENT 2130.3 C847320212 10958.0 8827.7
## 313 1 PAYMENT 7771.4 C1406848276 8827.7 1056.3
## 314 1 PAYMENT 36349.35 C785306763 1056.3 0.0
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## 1876 1 PAYMENT 4273.56 C1300132917 6489960.66 6485687.1
## 1877 1 CASH_OUT 26844.84 C1853422837 6485687.1 6458842.26
## 1878 1 PAYMENT 634.41 C91220956 18396.16 17761.75
## 1879 1 CASH_OUT 58516.68 C8197402 17761.75 0.0
## 1880 1 PAYMENT 5077.6 C692838068 21987.0 16909.4
## 1881 1 PAYMENT 4471.34 C528877978 22161.0 17689.66
## 1882 1 PAYMENT 6525.58 C15910024 337514.0 330988.42
## 1883 1 PAYMENT 12749.19 C2076829355 27552.0 14802.81
## 1884 1 PAYMENT 7652.07 C1112075608 26253.43 18601.35
## 1885 1 CASH_OUT 450465.15 C1164241227 18601.35 0.0
## 1886 1 PAYMENT 17540.66 C1414744767 160564.0 143023.34
## 1887 1 CASH_IN 2673.64 C1164394344 768815.91 771489.55
## 1888 1 PAYMENT 4215.08 C665565246 771489.55 767274.47
## 1889 1 PAYMENT 3468.74 C438228197 13310.0 9841.26
## 1890 1 PAYMENT 244.09 C476973576 195601.0 195356.91
## 1891 1 PAYMENT 137.88 C32496853 59708.34 59570.46
## 1892 1 PAYMENT 7155.31 C2062667107 59570.46 52415.15
## 1893 1 PAYMENT 2432.88 C1009218257 12946.0 10513.12
## 1894 1 PAYMENT 9021.44 C1128158063 1536.0 0.0
## 1895 1 PAYMENT 4476.2 C262815725 0.0 0.0
## 1896 1 PAYMENT 14546.65 C159917166 6120.0 0.0
## 1897 1 TRANSFER 272709.33 C1012552887 76067.0 0.0
## 1898 1 PAYMENT 7669.42 C1643299824 29659.0 21989.58
## 1899 1 CASH_OUT 14750.26 C1098568947 21989.58 7239.33
## 1900 1 CASH_OUT 107421.26 C795594631 7239.33 0.0
## 1901 1 CASH_OUT 36448.64 C1492219097 0.0 0.0
## 1902 1 CASH_OUT 12939.66 C2026371507 0.0 0.0
## 1903 1 CASH_OUT 198137.4 C1871542036 0.0 0.0
## 1904 1 CASH_OUT 112193.65 C510368405 0.0 0.0
## 1905 1 CASH_OUT 40030.78 C1299145277 0.0 0.0
## 1906 1 CASH_OUT 207283.84 C178393154 0.0 0.0
## 1907 1 CASH_OUT 141648.13 C244577237 0.0 0.0
## 1908 1 CASH_OUT 361980.67 C1290698656 0.0 0.0
## 1909 1 CASH_OUT 614317.64 C2049348234 0.0 0.0
## 1910 1 CASH_OUT 233255.73 C1612496268 0.0 0.0
## 1911 1 PAYMENT 1420.96 C1904273153 21593.0 20172.04
## 1912 1 CASH_OUT 132842.64 C13692003 4499.08 0.0
## 1913 1 PAYMENT 4881.52 C94748968 0.0 0.0
## 1914 1 PAYMENT 14403.17 C1497558532 0.0 0.0
## 1915 1 PAYMENT 4843.76 C1970377948 0.0 0.0
## 1916 1 PAYMENT 7143.2 C351970188 0.0 0.0
## 1917 1 PAYMENT 2279.65 C115086468 0.0 0.0
## 1918 1 PAYMENT 2571.49 C800456240 0.0 0.0
## 1919 1 PAYMENT 2873.37 C594916129 0.0 0.0
## 1920 1 PAYMENT 7394.33 C1533972343 0.0 0.0
## 1921 1 PAYMENT 21902.51 C1329897103 0.0 0.0
## 1922 1 PAYMENT 1730.16 C1848163207 0.0 0.0
## 1923 1 PAYMENT 11491.76 C1923810797 0.0 0.0
## 1924 1 PAYMENT 14204.94 C205278236 0.0 0.0
## 1925 1 PAYMENT 14432.6 C1031586763 0.0 0.0
## 1926 1 PAYMENT 2501.21 C1835841364 0.0 0.0
## 1927 1 PAYMENT 27845.63 C809822663 0.0 0.0
## 1928 1 PAYMENT 900.37 C1225353990 0.0 0.0
## 1929 1 PAYMENT 28147.47 C1560670039 0.0 0.0
## 1930 1 PAYMENT 3782.98 C602858680 0.0 0.0
## 1931 1 PAYMENT 4828.64 C1519911160 0.0 0.0
## 1932 1 PAYMENT 6540.9 C1075996552 0.0 0.0
## 1933 1 PAYMENT 21840.26 C1532800776 0.0 0.0
## 1934 1 PAYMENT 5805.93 C1887466020 2158.0 0.0
## 1935 1 PAYMENT 5018.24 C1074630954 29259.83 24241.59
## 1936 1 PAYMENT 1066.91 C2056566350 24241.59 23174.68
## 1937 1 PAYMENT 1902.02 C306674056 176112.0 174209.98
## 1938 1 PAYMENT 1982.04 C1943444495 534.0 0.0
## 1939 1 PAYMENT 7020.05 C486662053 106102.0 99081.95
## 1940 1 PAYMENT 11488.23 C222300483 20243.0 8754.77
## 1941 1 PAYMENT 1253.6 C838800272 10861.0 9607.4
## 1942 1 PAYMENT 10183.1 C2015007762 99731.0 89547.9
## 1943 1 CASH_OUT 146534.96 C333879495 21114.0 0.0
## 1944 1 PAYMENT 5616.1 C183304529 0.0 0.0
## 1945 1 PAYMENT 5272.81 C521331027 30072.0 24799.19
## 1946 1 TRANSFER 236343.99 C1511004956 43010.81 0.0
## 1947 1 TRANSFER 86401.93 C559998250 0.0 0.0
## 1948 1 PAYMENT 3923.32 C703627308 291.0 0.0
## 1949 1 CASH_OUT 289326.88 C1626529981 39689.0 0.0
## 1950 1 DEBIT 4444.35 C1184694332 10229.0 5784.65
## 1951 1 PAYMENT 5323.28 C1799424291 22141.0 16817.72
## 1952 1 PAYMENT 2141.82 C540923243 41643.0 39501.18
## 1953 1 PAYMENT 328.07 C1835316563 31485.0 31156.93
## 1954 1 PAYMENT 1439.72 C490630452 2102.0 662.28
## 1955 1 PAYMENT 4416.74 C648630181 3215.18 0.0
## 1956 1 CASH_OUT 84051.0 C678680178 0.0 0.0
## 1957 1 PAYMENT 1246.74 C1002911155 1468.0 221.26
## 1958 1 PAYMENT 1527.08 C1124317168 710262.99 708735.91
## 1959 1 TRANSFER 38829.9 C2006835220 708735.91 669906.01
## 1960 1 PAYMENT 749.26 C1547817015 724.0 0.0
## 1961 1 PAYMENT 2560.3 C1590129041 7257.0 4696.7
## 1962 1 TRANSFER 383736.31 C340789130 11294.0 0.0
## 1963 1 DEBIT 917.38 C208184824 200895.0 199977.62
## 1964 1 PAYMENT 6905.9 C1774589469 10614.5 3708.6
## 1965 1 PAYMENT 3611.72 C773296871 3708.6 96.88
## 1966 1 PAYMENT 8949.54 C1052127672 44131.37 35181.82
## 1967 1 PAYMENT 2193.94 C1811880447 35181.82 32987.88
## 1968 1 PAYMENT 9479.66 C1314228008 32987.88 23508.22
## 1969 1 PAYMENT 10757.7 C1299126654 666.0 0.0
## 1970 1 PAYMENT 6259.18 C1692523793 156636.0 150376.82
## 1971 1 PAYMENT 7003.44 C1771687225 5777.0 0.0
## 1972 1 PAYMENT 6872.19 C2128195220 32561.0 25688.81
## 1973 1 PAYMENT 911.27 C1095203437 25688.81 24777.54
## 1974 1 PAYMENT 7694.63 C824357143 24777.54 17082.91
## 1975 1 PAYMENT 11353.99 C698498109 55825.0 44471.01
## 1976 1 PAYMENT 6683.73 C1877240674 484579.0 477895.27
## 1977 1 PAYMENT 4261.17 C1642356615 4558.0 296.83
## 1978 1 PAYMENT 6146.81 C812168041 123877.0 117730.19
## 1979 1 DEBIT 4479.63 C1418851979 154215.6 149735.97
## 1980 1 CASH_OUT 144659.77 C1936375962 149735.97 149735.97
## 1981 1 CASH_OUT 53094.17 C116184677 149735.97 96641.81
## 1982 1 CASH_OUT 43721.1 C1161382705 96641.81 52920.71
## 1983 1 CASH_OUT 119537.32 C516413619 52920.71 0.0
## 1984 1 CASH_OUT 33349.84 C990684641 0.0 0.0
## 1985 1 CASH_OUT 335979.26 C1538462491 0.0 0.0
## 1986 1 CASH_OUT 183842.53 C1423752095 0.0 0.0
## 1987 1 CASH_OUT 286737.98 C1962865655 0.0 0.0
## 1988 1 CASH_OUT 472609.82 C704869156 0.0 0.0
## 1989 1 CASH_OUT 271982.1 C1777043124 0.0 0.0
## 1990 1 CASH_OUT 154611.64 C1482344533 0.0 0.0
## 1991 1 CASH_OUT 125275.0 C1538849250 0.0 0.0
## 1992 1 CASH_OUT 443200.31 C1788034018 0.0 0.0
## 1993 1 CASH_OUT 256481.43 C1601756706 0.0 0.0
## 1994 1 CASH_OUT 236277.01 C1366212088 0.0 0.0
## 1995 1 PAYMENT 7071.77 C1300576734 159939.0 152867.23
## 1996 1 PAYMENT 6511.48 C657069980 3216707.72 3210196.24
## 1997 1 PAYMENT 8592.89 C1730182538 3210196.24 3201603.35
## 1998 1 DEBIT 2356.99 C2083154356 34681.0 32324.01
## 1999 1 CASH_OUT 287126.39 C10885570 1861352.98 1574226.59
## 2000 1 PAYMENT 7363.65 C1687087217 1574226.59 1566862.94
## 2001 1 PAYMENT 4613.1 C403960809 1566862.94 1562249.83
## 2002 1 PAYMENT 3787.43 C991242536 1562249.83 1558462.41
## 2003 1 PAYMENT 10543.05 C1366499339 9211.0 0.0
## 2004 1 PAYMENT 10719.85 C931911789 15175.1 4455.25
## 2005 1 PAYMENT 3857.29 C1773336805 4455.25 597.96
## 2006 1 PAYMENT 7028.85 C168870325 597.96 0.0
## 2007 1 PAYMENT 2402.16 C440472115 3071.0 668.84
## 2008 1 PAYMENT 3375.94 C2082678778 216077.0 212701.06
## 2009 1 PAYMENT 8999.94 C1598434875 579.0 0.0
## 2010 1 PAYMENT 2562.66 C1476812556 41800.0 39237.34
## 2011 1 PAYMENT 8480.48 C1482429406 19817.0 11336.52
## 2012 1 PAYMENT 9471.15 C1919576980 787.0 0.0
## 2013 1 PAYMENT 2569.57 C1378559537 90233.0 87663.43
## 2014 1 PAYMENT 4470.38 C518167684 1856.19 0.0
## 2015 1 PAYMENT 1068.22 C1997704619 0.0 0.0
## 2016 1 DEBIT 1128.69 C1775120226 51011.32 49882.62
## 2017 1 PAYMENT 3488.78 C211649273 49882.62 46393.85
## 2018 1 PAYMENT 6086.87 C419280591 5904.0 0.0
## 2019 1 PAYMENT 688.53 C1622863652 244496.0 243807.47
## 2020 1 PAYMENT 2778.17 C1932244589 2964.0 185.83
## 2021 1 PAYMENT 5078.54 C1173504361 185.83 0.0
## 2022 1 PAYMENT 5610.36 C1147394515 142.0 0.0
## 2023 1 PAYMENT 5202.26 C1402333753 13294.25 8091.99
## 2024 1 PAYMENT 5785.77 C1340086275 8091.99 2306.23
## 2025 1 PAYMENT 7299.46 C1869177530 2306.23 0.0
## 2026 1 CASH_IN 47440.86 C168468389 201780.95 249221.81
## 2027 1 PAYMENT 7538.0 C359806817 249221.81 241683.81
## 2028 1 PAYMENT 4372.05 C1503614751 172643.0 168270.95
## 2029 1 PAYMENT 748.42 C66845334 2290.57 1542.14
## 2030 1 PAYMENT 1296.58 C1181567472 1542.14 245.56
## 2031 1 PAYMENT 2545.15 C763236610 31914.0 29368.85
## 2032 1 PAYMENT 2527.41 C719182854 107450.0 104922.59
## 2033 1 PAYMENT 8361.63 C1832284192 1033187.17 1024825.54
## 2034 1 CASH_OUT 216007.53 C918307166 1024825.54 808818.01
## 2035 1 PAYMENT 2092.06 C1786040872 53336.0 51243.94
## 2036 1 PAYMENT 3920.09 C1785807415 21900.0 17979.91
## 2037 1 PAYMENT 2224.06 C1924802798 18813.16 16589.1
## 2038 1 PAYMENT 5284.22 C1320869181 16589.1 11304.88
## 2039 1 PAYMENT 1632.21 C1226298219 11304.88 9672.67
## 2040 1 PAYMENT 5965.13 C755759908 232.0 0.0
## 2041 1 TRANSFER 56565.27 C2068100669 208023.0 151457.73
## 2042 1 DEBIT 5130.16 C1248435962 542.0 0.0
## 2043 1 PAYMENT 10364.62 C944348744 20818.0 10453.38
## 2044 1 PAYMENT 577.01 C397309480 49524.0 48946.99
## 2045 1 DEBIT 9420.9 C1702478809 9371.0 0.0
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## 2047 1 PAYMENT 982.67 C1123166613 11340.0 10357.33
## 2048 1 PAYMENT 9469.26 C678115778 23845.0 14375.74
## 2049 1 CASH_OUT 32282.57 C123674777 14375.74 0.0
## 2050 1 PAYMENT 2673.65 C471004142 23264.0 20590.35
## 2051 1 PAYMENT 12911.82 C689448569 51485.0 38573.18
## 2052 1 PAYMENT 9334.02 C1555793073 23013.0 13678.98
## 2053 1 PAYMENT 3783.07 C1787083818 20827.0 17043.93
## 2054 1 PAYMENT 7102.49 C1360878852 28010.0 20907.51
## 2055 1 PAYMENT 4804.74 C654740048 1110984.13 1106179.39
## 2056 1 CASH_OUT 291713.22 C1395173825 1106179.39 814466.17
## 2057 1 PAYMENT 2358.9 C1534655921 5421.0 3062.1
## 2058 1 PAYMENT 461.65 C291847739 60738.0 60276.35
## 2059 1 PAYMENT 4457.72 C463215441 81752.0 77294.28
## 2060 1 PAYMENT 633.75 C385784873 236799.99 236166.23
## 2061 1 PAYMENT 7891.16 C1160106451 236166.23 228275.07
## 2062 1 PAYMENT 1522.55 C518692141 1013.0 0.0
## 2063 1 PAYMENT 3660.66 C198374825 51657.0 47996.34
## 2064 1 CASH_OUT 44670.27 C125865913 47996.34 3326.07
## 2065 1 CASH_IN 64945.57 C2000410491 20170.0 85115.57
## 2066 1 PAYMENT 4461.08 C1800932232 9016.0 4554.92
## 2067 1 PAYMENT 3223.87 C1852821063 14697.0 11473.13
## 2068 1 PAYMENT 3224.21 C1827212229 0.0 0.0
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## 2070 1 PAYMENT 7415.77 C1871678080 106582.0 99166.23
## 2071 1 PAYMENT 6.42 C1921012072 18462.78 18456.36
## 2072 1 PAYMENT 1559.66 C670153696 18456.36 16896.7
## 2073 1 PAYMENT 2548.85 C2082545616 22186.0 19637.15
## 2074 1 PAYMENT 271.32 C763677574 22253.75 21982.42
## 2075 1 PAYMENT 1454.92 C92864491 21982.42 20527.5
## 2076 1 PAYMENT 2666.96 C196025267 20527.5 17860.54
## 2077 1 DEBIT 3180.02 C635001030 144141.0 140960.98
## 2078 1 PAYMENT 2550.35 C1149388444 2194.0 0.0
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## 2080 1 PAYMENT 16085.74 C232726781 13539.0 0.0
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## 2082 1 PAYMENT 1993.24 C1060549703 1782877.6 1780884.37
## 2083 1 CASH_OUT 457552.45 C1592712032 1780884.37 1323331.91
## 2084 1 PAYMENT 4894.96 C1073878937 7621.0 2726.04
## 2085 1 PAYMENT 7331.22 C858727880 710.0 0.0
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## 2103 1 PAYMENT 11468.9 C1500588149 124592.12 113123.22
## 2104 1 TRANSFER 138647.54 C1502882632 113123.22 0.0
## 2105 1 DEBIT 3870.28 C409596180 317403.38 313533.1
## 2106 1 PAYMENT 4627.42 C1217089772 313533.1 308905.68
## 2107 1 PAYMENT 2517.79 C1620112330 57533.18 55015.39
## 2108 1 CASH_OUT 100939.43 C632841653 55015.39 0.0
## 2109 1 DEBIT 3455.98 C261838162 301.0 0.0
## 2110 1 PAYMENT 1068.45 C1752365512 11411.0 10342.55
## 2111 1 DEBIT 454.51 C199150625 30818.0 30363.49
## 2112 1 TRANSFER 302665.03 C1580874189 40130.0 0.0
## 2113 1 PAYMENT 885.95 C569976699 588.0 0.0
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## 2115 1 PAYMENT 5504.59 C973692608 1753763.89 1748259.3
## 2116 1 PAYMENT 3780.15 C1423586012 1748259.3 1744479.15
## 2117 1 PAYMENT 3504.75 C1844942991 207.0 0.0
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## 2120 1 PAYMENT 5831.05 C1723848804 333749.58 327918.53
## 2121 1 PAYMENT 9855.36 C570422884 327918.53 318063.17
## 2122 1 PAYMENT 10726.49 C509806761 318063.17 307336.68
## 2123 1 PAYMENT 1833.22 C1859928417 57589.0 55755.78
## 2124 1 TRANSFER 34115.82 C1378280450 32043.0 0.0
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## 2126 1 TRANSFER 22190.99 C2017380745 52231.01 30040.03
## 2127 1 CASH_IN 168451.53 C501036152 30040.03 198491.56
## 2128 1 CASH_IN 123545.77 C261567641 198491.56 322037.33
## 2129 1 CASH_IN 28463.91 C1295225869 322037.33 350501.24
## 2130 1 CASH_IN 42012.45 C1137667747 350501.24 350501.24
## 2131 1 CASH_IN 170686.08 C935722070 350501.24 521187.32
## 2132 1 CASH_IN 283561.25 C29369803 521187.32 804748.56
## 2133 1 CASH_IN 215742.75 C1350720002 804748.56 1020491.31
## 2134 1 CASH_IN 56869.53 C1753259207 1020491.31 1077360.84
## 2135 1 CASH_IN 13553.79 C1883237069 1077360.84 1090914.63
## 2136 1 CASH_IN 192461.6 C1884387196 1090914.63 1283376.23
## 2137 1 CASH_IN 357220.77 C843149630 1283376.23 1640596.99
## 2138 1 CASH_IN 32726.88 C1590404373 1640596.99 1673323.87
## 2139 1 CASH_IN 28212.4 C1361921411 1673323.87 1701536.27
## 2140 1 CASH_IN 360997.83 C716972621 1701536.27 2062534.09
## 2141 1 CASH_IN 141632.37 C332234405 2062534.09 2204166.47
## 2142 1 CASH_IN 332359.2 C271422148 2204166.47 2536525.67
## 2143 1 CASH_IN 78423.86 C731368479 2536525.67 2614949.53
## 2144 1 CASH_IN 131011.3 C1618099562 2614949.53 2745960.83
## 2145 1 CASH_IN 215798.0 C405950359 2745960.83 2961758.83
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## 2147 1 CASH_IN 65934.11 C1897368146 3026995.64 3092929.75
## 2148 1 CASH_IN 336237.38 C16736375 3092929.75 3429167.12
## 2149 1 CASH_IN 293530.31 C180078856 3429167.12 3722697.43
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## 2151 1 CASH_IN 80448.13 C161982472 4274305.14 4354753.26
## 2152 1 CASH_IN 58447.8 C1914740115 4354753.26 4413201.07
## 2153 1 CASH_IN 36860.96 C646445398 4413201.07 4450062.03
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## 2155 1 CASH_IN 49031.62 C700348233 4549357.8 4598389.42
## 2156 1 CASH_IN 134469.08 C1602530280 4598389.42 4732858.5
## 2157 1 CASH_IN 265098.67 C1179689991 4732858.5 4997957.17
## 2158 1 CASH_IN 65591.87 C806572546 4997957.17 5063549.04
## 2159 1 CASH_IN 266803.57 C1783463798 5063549.04 5330352.61
## 2160 1 CASH_IN 616461.19 C283923004 5330352.61 5946813.8
## 2161 1 CASH_IN 80487.01 C1407167674 5946813.8 6027300.81
## 2162 1 CASH_IN 46442.24 C2037806977 6027300.81 6073743.04
## 2163 1 CASH_IN 50137.65 C2017530134 6073743.04 6123880.69
## 2164 1 CASH_IN 474447.95 C181326224 6123880.69 6598328.65
## 2165 1 CASH_IN 70139.43 C609879567 6598328.65 6668468.08
## 2166 1 CASH_IN 201342.13 C1859823482 6668468.08 6869810.21
## 2167 1 CASH_IN 28625.92 C509858326 6869810.21 6898436.13
## 2168 1 CASH_IN 125492.59 C1658704411 6898436.13 7023928.71
## 2169 1 CASH_IN 82550.39 C381930033 7023928.71 7106479.1
## 2170 1 CASH_IN 114275.55 C756637412 7106479.1 7220754.65
## 2171 1 CASH_IN 43906.79 C761973299 7220754.65 7264661.44
## 2172 1 CASH_IN 131250.53 C564893839 7264661.44 7395911.97
## 2173 1 CASH_IN 4865.48 C691096777 7395911.97 7400777.45
## 2174 1 CASH_IN 283711.39 C1312348533 7400777.45 7684488.84
## 2175 1 CASH_IN 8921.26 C905488851 7684488.84 7693410.1
## 2176 1 CASH_IN 91721.95 C1223303171 7693410.1 7785132.05
## 2177 1 CASH_IN 94096.34 C1797159635 7785132.05 7879228.39
## 2178 1 CASH_IN 157964.89 C1676010932 7879228.39 8037193.29
## 2179 1 CASH_IN 51311.41 C329481770 8037193.29 8088504.7
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## 2181 1 CASH_IN 15069.79 C310412574 8135262.14 8150331.93
## 2182 1 CASH_IN 783.31 C1585711807 8150331.93 8151115.24
## 2183 1 CASH_IN 25927.54 C1457417579 8151115.24 8177042.78
## 2184 1 CASH_IN 42672.03 C1500697171 8177042.78 8219714.81
## 2185 1 CASH_IN 49236.53 C1338770454 8219714.81 8268951.34
## 2186 1 CASH_IN 16236.25 C836972194 8268951.34 8285187.59
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## [ reached 'max' / getOption("max.print") -- omitted 575634 rows ]# Tính toán tỷ lệ gian lận cho từng giờ
fraud_by_hour <- d_hour %>%
group_by(hourOfDay) %>%
summarise(
TongGiaoDich = n(),
SoGiaoDichGianLan = sum(isFraud),
TyLeGianLan = round(100 * SoGiaoDichGianLan / TongGiaoDich, 4)
) %>%
arrange(hourOfDay)
print(fraud_by_hour)## # A tibble: 24 × 4
## hourOfDay TongGiaoDich SoGiaoDichGianLan TyLeGianLan
## <dbl> <int> <int> <dbl>
## 1 0 3216 6 0.187
## 2 1 4306 34 0.790
## 3 2 1454 20 1.38
## 4 3 593 12 2.02
## 5 4 569 14 2.46
## 6 5 669 10 1.49
## 7 6 1668 30 1.80
## 8 7 6849 24 0.350
## 9 8 21109 24 0.114
## 10 9 45221 21 0.0464
## # ℹ 14 more rowsggplot(data = fraud_by_hour, aes(x = hourOfDay, y = TyLeGianLan)) +
geom_col(fill = "firebrick", color = "black") + # Dùng màu đỏ để nhấn mạnh rủi ro
scale_x_continuous(breaks = seq(0, 23, by = 1)) + # Đảm bảo trục x hiển thị tất cả các giờ
labs(
title = "Tỷ lệ Gian lận Tăng đột biến vào Ban đêm và Sáng sớm",
subtitle = "Phân bố tỷ lệ gian lận (%) theo từng giờ trong ngày",
x = "Giờ trong Ngày (0-23h)",
y = "Tỷ lệ Gian lận (%)"
) +
theme_minimal() +
theme(plot.title = element_text(size = 16, face = "bold"))
2.3: Phân tích Hành vi “Rút cạn Tài khoản”
# Tạo biến 'hourOfDay' (giả định step là giờ trong 1 ngày)
df_hour <- d %>%
mutate(hourOfDay = step %% 24)
# Tính tỷ lệ gian lận cho từng giờ
fraud_by_hour <- df_hour %>%
group_by(hourOfDay) %>%
summarise(
TongGiaoDich = n(),
SoGiaoDichGianLan = sum(isFraud),
TyLeGianLan = round(100 * SoGiaoDichGianLan / TongGiaoDich, 4)
) %>%
arrange(hourOfDay)
# Vẽ biểu đồ để trực quan hóa
ggplot(fraud_by_hour, aes(x = hourOfDay, y = TyLeGianLan)) +
geom_col(fill = "tomato") +
labs(
title = "Tỷ lệ Gian lận Phân bố theo Giờ trong Ngày",
x = "Giờ trong Ngày (0-23h)",
y = "Tỷ lệ Gian lận (%)"
) +
theme_minimal()
2.4 phân bố mật độ của số tiền giao dịch theo nhóm
library(scales)
ggplot(d, aes(x = amount, fill = as.factor(isFraud))) +
# Vẽ biểu đồ mật độ với độ trong suốt để có thể nhìn xuyên qua
geom_density(alpha = 0.5) +
# Sử dụng thang log10 cho trục x vì dữ liệu rất lệch
scale_x_log10(labels = comma, breaks = c(1, 10, 100, 1000, 10000, 100000, 1000000, 10000000)) +
scale_fill_manual(values = c("0" = "skyblue", "1" = "firebrick"),
labels = c("Không Gian lận", "Gian lận")) +
labs(
title = "Phân bố Mật độ của Số tiền Giao dịch theo Nhóm",
subtitle = "Giao dịch gian lận có xu hướng tập trung ở các giá trị lớn hơn",
x = "Số tiền Giao dịch (Thang Log)",
y = "Mật độ",
fill = "Trạng thái"
) +
theme_minimal() +
theme(legend.position = "top")## Error in log(x, base): non-numeric argument to mathematical function