208.ANALYZING GLOBAL BANK FRAUDSTERS DATASET USING PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC SGPLOT | PROC MACRO | IN SAS FOR INSIGHTS ON FINANCIAL CRIME TYPES TRENDS PENALTIES RISKS WORLDWIDEANALYZING GLOBAL BANK FRAUDSTERS DATASET USING PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC SGPLOT | PROC MACRO | IN SAS FOR INSIGHTS ON FINANCIAL CRIME TYPES TRENDS PENALTIES RISKS WORLDWIDE

-

ANALYZING GLOBAL BANK FRAUDSTERS DATASET USING PROC PRINT | PROC MEANS | PROC FREQ | PROC SQL | PROC SGPLOT | PROC MACRO | IN SAS FOR INSIGHTS ON FINANCIAL CRIME TYPES TRENDS PENALTIES RISKS WORLDWIDE

 /*Different types of bank fraudsters worldwide*/

STEP 1: DATASET CREATION

data Bank_Fraudsters;

    length Fraud_ID 8 

           Fraudster_Name $25 

           Country $20 

           Fraud_Type $30 

           Amount_Lost_USD 8 

           Year 8 

           Method $25 

           Caught $3 

           Penalty_Years 8;

    input Fraud_ID Fraudster_Name $ Country $ Fraud_Type $ Amount_Lost_USD Year Method $ Caught $ Penalty_Years;

    datalines;

1 John_Doe USA Identity_Theft 125000 2019 Online_Yield Yes 5

2 Raj_Patel India Card_Cloning 84000 2020 ATM_Hack No .

3 Mei_Wang China Money_Laundering 430000 2021 Shell_Companies Yes 10

4 Carlos_Soto Mexico Loan_Scam 59000 2018 Fake_Records Yes 3

5 Fatima_Ali UAE Phishing 72000 2022 Email_Spoofing No .

6 Ivan_Kuznetsov Russia Crypto_Scam 205000 2021 Blockchain_Fraud No .

7 Jean_Martin France Investment_Fraud 110000 2020 Ponzi_Scheme Yes 6

8 Linda_Kim South_Korea Skimming 63000 2019 ATM_Device Yes 4

9 George_Smith UK Account_Takeover 95000 2022 SIM_Swap No .

10 Akira_Tanaka Japan Check_Fraud 27000 2018 Forged_Cheques Yes 2

11 Ahmed_Khan Pakistan Online_Scam 34000 2021 Fake_Websites No .

12 Maria_Rojas Brazil Money_Laundering 470000 2020 Shell_Companies Yes 8

13 Bongani_Ndlovu South_Africa Phishing 59000 2019 Email_Spoofing Yes 3

14 Emily_Wilson Canada Identity_Theft 88000 2022 Fake_Documents No .

15 Elena_Popov Ukraine Card_Cloning 77000 2021 ATM_Hack Yes 5

16 Andrew_Blake Australia Loan_Scam 66000 2020 Fake_Credit No .

17 Hassan_Musa Nigeria Investment_Fraud 92000 2021 Ponzi_Scheme No .

18 Ingrid_Andersson Sweden Online_Scam 58000 2019 Fake_Ecommerce Yes 2

19 Gopal_Sharma India SIM_Swap 69000 2022 Mobile_Fraud Yes 4

20 Tommy_Nguyen Vietnam Crypto_Scam 230000 2021 Rug_Pull No .

;

run;

proc print;run;

Output:

Obs Fraud_ID Fraudster_Name Country Fraud_Type Amount_Lost_USD Year Method Caught Penalty_Years
1 1 John_Doe USA Identity_Theft 125000 2019 Online_Yield Yes 5
2 2 Raj_Patel India Card_Cloning 84000 2020 ATM_Hack No .
3 3 Mei_Wang China Money_Laundering 430000 2021 Shell_Companies Yes 10
4 4 Carlos_Soto Mexico Loan_Scam 59000 2018 Fake_Records Yes 3
5 5 Fatima_Ali UAE Phishing 72000 2022 Email_Spoofing No .
6 6 Ivan_Kuznetsov Russia Crypto_Scam 205000 2021 Blockchain_Fraud No .
7 7 Jean_Martin France Investment_Fraud 110000 2020 Ponzi_Scheme Yes 6
8 8 Linda_Kim South_Korea Skimming 63000 2019 ATM_Device Yes 4
9 9 George_Smith UK Account_Takeover 95000 2022 SIM_Swap No .
10 10 Akira_Tanaka Japan Check_Fraud 27000 2018 Forged_Cheques Yes 2
11 11 Ahmed_Khan Pakistan Online_Scam 34000 2021 Fake_Websites No .
12 12 Maria_Rojas Brazil Money_Laundering 470000 2020 Shell_Companies Yes 8
13 13 Bongani_Ndlovu South_Africa Phishing 59000 2019 Email_Spoofing Yes 3
14 14 Emily_Wilson Canada Identity_Theft 88000 2022 Fake_Documents No .
15 15 Elena_Popov Ukraine Card_Cloning 77000 2021 ATM_Hack Yes 5
16 16 Andrew_Blake Australia Loan_Scam 66000 2020 Fake_Credit No .
17 17 Hassan_Musa Nigeria Investment_Fraud 92000 2021 Ponzi_Scheme No .
18 18 Ingrid_Andersson Sweden Online_Scam 58000 2019 Fake_Ecommerce Yes 2
19 19 Gopal_Sharma India SIM_Swap 69000 2022 Mobile_Fraud Yes 4
20 20 Tommy_Nguyen Vietnam Crypto_Scam 230000 2021 Rug_Pull No .


STEP 2: PROC PRINT

proc print data=Bank_Fraudsters;

    title "Complete Bank Fraudsters Dataset";

run;

Output:

Complete Bank Fraudsters Dataset

Obs Fraud_ID Fraudster_Name Country Fraud_Type Amount_Lost_USD Year Method Caught Penalty_Years
1 1 John_Doe USA Identity_Theft 125000 2019 Online_Yield Yes 5
2 2 Raj_Patel India Card_Cloning 84000 2020 ATM_Hack No .
3 3 Mei_Wang China Money_Laundering 430000 2021 Shell_Companies Yes 10
4 4 Carlos_Soto Mexico Loan_Scam 59000 2018 Fake_Records Yes 3
5 5 Fatima_Ali UAE Phishing 72000 2022 Email_Spoofing No .
6 6 Ivan_Kuznetsov Russia Crypto_Scam 205000 2021 Blockchain_Fraud No .
7 7 Jean_Martin France Investment_Fraud 110000 2020 Ponzi_Scheme Yes 6
8 8 Linda_Kim South_Korea Skimming 63000 2019 ATM_Device Yes 4
9 9 George_Smith UK Account_Takeover 95000 2022 SIM_Swap No .
10 10 Akira_Tanaka Japan Check_Fraud 27000 2018 Forged_Cheques Yes 2
11 11 Ahmed_Khan Pakistan Online_Scam 34000 2021 Fake_Websites No .
12 12 Maria_Rojas Brazil Money_Laundering 470000 2020 Shell_Companies Yes 8
13 13 Bongani_Ndlovu South_Africa Phishing 59000 2019 Email_Spoofing Yes 3
14 14 Emily_Wilson Canada Identity_Theft 88000 2022 Fake_Documents No .
15 15 Elena_Popov Ukraine Card_Cloning 77000 2021 ATM_Hack Yes 5
16 16 Andrew_Blake Australia Loan_Scam 66000 2020 Fake_Credit No .
17 17 Hassan_Musa Nigeria Investment_Fraud 92000 2021 Ponzi_Scheme No .
18 18 Ingrid_Andersson Sweden Online_Scam 58000 2019 Fake_Ecommerce Yes 2
19 19 Gopal_Sharma India SIM_Swap 69000 2022 Mobile_Fraud Yes 4
20 20 Tommy_Nguyen Vietnam Crypto_Scam 230000 2021 Rug_Pull No .


STEP 3: PROC MEANS

proc means data=Bank_Fraudsters mean sum min max maxdec=2;

    var Amount_Lost_USD Penalty_Years;

    title "Descriptive Statistics of Financial Loss and Penalties";

run;

Output:

Descriptive Statistics of Financial Loss and Penalties

The MEANS Procedure

Variable Mean Sum Minimum Maximum
Amount_Lost_USD
Penalty_Years
125650.00
4.73
2513000.00
52.00
27000.00
2.00
470000.00
10.00


STEP 4: PROC FREQ

1. By Country:

proc freq data=Bank_Fraudsters;

    tables Country / nocum nopercent;

    title "Fraud Cases by Country";

run;

Output:

Fraud Cases by Country

The FREQ Procedure

Country Frequency
Australia 1
Brazil 1
Canada 1
China 1
France 1
India 2
Japan 1
Mexico 1
Nigeria 1
Pakistan 1
Russia 1
South_Africa 1
South_Korea 1
Sweden 1
UAE 1
UK 1
USA 1
Ukraine 1
Vietnam 1

2. By Fraud Type:

proc freq data=Bank_Fraudsters;

    tables Fraud_Type;

    title "Frequency of Fraud Types";

run;

Output:

Frequency of Fraud Types

The FREQ Procedure

Fraud_Type Frequency Percent Cumulative
Frequency		 Cumulative
Percent
Account_Takeover 1 5.00 1 5.00
Card_Cloning 2 10.00 3 15.00
Check_Fraud 1 5.00 4 20.00
Crypto_Scam 2 10.00 6 30.00
Identity_Theft 2 10.00 8 40.00
Investment_Fraud 2 10.00 10 50.00
Loan_Scam 2 10.00 12 60.00
Money_Laundering 2 10.00 14 70.00
Online_Scam 2 10.00 16 80.00
Phishing 2 10.00 18 90.00
SIM_Swap 1 5.00 19 95.00
Skimming 1 5.00 20 100.00

3. Caught vs Not Caught:

proc freq data=Bank_Fraudsters;

    tables Caught;

    title "Caught vs Not Caught Fraudsters";

run;

Output:

Caught vs Not Caught Fraudsters

The FREQ Procedure

Caught Frequency Percent Cumulative
Frequency		 Cumulative
Percent
No 9 45.00 9 45.00
Yes 11 55.00 20 100.00

STEP 5: PROC SQL ANALYSIS

1. Top 5 Highest Losses

proc sql outobs=5;

    title "Top 5 Fraud Cases by Amount Lost";

    select Fraudster_Name, Country, Fraud_Type, Amount_Lost_USD

    from Bank_Fraudsters

    order by Amount_Lost_USD desc;

quit;

Output:

Top 5 Fraud Cases by Amount Lost

Fraudster_Name Country Fraud_Type Amount_Lost_USD
Maria_Rojas Brazil Money_Laundering 470000
Mei_Wang China Money_Laundering 430000
Tommy_Nguyen Vietnam Crypto_Scam 230000
Ivan_Kuznetsov Russia Crypto_Scam 205000
John_Doe USA Identity_Theft 125000

2. Average Penalty for Caught Fraudsters

proc sql;

    title "Average Penalty (in Years) for Caught Fraudsters";

    select avg(Penalty_Years) as Avg_Penalty_Years

    from Bank_Fraudsters

    where Caught = 'Yes';

quit;

Output:

Average Penalty (in Years) for Caught Fraudsters

Avg_Penalty_Years
4.727273


3. Total Fraud Amount by Fraud Type

proc sql;

    title "Total Amount Lost by Fraud Type";

    select Fraud_Type, sum(Amount_Lost_USD) as Total_Loss format=dollar12.

    from Bank_Fraudsters

    group by Fraud_Type;

quit;

Output:

Total Amount Lost by Fraud Type

Fraud_Type Total_Loss
Account_Takeover $95,000
Card_Cloning $161,000
Check_Fraud $27,000
Crypto_Scam $435,000
Identity_Theft $213,000
Investment_Fraud $202,000
Loan_Scam $125,000
Money_Laundering $900,000
Online_Scam $92,000
Phishing $131,000
SIM_Swap $69,000
Skimming $63,000

STEP 6: USING MACROS FOR REPEATABLE ANALYSIS

%macro CountryFraud(countryname);

    title "Fraud Summary for &countryname.";

    proc sql;

        select Fraudster_Name, Fraud_Type, Amount_Lost_USD, Caught, Penalty_Years

        from Bank_Fraudsters

        where Country = "&countryname.";

    quit;

%mend;


%CountryFraud(India);

Output:

Fraud Summary for India

Fraudster_Name Fraud_Type Amount_Lost_USD Caught Penalty_Years
Raj_Patel Card_Cloning 84000 No .
Gopal_Sharma SIM_Swap 69000 Yes 4


%CountryFraud(USA);

Output:

Fraud Summary for USA

Fraudster_Name Fraud_Type Amount_Lost_USD Caught Penalty_Years
John_Doe Identity_Theft 125000 Yes 5


 Automate fraud type statistics:

 %macro FraudTypeStats(type);

    title "Statistics for &type. Fraud Type";

    proc sql;

        select count(*) as Total_Cases, avg(Amount_Lost_USD) as Avg_Loss

        from Bank_Fraudsters

        where Fraud_Type = "&type.";

    quit;

%mend;


%FraudTypeStats(Phishing);

Output:

Statistics for Phishing Fraud Type

Total_Cases Avg_Loss
2 65500


%FraudTypeStats(Money_Laundering);

Output:

Statistics for Money_Laundering Fraud Type

Total_Cases Avg_Loss
2 450000


STEP 7: CONDITIONAL DERIVED COLUMN

data Bank_Fraudsters_Categorized;

    set Bank_Fraudsters;

    length Risk_Level $10;

    if Amount_Lost_USD >= 200000 then Risk_Level = 'High';

    else if Amount_Lost_USD >= 75000 then Risk_Level = 'Medium';

    else Risk_Level = 'Low';

run;

proc print;run;

Output:

Obs Fraud_ID Fraudster_Name Country Fraud_Type Amount_Lost_USD Year Method Caught Penalty_Years Risk_Level
1 1 John_Doe USA Identity_Theft 125000 2019 Online_Yield Yes 5 Medium
2 2 Raj_Patel India Card_Cloning 84000 2020 ATM_Hack No . Medium
3 3 Mei_Wang China Money_Laundering 430000 2021 Shell_Companies Yes 10 High
4 4 Carlos_Soto Mexico Loan_Scam 59000 2018 Fake_Records Yes 3 Low
5 5 Fatima_Ali UAE Phishing 72000 2022 Email_Spoofing No . Low
6 6 Ivan_Kuznetsov Russia Crypto_Scam 205000 2021 Blockchain_Fraud No . High
7 7 Jean_Martin France Investment_Fraud 110000 2020 Ponzi_Scheme Yes 6 Medium
8 8 Linda_Kim South_Korea Skimming 63000 2019 ATM_Device Yes 4 Low
9 9 George_Smith UK Account_Takeover 95000 2022 SIM_Swap No . Medium
10 10 Akira_Tanaka Japan Check_Fraud 27000 2018 Forged_Cheques Yes 2 Low
11 11 Ahmed_Khan Pakistan Online_Scam 34000 2021 Fake_Websites No . Low
12 12 Maria_Rojas Brazil Money_Laundering 470000 2020 Shell_Companies Yes 8 High
13 13 Bongani_Ndlovu South_Africa Phishing 59000 2019 Email_Spoofing Yes 3 Low
14 14 Emily_Wilson Canada Identity_Theft 88000 2022 Fake_Documents No . Medium
15 15 Elena_Popov Ukraine Card_Cloning 77000 2021 ATM_Hack Yes 5 Medium
16 16 Andrew_Blake Australia Loan_Scam 66000 2020 Fake_Credit No . Low
17 17 Hassan_Musa Nigeria Investment_Fraud 92000 2021 Ponzi_Scheme No . Medium
18 18 Ingrid_Andersson Sweden Online_Scam 58000 2019 Fake_Ecommerce Yes 2 Low
19 19 Gopal_Sharma India SIM_Swap 69000 2022 Mobile_Fraud Yes 4 Low
20 20 Tommy_Nguyen Vietnam Crypto_Scam 230000 2021 Rug_Pull No . High


proc freq data=Bank_Fraudsters_Categorized;

    tables Risk_Level;

    title "Fraud Risk Level Categorization";

run;

Output:

Fraud Risk Level Categorization

The FREQ Procedure

Risk_Level Frequency Percent Cumulative
Frequency		 Cumulative
Percent
High 4 20.00 4 20.00
Low 9 45.00 13 65.00
Medium 7 35.00 20 100.00

STEP 8: VISUALIZATION 

proc sgplot data=Bank_Fraudsters;

    vbar Fraud_Type / response=Amount_Lost_USD stat=sum;

    title "Total Loss by Fraud Type";

run;

Log:
NOTE: PROCEDURE SGPLOT used (Total process time):
      real time           3.23 seconds
      cpu time            0.57 seconds

NOTE: Listing image output written to SGPlot1.png.
NOTE: There were 20 observations read from the data set WORK.BANK_FRAUDSTERS.


To Visit My Previous E-Commerce Dataset:Click Here
To Visit My Previous Length,Input,Retain Statements:Click Here
To Visit My Previous Urban Traffic Dataset:Click Here
To Visit My Previous Home Energy Consumption Dataset:Click Here





--->PLEASE FOLLOW OUR BLOG FOR MORE INFORMATION.
--->PLEASE DO COMMENTS AND SHARE OUR BLOG.

PLEASE FOLLOW OUR TELEGRAM CHANNEL CLICK HERE

PLEASE FOLLOW OUR FACEBOOK PAGE  CLICK HERE

PLEASE FOLLOW OUR INSTAGRAM PAGE CLICK HERE






Comments

Post a Comment

Popular posts from this blog

409.Can We Build a Reliable Emergency Services Analytics & Fraud Detection System in SAS While Identifying and Fixing Intentional Errors?

-
Image
Building a Reliable Emergency Services Analytics & Fraud Detection System in SAS Through Systematic Error Identification and Correction -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- HERE WE USED THESE SAS STATEMENTS AND FUNCTIONS FOR THIS PROJECT: DATA STEP | SET | INPUT | DATALINES | LENGTH | FORMAT | IF-THEN-ELSE | MDY | INTCK | INTNX | STRIP | TRIM | CAT | CATX | PROPCASE | UPCASE | LOWCASE | COALESCE | MERGE | PROC SQL | PROC MEANS | PROC UNIVARIATE | PROC FREQ | PROC CORR | PROC SGPLOT | PROC TRANSPOSE | PROC APPEND | PROC DATASETS DELETE | %MACRO / %MEND -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- INTRODUCTION Table of Con...
Read more

397.If a satellite has excellent signal strength but very high latency, can it still deliver good quality communication? Why or why not?A Sas Study

-
Image
If a satellite has excellent signal strength but very high latency, can it still deliver good quality communication? Why or why not?A Sas Study -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS — DATA STEP | PROC SQL |  PROC PRINT | PROC SGPLOT | MACROS | PROC CORR | PROC MEANS | PROC FREQ | PROC UNIVARIATE | SET | APPEND | PROC TRANSPOSE | PROC DATASETS DELETE | DATA FUNCTIONS -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- INTRODUCTION Satellite communication is a backbone of modern connectivity—supporting  GPS, TV broadcasting, military operations, disaster management, banking, a...
Read more

401.How Efficient Are Global Data Centers? A Complete SAS Analytics Study

-
Image
How Efficient Are Global Data Centers? A Complete SAS Analytics Study -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS — DATA STEP | PROC SQL |  PROC PRINT | PROC SGPLOT | MACROS | PROC CORR | PROC MEANS | PROC FREQ | PROC UNIVARIATE | APPEND PROC TRANSPOSE | PROC DATASETS DELETE | DATA FUNCTIONS -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 1. INTRODUCTION Modern data centers are the backbone of cloud computing, AI, banking, healthcare, and government systems. They consume massive power, require efficient cooling, and must ensure zero downtime . This project simulates global data c...
Read more