720.Can We Detect Errors, Prevent Fraud, And Optimize Biometric Access System Security Using Advanced SAS Programming?

Smart Biometric Security Analytics: Error Detection And Fraud Prevention With SAS

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP | LENGTH | FORMAT | INPUT | DATALINES | SET | MERGE | IF-THEN-ELSE | PROC TRANSPOSE | PROC APPEND | PROC DATASETS DELETE | MACRO | %MEND | %MACRO CALL

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Introduction

Biometric authentication systems are increasingly used in modern security infrastructures such as corporate offices, government facilities, airports, research laboratories, and banking institutions. These systems rely on biometric identifiers such as fingerprints, facial recognition, iris scans, voice recognition, or palm vein detection to grant secure access.

However, biometric systems produce large amounts of operational data. This data must be monitored carefully to detect:

• authentication delays
• system misuse
• unusual failure rates
• fraud attempts
• system performance issues

Organizations therefore analyze biometric system logs using data analytics tools.

SAS (Statistical Analysis System) is widely used for this purpose because it provides powerful capabilities for:

• data manipulation
• security analytics
• fraud detection
• operational reporting

In this project, we simulate a Biometric Access Control Dataset and demonstrate how SAS programming can be used to:

• detect data errors
• correct incorrect values
• standardize inconsistent formats
• calculate security metrics
• detect potential fraud
• optimize system utilization

We intentionally introduce errors into the dataset to simulate real-world data issues and then demonstrate how SAS programming techniques resolve them.

Table Of Contents

1. Business Context
2. Dataset Design
3. Intentional Error Dataset Creation
4. Error Identification
5. Corrected Dataset Code
6. Character Functions Usage
7. Numeric Functions Usage
8. Date Functions (MDY, INTCK, INTNX)
9. Dataset Combination (SET and MERGE)
10. Dataset Transformation (TRANSPOSE)
11. Dataset Extension (APPEND)
12. Fraud Detection Macro
13. Utilization Classification
14. Dataset Cleanup Using PROC DATASETS DELETE
15. Business Insights
16. 20 Key Points
17. Conclusion

Business Context

Consider a multinational company that uses biometric authentication across multiple secure facilities. Each entry attempt is recorded with details such as:

• biometric type used
• authentication time
• failure rate
• security score
• system utilization

The security team wants to analyze this dataset to answer questions such as:

• Which biometric system has the highest failure rate?
• Are there suspicious access attempts?
• Which systems have poor security scores?
• Which locations experience fraud attempts?

To answer these questions, the security analytics team uses SAS programming.

Dataset Design

Our dataset contains the following variables.

Variable	Description
System_ID	Unique system identifier
Biometric_Type	Fingerprint / Face / Iris etc
Authentication_Time	Seconds required to authenticate
Failure_Rate	Percentage of failed attempts
False_Acceptance	False acceptance rate
Usage_Count	Number of access attempts
Security_Score	Overall security evaluation
Percentage	Performance percentage
Fees	Maintenance fee
Access_Date	Date of authentication
Location	Access location
Utilization_Class	System utilization classification

1. Creating Dataset With Intentional Errors

data biometric_raw;

length System_ID $5 Biometric_Type $20 Location $20;

input System_ID $ Biometric_Type:$20. Authentication_Time Failure_Rate False_Acceptance

 Usage_Count Security_Score percentage fees Location $ Access_Date:ddmmyy10.;

format Access_Date date9.;

datalines;

S101 fingerprint 2.4 3.2 0.01 230 88 90 5000 delhi 01-02-2024

S102 Face_recognition 3.1 2.8 0.02 190 85 92 4500 mumbai 02-03-2024

S103 iris 1.8 1.1 0.01 250 91 94 5200 chennai 15-04-2024

S104 voice_recognition 4.0 5.5 0.05 120 70 80 4200 hyderabad 12-05-2024

S105 palm_vein 2.7 . 0.02 200 87 91 4700 pune 10-06-2024

S106 fingerprint 3.5 7.2 0.06 90 60 72 3900 kolkata 22-07-2024

S107 face_recognition 2.9 2.2 0.02 210 89 93 5100 delhi 10-08-2024

S108 iris 1.7 1.0 0.01 260 92 95 5300 chennai 12-09-2024

S109 voice_recognition 4.3 6.1 0.07 100 65 70 3800 bangalore 14-10-2024

S110 palm_vein 2.6 2.0 0.02 215 90 92 5000 hyderabad 21-11-2024

S111 fingerprint 3.0 3.5 0.03 180 84 88 4500 mumbai 05-12-2024

S112 face_recognition 3.4 4.0 0.04 160 80 85 4300 pune 10-01-2025

S113 iris 1.9 1.2 0.01 240 93 96 5400 chennai 20-02-2025

;

run;

proc print data=biometric_raw;

run;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date
1	S101	fingerprint	delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024
2	S102	Face_recognition	mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024
3	S103	iris	chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024
4	S104	voice_recognition	hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024
5	S105	palm_vein	pune	2.7	.	0.02	200	87	91	4700	10JUN2024
6	S106	fingerprint	kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024
7	S107	face_recognition	delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024
8	S108	iris	chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024
9	S109	voice_recognition	bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024
10	S110	palm_vein	hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024
11	S111	fingerprint	mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024
12	S112	face_recognition	pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025
13	S113	iris	chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025

Errors In The Dataset

The dataset contains several intentional errors.

Error 1: Date Format Issue

Dates are written as:

01-02-2024

But SAS expects a date input format.

Error 2: Missing Value

Failure rate is missing for System_ID S105.

Error 3: Inconsistent Case

Biometric types appear as:

fingerprint

Face recognition

iris

Case inconsistency affects reporting.

Error 4: Character Formatting Issues

Extra spaces and inconsistent capitalization appear in several variables.

2. Corrected Dataset Code

data biometric_clean;

set biometric_raw;

Biometric_Type = propcase(strip(Biometric_Type));

Location = propcase(strip(Location));

Failure_Rate = coalesce(Failure_Rate,0);

Auth_Rounded = round(Authentication_Time,0.1);

Usage_Adjusted = abs(Usage_Count);

Access_Month = intnx('month',Access_Date,0,'b');

Access_Duration = intck('day',mdy(1,1,2024),Access_Date);

Utilization_Class =

ifc(Usage_Count >220,"High",

ifc(Usage_Count >150,"Medium","Low"));

run;

proc print data=biometric_clean;

run;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date	Auth_Rounded	Usage_Adjusted	Access_Month	Access_Duration	Utilization_Class
1	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
2	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
3	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
4	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
5	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
6	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
7	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
8	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
9	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
10	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
11	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
12	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
13	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High

Explanation Of Code

SET Statement

set biometric_raw;

Reads observations from the raw dataset.

STRIP Function

strip()

Removes leading and trailing spaces.

PROPCASE Function

propcase()

Converts text to proper case.

Example

face recognition → Face Recognition

COALESCE Function

coalesce(Failure_Rate,0)

Replaces missing values with zero.

ROUND Function

round(Authentication_Time,0.1)

Rounds authentication time to 1 decimal place.

ABS Function

abs()

Ensures numeric values remain positive.

Date Functions

MDY

mdy(1,1,2024)

Creates SAS date.

INTCK

intck('day',start,end)

Counts number of days between two dates.

INTNX

intnx('month',date,0,'b')

Returns beginning of the month.

3. Dataset Combination Using SET

data biometric_archive;

set biometric_clean 

    biometric_clean;

run;

proc print data=biometric_archive;

run;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date	Auth_Rounded	Usage_Adjusted	Access_Month	Access_Duration	Utilization_Class
1	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
2	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
3	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
4	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
5	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
6	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
7	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
8	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
9	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
10	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
11	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
12	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
13	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High
14	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
15	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
16	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
17	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
18	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
19	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
20	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
21	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
22	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
23	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
24	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
25	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
26	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High

SET stacks datasets vertically.

4. Dataset Merge

proc sort data=biometric_clean;by System_ID;run;

proc print data=biometric_clean;

run;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date	Auth_Rounded	Usage_Adjusted	Access_Month	Access_Duration	Utilization_Class
1	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
2	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
3	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
4	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
5	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
6	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
7	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
8	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
9	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
10	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
11	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
12	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
13	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High

proc sort data=biometric_archive;by System_ID;run;

proc print data=biometric_archive;

run;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date	Auth_Rounded	Usage_Adjusted	Access_Month	Access_Duration	Utilization_Class
1	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
2	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
3	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
4	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
5	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
6	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
7	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
8	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
9	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
10	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
11	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
12	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
13	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
14	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
15	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
16	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
17	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
18	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
19	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
20	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
21	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
22	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
23	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
24	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
25	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High
26	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High

data security_merge;

merge biometric_clean 

      biometric_archive;

by System_ID;

run;

proc print data=security_merge;

run;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date	Auth_Rounded	Usage_Adjusted	Access_Month	Access_Duration	Utilization_Class
1	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
2	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
3	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
4	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
5	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
6	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
7	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
8	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
9	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
10	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
11	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
12	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
13	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
14	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
15	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
16	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
17	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
18	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
19	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
20	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
21	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
22	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
23	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
24	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
25	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High
26	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High

MERGE combines datasets horizontally.

5. Dataset Transpose

proc transpose data=biometric_clean out=biometric_transposed;

var Authentication_Time Failure_Rate Security_Score;

run;

proc print data=biometric_transposed;

run;

OUTPUT:

Obs	_NAME_	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13
1	Authentication_Time	2.4	3.1	1.8	4.0	2.7	3.5	2.9	1.7	4.3	2.6	3.0	3.4	1.9
2	Failure_Rate	3.2	2.8	1.1	5.5	0.0	7.2	2.2	1.0	6.1	2.0	3.5	4.0	1.2
3	Security_Score	88.0	85.0	91.0	70.0	87.0	60.0	89.0	92.0	65.0	90.0	84.0	80.0	93.0

TRANSPOSE rotates dataset structure.

6. Append Example

proc append base=biometric_archive

            data=biometric_clean;

run;

proc print data=biometric_archive;

run;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date	Auth_Rounded	Usage_Adjusted	Access_Month	Access_Duration	Utilization_Class
1	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
2	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
3	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
4	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
5	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
6	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
7	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
8	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
9	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
10	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
11	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
12	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
13	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
14	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
15	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
16	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
17	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
18	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
19	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
20	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
21	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
22	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
23	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
24	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
25	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High
26	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High
27	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High
28	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium
29	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High
30	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low
31	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium
32	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low
33	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium
34	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High
35	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low
36	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium
37	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium
38	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium
39	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High

TRANSPOSE rotates dataset structure.

7. Fraud Detection Macro

%macro fraud_detection;

data fraud_flag;

set biometric_clean;

if Failure_Rate >5 and False_Acceptance >0.05 then Fraud_Flag="YES";

else Fraud_Flag="NO";

run;

proc print data=fraud_flag;

run;

%mend;

%fraud_detection;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date	Auth_Rounded	Usage_Adjusted	Access_Month	Access_Duration	Utilization_Class	Fraud_Flag
1	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High	NO
2	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium	NO
3	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High	NO
4	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low	NO
5	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium	NO
6	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low	YES
7	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium	NO
8	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High	NO
9	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low	YES
10	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium	NO
11	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium	NO
12	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium	NO
13	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High	NO

Macro Explanation

Macros allow reusable SAS programs.

Fraud logic:

High failure rate

AND

High false acceptance

These patterns indicate possible fraud attempts.

8. Utilization Classification

%macro Utilization;

data Utilization;

set biometric_clean;

length Utilization $8.;

if Usage_Count > 220 then Utilization="HIGH";

else if 150 < Usage_Count < 220 then Utilization="MEDIUM";

else Utilization="LOW";

run;

proc print data=Utilization;

run;

%mend;

%Utilization;

OUTPUT:

Obs	System_ID	Biometric_Type	Location	Authentication_Time	Failure_Rate	False_Acceptance	Usage_Count	Security_Score	percentage	fees	Access_Date	Auth_Rounded	Usage_Adjusted	Access_Month	Access_Duration	Utilization_Class	Utilization
1	S101	Fingerprint	Delhi	2.4	3.2	0.01	230	88	90	5000	01FEB2024	2.4	230	23407	31	High	HIGH
2	S102	Face_recognition	Mumbai	3.1	2.8	0.02	190	85	92	4500	02MAR2024	3.1	190	23436	61	Medium	MEDIUM
3	S103	Iris	Chennai	1.8	1.1	0.01	250	91	94	5200	15APR2024	1.8	250	23467	105	High	HIGH
4	S104	Voice_recognition	Hyderabad	4.0	5.5	0.05	120	70	80	4200	12MAY2024	4.0	120	23497	132	Low	LOW
5	S105	Palm_vein	Pune	2.7	0.0	0.02	200	87	91	4700	10JUN2024	2.7	200	23528	161	Medium	MEDIUM
6	S106	Fingerprint	Kolkata	3.5	7.2	0.06	90	60	72	3900	22JUL2024	3.5	90	23558	203	Low	LOW
7	S107	Face_recognition	Delhi	2.9	2.2	0.02	210	89	93	5100	10AUG2024	2.9	210	23589	222	Medium	MEDIUM
8	S108	Iris	Chennai	1.7	1.0	0.01	260	92	95	5300	12SEP2024	1.7	260	23620	255	High	HIGH
9	S109	Voice_recognition	Bangalore	4.3	6.1	0.07	100	65	70	3800	14OCT2024	4.3	100	23650	287	Low	LOW
10	S110	Palm_vein	Hyderabad	2.6	2.0	0.02	215	90	92	5000	21NOV2024	2.6	215	23681	325	Medium	MEDIUM
11	S111	Fingerprint	Mumbai	3.0	3.5	0.03	180	84	88	4500	05DEC2024	3.0	180	23711	339	Medium	MEDIUM
12	S112	Face_recognition	Pune	3.4	4.0	0.04	160	80	85	4300	10JAN2025	3.4	160	23742	375	Medium	MEDIUM
13	S113	Iris	Chennai	1.9	1.2	0.01	240	93	96	5400	20FEB2025	1.9	240	23773	416	High	HIGH

Systems are classified based on usage.

Usage Count	Category
High	>220
Medium	150–220
Low	<150

9.PROC DATASETS DELETE

proc datasets library=work;

delete biometric_archive biometric_transposed;

quit;

OUTPUT:

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

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

Deletes unnecessary datasets.

Advantages:

·  frees memory

·  reduces clutter

·  improves efficiency

10. Business Insights

Using SAS analysis we can identify:

High Risk Systems

Systems with:

·  high failure rate

·  high false acceptance

Efficient Systems

Low authentication time and high security score.

Underutilized Systems

Low usage count systems may indicate:

·  poor placement

·  user dissatisfaction

11.  20 Key Points Of This Project

·  Dataset Creation for Biometric Access Systems Using SAS DATA Step and DATALINES

·  Simulation of Real-World Data Issues by Introducing Intentional Errors

·  Data Cleaning and Standardization Using Character Functions like STRIP and TRIM

·  Text Formatting and Case Standardization Using PROPCASE, UPCASE, and LOWCASE

·  Handling Missing Values in Security Data Using COALESCE Function

·  Numeric Data Correction and Precision Handling Using ROUND and ABS Functions

·  Date Construction and Formatting Using MDY Function

·  Time Interval and Activity Duration Analysis Using INTCK Function

·  Monthly Time Period Derivation Using INTNX Function

·  Vertical Dataset Combination Using SET Statement

·  Horizontal Dataset Integration Using MERGE Statement

·  Dataset Restructuring for Analytical Reporting Using PROC TRANSPOSE

·  Incremental Data Addition Using PROC APPEND

·  System Utilization Classification Based on Usage_Count Logic

·  Fraud Detection Logic Based on Failure Rate and False Acceptance Thresholds

·  Automation of Fraud Detection Process Using SAS MACROS

·  Text Concatenation and Label Creation Using CAT and CATX Functions

·  Detection and Correction of Data Quality Issues in Security Logs

·  Workspace and Dataset Management Using PROC DATASETS DELETE

·  Generating Security and Performance Insights for Biometric Authentication Systems Using SAS Analytics

Conclusion

This project demonstrates how SAS programming can be used to analyze and improve biometric access systems.

·  Real datasets contain errors and inconsistencies

·  SAS provides powerful tools to clean data

·  Character functions standardize text

·  Numeric functions correct values

·  Date functions analyze time patterns

·  Macros automate fraud detection

·  Dataset operations support advanced analysis

Using these techniques, organizations can ensure that biometric authentication systems remain reliable, secure, and fraud-resistant.

SAS INTERVIEW QUESTIONS

·  What is %STR and %NRSTR?

·  What is %DO loop?

·  What is SYMGET function?

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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 PARKING SYSTEM data.

Our Mission:

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This project is suitable for:

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