Digital Identity Verification Fraud Detection System Using SAS

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | SET | MERGE | PROC SORT | PROC SQL | PROC APPEND | PROC TRANSPOSE | PROC DATASETS | DELETE | INPUT | DATALINES | LENGTH | FORMAT | IF-THEN-ELSE | ABS | ROUND | COALESCE | STRIP | TRIM | CAT | CATX | PROPCASE | UPCASE | LOWCASE | MDY | INTCK | INTNX | TODAY | PROC PRINT | RUN | QUIT | %MACRO | %MEND

Introduction

In today’s digital economy, identity verification systems play a critical role in banking, fintech, healthcare, e-commerce, and government services. Every time a user uploads an Aadhaar card, passport, selfie, or biometric scan, a backend system validates authenticity. However, digital identity systems are vulnerable to fraud attempts such as spoofing, synthetic identity creation, and document tampering.

In this project, we will:

Create a Digital Identity Verification Dataset
Introduce intentional errors
Detect and correct those errors
Apply macros for fraud detection logic
Use date functions (MDY, INTCK, INTNX)
Apply APPEND, TRANSPOSE, SET, MERGE
Use numeric and character functions
Demonstrate PROC DATASETS DELETE
Implement Utilization Classification
Apply Percentage Calculations

Table Of Contents

Business Context
Dataset Structure
Raw Dataset Creation With Intentional Errors
Error Identification
Corrected Dataset Creation
Date Handling Using MDY, INTCK, INTNX
Fraud Detection Macro
Utilization Classification Logic
SET vs MERGE vs APPEND
PROC TRANSPOSE
Numeric Functions
Character Functions
COALESCE Usage
PROC DATASETS DELETE
Final Fraud Detection Output
15 Key Points About This Project
Benefits
Summary
Conclusion

Business Context

Imagine you are working as a SAS Programmer in a fintech company in Hyderabad. The company processes:

Aadhaar eKYC
Biometric verification
Video KYC
OTP-based authentication

Your role is to:

Identify high-risk verification attempts
Calculate fraud attempt percentages
Track processing delays
Classify system utilization
Correct dirty data
Automate fraud detection using macros

1. Raw Dataset Creation (With Intentional Errors)

title "Raw Digital Identity Verification Data With Errors";

data digital_identity_raw;

input Verification_ID $ Method:$12. Processing_Time Failure_Rate Fraud_Attempts

Confidence_Score Outcome $ Month $ Day Year;

format Verification_Date date9.;

Verification_Date = mdy(Month, Day, Year);

datalines;

V001 Aadhaar 35 0.02 1 95 Approved 01 15 2025

V002 biometric 50 0.10 3 88 Approved 02 10 2025

V003 OTP 20 . 0 90 Rejected 03 05 2025

V004 aadhaar 70 0.30 5 60 Rejected 13 20 2025

V005 VideoKYC -15 0.05 2 85 Approved 04 25 2025

V006 OTP 30 0.01 0 98 approved 05 11 2025

V007 Biometric 45 0.20 4 75 Rejected 06 18 2025

V008 Aadhaar 60 0.25 6 55 Rejected 07 21 2025

V009 VideoKYC 40 0.03 1 92 Approved 08 09 2025

V010 OTP 25 0.00 0 99 Approved 09 13 2025

V011 Aadhaar 90 0.50 8 40 Rejected 10 05 2025

V012 Biometric 55 0.15 3 80 Approved 11 22 2025

V013 OTP 15 0.02 0 96 Approved 12 02 2025

V014 Aadhaar 100 0.60 9 35 Rejected 01 17 2026

V015 VideoKYC 48 0.07 2 89 Approved 02 28 2026

;

run;

proc print data=digital_identity_raw;

run;

title;

OUTPUT:

Raw Digital Identity Verification Data With Errors


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date
1	V001	Aadhaar	35	0.02	1	95	Approved	01	15	2025	15JAN2025
2	V002	biometric	50	0.10	3	88	Approved	02	10	2025	10FEB2025
3	V003	OTP	20	.	0	90	Rejected	03	5	2025	05MAR2025
4	V004	aadhaar	70	0.30	5	60	Rejected	13	20	2025	.
5	V005	VideoKYC	-15	0.05	2	85	Approved	04	25	2025	25APR2025
6	V006	OTP	30	0.01	0	98	approved	05	11	2025	11MAY2025
7	V007	Biometric	45	0.20	4	75	Rejected	06	18	2025	18JUN2025
8	V008	Aadhaar	60	0.25	6	55	Rejected	07	21	2025	21JUL2025
9	V009	VideoKYC	40	0.03	1	92	Approved	08	9	2025	09AUG2025
10	V010	OTP	25	0.00	0	99	Approved	09	13	2025	13SEP2025
11	V011	Aadhaar	90	0.50	8	40	Rejected	10	5	2025	05OCT2025
12	V012	Biometric	55	0.15	3	80	Approved	11	22	2025	22NOV2025
13	V013	OTP	15	0.02	0	96	Approved	12	2	2025	02DEC2025
14	V014	Aadhaar	100	0.60	9	35	Rejected	01	17	2026	17JAN2026
15	V015	VideoKYC	48	0.07	2	89	Approved	02	28	2026	28FEB2026

❗ Intentional Errors

· Month = 13 (Invalid date)

· Negative Processing_Time

· Missing Failure_Rate

· Mixed case in Method

· Mixed case in Outcome

· Inconsistent naming: Aadhaar vs aadhaar

2. Corrected Dataset Creation

title "Corrected Digital Identity Dataset";

data digital_identity_clean;

set digital_identity_raw;

Method = propcase(strip(Method));

Outcome = upcase(strip(Outcome));

if Processing_Time < 0 then Processing_Time = abs(Processing_Time);

if Failure_Rate = . then Failure_Rate = 0;

if Month > 12 then Month = 12

Verification_Date = mdy(Month, Day, Year);

Fraud_Percentage = (Fraud_Attempts / 10) * 100;

run;

proc print data=digital_identity_clean;

run;

title;

OUTPUT:

Corrected Digital Identity Dataset


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10
2	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30
3	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0
4	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50
5	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20
6	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40
8	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60
9	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10
10	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0
11	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80
12	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30
13	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0
14	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20

Code Explanation

SET

· Reads dataset into new dataset

· Used for transformation

PROPCASE

· Converts Aadhaar → Aadhaar (proper case)

STRIP

· Removes leading/trailing spaces

UPCASE

· Standardizes Approved vs approved

ABS

· Converts negative to positive

MDY

· Creates SAS date value

3. Date Calculations Using INTCK and INTNX

data digital_identity_dates;

set digital_identity_clean;

Today_Date = today();

Days_Since_Verification = intck('day', Verification_Date, Today_Date);

Next_Review_Date = intnx('month', Verification_Date, 3, 'same');

format Today_Date Next_Review_Date date9.;

run;

proc print data=digital_identity_dates;

run;

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025
2	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025
3	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025
4	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026
5	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025
6	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025
8	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025
9	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025
10	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025
11	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026
12	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026
13	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026
14	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026

INTCK

Calculates difference between two dates.

INTNX

Moves date forward by interval.

4. Utilization Classification

data digital_identity_utilization;

set digital_identity_dates;

length Utilization_Class $12.;

if Processing_Time <= 30 then Utilization_Class="Fast";

else if Processing_Time <= 60 then Utilization_Class="Moderate";

else Utilization_Class="Slow";

run;

proc print data=digital_identity_utilization;

run;

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate
2	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate
3	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast
4	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow
5	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast
6	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate
8	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate
9	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate
10	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast
11	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow
12	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate
13	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast
14	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate

5. Fraud Detection Macro

%macro fraud_logic(input=, output=);

data &output;

set &input;

if Fraud_Attempts >= 5 and Confidence_Score < 70 then Fraud_Flag=1;

else if Failure_Rate > 0.25 then Fraud_Flag=1;

else Fraud_Flag=0;

run;

proc print data=&output;

run;

%mend;

%fraud_logic(input=digital_identity_utilization,

output=digital_identity_fraud);

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class	Fraud_Flag
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate	0
2	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate	0
3	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast	0
4	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow	1
5	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast	0
6	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast	0
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate	0
8	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate	1
9	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate	0
10	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast	0
11	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow	1
12	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate	0
13	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast	0
14	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow	1
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate	0

· Reusable

· Parameter driven

· Scalable for multiple datasets

6. Using MERGE

data method_risk;

input Method:$12. Risk_Level $;

datalines;

Aadhaar Medium

Biometric High

Otp Low

Videokyc Medium

;

run;

proc print data=method_risk;

run;

OUTPUT:


Obs	Method	Risk_Level
1	Aadhaar	Medium
2	Biometric	High
3	Otp	Low
4	Videokyc	Medium

proc sort data=digital_identity_fraud;

by Method;

run;

proc print data=digital_identity_fraud;

run;

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class	Fraud_Flag
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate	0
2	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow	1
3	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate	1
4	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow	1
5	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow	1
6	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate	0
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate	0
8	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate	0
9	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast	0
10	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast	0
11	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast	0
12	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast	0
13	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast	0
14	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate	0
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate	0

proc sort data=method_risk;

by Method;

run;

proc print data=method_risk;

run;

OUTPUT:


Obs	Method	Risk_Level
1	Aadhaar	Medium
2	Biometric	High
3	Otp	Low
4	Videokyc	Medium

data digital_identity_merged;

merge digital_identity_fraud method_risk;

by Method;

run;

proc print data=digital_identity_merged;

run;

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class	Fraud_Flag	Risk_Level
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate	0	Medium
2	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow	1	Medium
3	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate	1	Medium
4	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow	1	Medium
5	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow	1	Medium
6		Biometric	.	.	.	.			.	.	.	.	.	.	.		.	High
7	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate	0
8	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate	0
9	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate	0
10	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast	0	Low
11	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast	0	Low
12	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast	0	Low
13	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast	0	Low
14	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast	0	Medium
15	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate	0	Medium
16	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate	0	Medium

MERGE

Combines datasets horizontally.

7. Using Same Datasets For APPEND

proc append base=digital_identity_merged

data=digital_identity_merged force;

run;

proc print data=digital_identity_merged;

run;

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class	Fraud_Flag	Risk_Level
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate	0	Medium
2	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow	1	Medium
3	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate	1	Medium
4	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow	1	Medium
5	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow	1	Medium
6		Biometri	.	.	.	.			.	.	.	.	.	.	.		.	High
7	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate	0
8	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate	0
9	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate	0
10	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast	0	Low
11	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast	0	Low
12	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast	0	Low
13	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast	0	Low
14	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast	0	Medium
15	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate	0	Medium
16	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate	0	Medium
17	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate	0	Medium
18	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow	1	Medium
19	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate	1	Medium
20	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow	1	Medium
21	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow	1	Medium
22		Biometri	.	.	.	.			.	.	.	.	.	.	.		.	High
23	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate	0
24	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate	0
25	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate	0
26	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast	0	Low
27	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast	0	Low
28	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast	0	Low
29	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast	0	Low
30	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast	0	Medium
31	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate	0	Medium
32	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate	0	Medium

· You are appending the dataset to itself

· It will duplicate all observations

· It is logically incorrect in a real project

· It may cause unintended data inflation

8. Append Different Datasets

Step 1: Create New Dataset

title "New Incoming Digital Identity Records";

data digital_identity_new;

input Verification_ID $ Method:$12. Processing_Time Failure_Rate

Fraud_Attempts Confidence_Score Outcome $ Month Day Year;

format Verification_Date date9.;

Verification_Date = mdy(Month, Day, Year);

datalines;

V016 Aadhaar 42 0.04 1 93 Approved 03 10 2026

V017 Biometric 75 0.35 6 58 Rejected 04 15 2026

V018 OTP 28 0.01 0 97 Approved 05 21 2026

;

run;

proc print data=digital_identity_new;

run;

title;

OUTPUT:

New Incoming Digital Identity Records


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date
1	V016	Aadhaar	42	0.04	1	93	Approved	3	10	2026	10MAR2026
2	V017	Biometric	75	0.35	6	58	Rejected	4	15	2026	15APR2026
3	V018	OTP	28	0.01	0	97	Approved	5	21	2026	21MAY2026

Step 2: Standardize Structure Before Append

data digital_identity_new_clean;

set digital_identity_new;

Method = propcase(strip(Method));

Outcome = upcase(strip(Outcome));

if Processing_Time < 0 then Processing_Time = abs(Processing_Time);

if Failure_Rate = . then Failure_Rate = 0;

Fraud_Percentage = (Fraud_Attempts/10)*100;

run;

proc print data=digital_identity_new_clean;

run;

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage
1	V016	Aadhaar	42	0.04	1	93	APPROVED	3	10	2026	10MAR2026	10
2	V017	Biometric	75	0.35	6	58	REJECTED	4	15	2026	15APR2026	60
3	V018	Otp	28	0.01	0	97	APPROVED	5	21	2026	21MAY2026	0

· Before APPEND, variable structure must match.

Step 3: Correct PROC APPEND Usage

title "Appending New Records To Main Dataset";

proc append base=digital_identity_merged

data=digital_identity_new_clean

force;

run;

proc print data=digital_identity_merged;

run;

OUTPUT:

Appending New Records To Main Dataset


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class	Fraud_Flag	Risk_Level
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate	0	Medium
2	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow	1	Medium
3	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate	1	Medium
4	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow	1	Medium
5	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow	1	Medium
6	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate	0	High
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate	0	High
8	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate	0	High
9	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast	0	Low
10	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast	0	Low
11	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast	0	Low
12	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast	0	Low
13	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast	0	Medium
14	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate	0	Medium
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate	0	Medium
16	V016	Aadhaar	42	0.04	1	93	APPROVED		10	2026	10MAR2026	10	.	.	.		.
17	V017	Biometric	75	0.35	6	58	REJECTED		15	2026	15APR2026	60	.	.	.		.
18	V018	Otp	28	0.01	0	97	APPROVED		21	2026	21MAY2026	0	.	.	.		.

BASE

· Target dataset

· Existing master dataset

DATA

· New dataset to be added

FORCE

· Allows append even if:

· Variable lengths differ

· Minor attribute mismatch

· Should be used carefully in production

9. TRANSPOSE

proc transpose data=digital_identity_merged out=transposed_data;

var Processing_Time Fraud_Attempts Confidence_Score;

run;

proc print data=transposed_data;

run;

OUTPUT:


Obs	_NAME_	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16	COL17	COL18
1	Processing_Time	35	70	60	90	100	50	45	55	20	30	25	15	15	40	48	42	75	28
2	Fraud_Attempts	1	5	6	8	9	3	4	3	0	0	0	0	2	1	2	1	6	0
3	Confidence_Score	95	60	55	40	35	88	75	80	90	98	99	96	85	92	89	93	58	97

10. Numeric Functions Used

data numeric_example;

set digital_identity_merged;

Rounded_Score = round(Confidence_Score);

Total_Risk = sum(Fraud_Attempts, Failure_Rate*100);

run;

proc print data=numeric_example;

run;

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class	Fraud_Flag	Risk_Level	Rounded_Score	Total_Risk
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate	0	Medium	95	3
2	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow	1	Medium	60	35
3	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate	1	Medium	55	31
4	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow	1	Medium	40	58
5	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow	1	Medium	35	69
6	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate	0	High	88	13
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate	0	High	75	24
8	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate	0	High	80	18
9	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast	0	Low	90	0
10	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast	0	Low	98	1
11	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast	0	Low	99	0
12	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast	0	Low	96	2
13	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast	0	Medium	85	7
14	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate	0	Medium	92	4
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate	0	Medium	89	9
16	V016	Aadhaar	42	0.04	1	93	APPROVED		10	2026	10MAR2026	10	.	.	.		.		93	5
17	V017	Biometric	75	0.35	6	58	REJECTED		15	2026	15APR2026	60	.	.	.		.		58	41
18	V018	Otp	28	0.01	0	97	APPROVED		21	2026	21MAY2026	0	.	.	.		.		97	1

11. Character Functions Used

data char_example;

set digital_identity_merged;

Full_Info = catx("-", Verification_ID, Method, Outcome);

Lower_Method = lowcase(Method);

Trimmed_Method = trim(Method);

Clean_Method = strip(Method);

run;

proc print data=char_example;

run;

OUTPUT:


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Fraud_Percentage	Today_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class	Fraud_Flag	Risk_Level	Full_Info	Lower_Method	Trimmed_Method	Clean_Method
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	10	27FEB2026	408	15APR2025	Moderate	0	Medium	V001-Aadhaar-APPROVED	aadhaar	Aadhaar	Aadhaar
2	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	50	27FEB2026	69	20MAR2026	Slow	1	Medium	V004-Aadhaar-REJECTED	aadhaar	Aadhaar	Aadhaar
3	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	60	27FEB2026	221	21OCT2025	Moderate	1	Medium	V008-Aadhaar-REJECTED	aadhaar	Aadhaar	Aadhaar
4	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	80	27FEB2026	145	05JAN2026	Slow	1	Medium	V011-Aadhaar-REJECTED	aadhaar	Aadhaar	Aadhaar
5	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	90	27FEB2026	41	17APR2026	Slow	1	Medium	V014-Aadhaar-REJECTED	aadhaar	Aadhaar	Aadhaar
6	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	30	27FEB2026	382	10MAY2025	Moderate	0	High	V002-Biometric-APPROVED	biometric	Biometric	Biometric
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	40	27FEB2026	254	18SEP2025	Moderate	0	High	V007-Biometric-REJECTED	biometric	Biometric	Biometric
8	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	30	27FEB2026	97	22FEB2026	Moderate	0	High	V012-Biometric-APPROVED	biometric	Biometric	Biometric
9	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	0	27FEB2026	359	05JUN2025	Fast	0	Low	V003-Otp-REJECTED	otp	Otp	Otp
10	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	0	27FEB2026	292	11AUG2025	Fast	0	Low	V006-Otp-APPROVED	otp	Otp	Otp
11	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	0	27FEB2026	167	13DEC2025	Fast	0	Low	V010-Otp-APPROVED	otp	Otp	Otp
12	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	0	27FEB2026	87	02MAR2026	Fast	0	Low	V013-Otp-APPROVED	otp	Otp	Otp
13	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	20	27FEB2026	308	25JUL2025	Fast	0	Medium	V005-Videokyc-APPROVED	videokyc	Videokyc	Videokyc
14	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	10	27FEB2026	202	09NOV2025	Moderate	0	Medium	V009-Videokyc-APPROVED	videokyc	Videokyc	Videokyc
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	20	27FEB2026	-1	28MAY2026	Moderate	0	Medium	V015-Videokyc-APPROVED	videokyc	Videokyc	Videokyc
16	V016	Aadhaar	42	0.04	1	93	APPROVED		10	2026	10MAR2026	10	.	.	.		.		V016-Aadhaar-APPROVED	aadhaar	Aadhaar	Aadhaar
17	V017	Biometric	75	0.35	6	58	REJECTED		15	2026	15APR2026	60	.	.	.		.		V017-Biometric-REJECTED	biometric	Biometric	Biometric
18	V018	Otp	28	0.01	0	97	APPROVED		21	2026	21MAY2026	0	.	.	.		.		V018-Otp-APPROVED	otp	Otp	Otp

CAT

Concatenates without separator

CATX

Concatenates with separator

12. PROC DATASETS DELETE

proc datasets library=work nolist;

delete digital_identity_raw;

quit;

LOG:

 NOTE: Deleting WORK.DIGITAL_IDENTITY_RAW (memtype=DATA).

· Used to clean temporary datasets.

13. Final Corrected Full Code

title "Final Cleaned Digital Identity Fraud Detection System";

data digital_identity_final;

set digital_identity_raw;

Method = propcase(strip(Method));

Outcome = upcase(strip(Outcome));

if Processing_Time < 0 then Processing_Time = abs(Processing_Time);

if Failure_Rate = . then Failure_Rate = 0;

if Month > 12 then Month = 12;

Verification_Date = mdy(Month, Day, Year);

Days_Since_Verification = intck('day', Verification_Date, today());

Next_Review_Date = intnx('month', Verification_Date, 3, 'same');

if Processing_Time <= 30 then Utilization_Class="Fast";

else if Processing_Time <= 60 then Utilization_Class="Moderate";

else Utilization_Class="Slow";

if Fraud_Attempts >= 5 and Confidence_Score < 70 then Fraud_Flag=1;

else if Failure_Rate > 0.25 then Fraud_Flag=1;

else Fraud_Flag=0;

format Verification_Date Next_Review_Date date9.;

run;

proc print data=digital_identity_final;

run;

OUTPUT:

Final Cleaned Digital Identity Fraud Detection System


Obs	Verification_ID	Method	Processing_Time	Failure_Rate	Fraud_Attempts	Confidence_Score	Outcome	Month	Day	Year	Verification_Date	Days_Since_Verification	Next_Review_Date	Utilization_Class	Fraud_Flag
1	V001	Aadhaar	35	0.02	1	95	APPROVED	01	15	2025	15JAN2025	408	15APR2025	Mode	0
2	V002	Biometric	50	0.10	3	88	APPROVED	02	10	2025	10FEB2025	382	10MAY2025	Mode	0
3	V003	Otp	20	0.00	0	90	REJECTED	03	5	2025	05MAR2025	359	05JUN2025	Fast	0
4	V004	Aadhaar	70	0.30	5	60	REJECTED	12	20	2025	20DEC2025	69	20MAR2026	Slow	1
5	V005	Videokyc	15	0.05	2	85	APPROVED	04	25	2025	25APR2025	308	25JUL2025	Fast	0
6	V006	Otp	30	0.01	0	98	APPROVED	05	11	2025	11MAY2025	292	11AUG2025	Fast	0
7	V007	Biometric	45	0.20	4	75	REJECTED	06	18	2025	18JUN2025	254	18SEP2025	Mode	0
8	V008	Aadhaar	60	0.25	6	55	REJECTED	07	21	2025	21JUL2025	221	21OCT2025	Mode	1
9	V009	Videokyc	40	0.03	1	92	APPROVED	08	9	2025	09AUG2025	202	09NOV2025	Mode	0
10	V010	Otp	25	0.00	0	99	APPROVED	09	13	2025	13SEP2025	167	13DEC2025	Fast	0
11	V011	Aadhaar	90	0.50	8	40	REJECTED	10	5	2025	05OCT2025	145	05JAN2026	Slow	1
12	V012	Biometric	55	0.15	3	80	APPROVED	11	22	2025	22NOV2025	97	22FEB2026	Mode	0
13	V013	Otp	15	0.02	0	96	APPROVED	12	2	2025	02DEC2025	87	02MAR2026	Fast	0
14	V014	Aadhaar	100	0.60	9	35	REJECTED	01	17	2026	17JAN2026	41	17APR2026	Slow	1
15	V015	Videokyc	48	0.07	2	89	APPROVED	02	28	2026	28FEB2026	-1	28MAY2026	Mode	0

15 Key Points About This Project:

1.This project simulates a real-world digital identity verification system used in fintech and banking environments.

2.A structured dataset with 15+ observations was created including verification, fraud, performance, and date variables.

3.Intentional data quality errors were introduced to mimic real production data issues.

4. The SET statement was used to read and transform raw datasets during cleaning.

5.Character functions like PROPCASE, UPCASE, LOWCASE, STRIP, and TRIM were used for standardization.

6.Numeric functions like ABS, SUM, ROUND, and INT were used to correct and derive numeric values.

7.The MDY function was used to construct proper SAS date values from separate month, day, and year fields.

8.INTCK was applied to calculate date differences such as days since verification.

9.INTNX was used to generate future review dates based on verification timelines.

10.Utilization classification was created based on processing time thresholds (Fast, Moderate, Slow).

11. Fraud detection logic was implemented using business rules combining Fraud_Attempts, Failure_Rate, and Confidence_Score.

12.A reusable macro was built to automate fraud detection logic across datasets.

13.MERGE, SET, and PROC APPEND were used to combine datasets based on business requirements.

14.PROC TRANSPOSE was applied to reshape data for reporting and dashboard purposes.

15.PROC DATASETS DELETE was used to remove temporary datasets and maintain a clean working environment.

Benefits:

1.Improves Fraud Detection
It helps identify suspicious verification attempts early, reducing financial loss and identity misuse.

2.Improves Data Quality
By fixing errors like missing values, wrong dates, and inconsistent text, the system becomes more reliable and accurate.

3.Automates Fraud Logic
Using macros, fraud detection rules can be reused and applied automatically to new incoming data.

4.Enhances Operational Efficiency
Utilization classification (Fast, Moderate, Slow) helps measure processing performance and improve turnaround time.

5.Supports Business Decision Making
Fraud percentage, failure rate, and confidence score analysis provide management with clear insights for risk control and policy improvement.

Summary:

This project builds a digital identity verification system in SAS that detects fraud and fixes data errors.It creates a dataset with verification details, cleans incorrect values, standardizes text fields, and corrects date formats.Fraud detection rules are applied using logical conditions and macros to automatically flag risky cases.Processing time is classified to measure system performance.Overall, the project improves data accuracy, automates fraud detection, and supports better business decision-making.

Conclusion:

In this complete digital identity verification analytics system, we:

· Created raw dataset with intentional data quality issues

· Identified and corrected errors

· Standardized character fields

· Applied date engineering

· Calculated fraud percentages

· Implemented utilization classification

· Designed macro-based fraud detection

· Used SET, MERGE, APPEND, TRANSPOSE

· Applied numeric and character functions

· Cleaned datasets using PROC DATASETS DELETE

This mirrors real-world fintech fraud detection pipelines.

If you attend a SAS interview at companies like TCS, Cognizant, or Syneos, explaining this end-to-end workflow will demonstrate:

· Data cleaning capability

· Business logic understanding

· SAS technical strength

· Macro programming expertise

· Fraud analytics mindset

SAS INTERVIEW QUESTIONS

1.How do you append datasets in PROC SQL?

2.Difference between UNION and UNION ALL?

3.How to detect fraud using SAS?

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About the Author:
SAS Learning Hub is a data analytics and SAS programming platform focused on clinical, financial, and real-world data analysis. The content is created by professionals with academic training in Pharmaceutics and hands-on experience in Base SAS, PROC SQL, Macros, SDTM, and ADaM, providing practical and industry-relevant SAS learning resources.

Disclaimer:
The datasets and analysis in this article are created for educational and demonstration purposes only. They do not represent DIGITAL IDENTITY data.

Our Mission:
This blog provides industry-focused SAS programming tutorials and analytics projects covering finance, healthcare, and technology.

This project is suitable for:
·  Students learning SAS
·  Data analysts building portfolios
·  Professionals preparing for SAS interviews
·  Bloggers writing about analytics
·  Clinical SAS Programmer
·  Research Data Analyst
·  Regulatory Data Validator

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To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project
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413.Can We Build and Debug a Digital Identity Verification Fraud Detection System in SAS Using Advanced Data Engineering and Macros?

Digital Identity Verification Fraud Detection System Using SAS

Code Explanation

SET

PROPCASE

STRIP

UPCASE

ABS

MDY

INTCK

INTNX

MERGE

CAT

CATX

Conclusion:

1.How do you append datasets in PROC SQL?

2.Difference between UNION and UNION ALL?

3.How to detect fraud using SAS?

Follow Us On :

To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project
2.Which Airports Are Really the Busiest? – An End-to-End SAS Airport Traffic Analytics Project
3.Can Data Predict Election Outcomes? – A Complete SAS Voting Analytics Project

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About Us | Contact | Privacy Policy

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Popular posts from this blog

412.Can We Build And Clean A University Course Analytics & Fraud Detection System In Sas Using Only Macros While Intentionally Creating And Fixing Errors?

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

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

413.Can We Build and Debug a Digital Identity Verification Fraud Detection System in SAS Using Advanced Data Engineering and Macros?

Digital Identity Verification Fraud Detection System Using SAS

Code Explanation

SET

PROPCASE

STRIP

UPCASE

ABS

MDY

INTCK

INTNX

MERGE

CAT

CATX

Conclusion:

1.How do you append datasets in PROC SQL?

2.Difference between UNION and UNION ALL?

3.How to detect fraud using SAS?

Follow Us On :

To deepen your understanding of SAS analytics, please refer to our other data science and industry-focused projects listed below:

1.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project2.Which Airports Are Really the Busiest? – An End-to-End SAS Airport Traffic Analytics Project3.Can Data Predict Election Outcomes? – A Complete SAS Voting Analytics Project

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

About Us | Contact | Privacy Policy

Comments

Post a Comment

Popular posts from this blog

412.Can We Build And Clean A University Course Analytics & Fraud Detection System In Sas Using Only Macros While Intentionally Creating And Fixing Errors?

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

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

1.Which Country Truly Dominates the Olympics? – A Complete SAS Medal Efficiency Analytics Project
2.Which Airports Are Really the Busiest? – An End-to-End SAS Airport Traffic Analytics Project
3.Can Data Predict Election Outcomes? – A Complete SAS Voting Analytics Project