417.Can We Design, Debug, Detect Fraud, and Optimize Large Public Event Management Using Advanced SAS Programming Techniques?

Public Event Risk and Fraud Management Using Advanced SAS Techniques

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —DATA STEP| SET | IF-THEN-ELSE | LENGTH | FORMAT | INTCK | INTNX | MDY | MERGE | PROC SORT | PROC SQL | PROC APPEND | PROC TRANSPOSE | PROC REPORT | PROC FORMAT | MACRO (%MACRO / %MEND) | HASH OBJECT | PROC DATASETS DELETE | RUN | QUIT

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Introduction

Managing large public events such as concerts, expos, political rallies, and sports finals requires advanced operational monitoring. Crowd control, security risk detection, staff deployment optimization, and fraud surveillance are critical components.

In this integrated SAS project, we designed a complete Public Event Risk Surveillance System that:

·       Creates structured datasets

·       Introduces intentional errors

·       Performs debugging and data correction

·       Applies fraud detection logic

·       Derives utilization metrics

·       Implements rolling risk monitoring

·       Uses macros, SQL, hash objects, and reporting procedures

·       Builds audit and alert mechanisms

This project simulates a real-world operational intelligence system similar to safety monitoring frameworks used in regulated industries.

Table Of Contents

1.     Business Context 

2.     Raw Dataset Creation with Intentional Errors

3.     Error Identification and Debugging Strategy

4.     Data Cleaning and Standardization

5.     Character and Numeric Function Applications

6.     Date Intelligence (MDY, INTCK, INTNX, DATEPART)

7.     Utilization and Risk Index Derivation

8.     Fraud Detection Macro Logic

9.     Revenue Per Capita Analysis

10.  Duplicate Detection and Data Integrity Checks

11.  Dataset Integration Using SET, MERGE, APPEND

12.  Hash Object Lookup Optimization

13.  Rolling 30-Day Risk Monitoring

14.  Peak Hour Crowd Analysis

15.  Risk Band Classification Using PROC FORMAT

16.  SQL-Based Fraud Cross Validation

17.  Weighted Risk Model Enhancement

18.  Growth Trend Analysis Using LAG( )

19.  PROC TRANSPOSE for Structural Reshaping

20.  Monthly Risk Aggregation Macro

21.  Dashboard Reporting via PROC REPORT

22.  Automated Alert Dataset Creation

23.  Audit Trail Generation

24.  QC and Validation Framework

25.  WORK Library Cleanup Using PROC DATASETS

Business Context

Imagine a government intelligence department monitoring large public events across India to:

• Detect abnormal crowd surges
• Prevent ticket fraud
• Monitor security threats
• Optimize staff deployment
• Predict crowd risk levels

Variables included:

Variable	Description
Event_Name	Name of public event
Location	City
Attendees	Total attendance
Entry_Time	Entry timestamp
Security_Incidents	Reported incidents
Staff_Deployed	Number of staff
Crowd_Risk_Index	Calculated risk %
Ticket_Revenue	Revenue collected
Fraud_Flag	Fraud indicator

1. Raw Dataset Creation (With Intentional Errors)

data public_events_raw;

    length Event_Name $50 Location $30;

    format Entry_Time datetime20.;

    input Event_Name $ Location $ Attendees Entry_Time :datetime20.

          Security_Incidents Staff_Deployed Crowd_Risk_Index Ticket_Revenue;

datalines;

MusicFest Hyderabad 50000 01JAN2026:10:00:00 5 200 85 2500000

TechExpo Bangalore 30000 05FEB2026:09:30:00 2 150 70 1800000

CricketFinal Mumbai 120000 10MAR2026:14:00:00 15 500 95 8000000

PoliticalRally Delhi 200000 20APR2026:11:00:00 25 300 110 10000000

FoodCarnival Chennai -5000 15MAY2026:16:00:00 1 100 40 500000

ArtExpo Pune 15000 25JUN2026:10:30:00 . 80 55 600000

;

run;

proc print data=public_events_raw;

run;

OUTPUT:

Obs	Event_Name	Location	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue
1	MusicFest	Hyderabad	01JAN2026:10:00:00	50000	5	200	85	2500000
2	TechExpo	Bangalore	05FEB2026:09:30:00	30000	2	150	70	1800000
3	CricketFinal	Mumbai	10MAR2026:14:00:00	120000	15	500	95	8000000
4	PoliticalRally	Delhi	20APR2026:11:00:00	200000	25	300	110	10000000
5	FoodCarnival	Chennai	15MAY2026:16:00:00	-5000	1	100	40	500000
6	ArtExpo	Pune	25JUN2026:10:30:00	15000	.	80	55	600000

Intentional Errors 

·  Crowd_Risk_Index = 110 (Invalid >100)

·  Negative Attendees = -5000

·  Missing Security_Incidents

·  No Fraud_Flag

·  No Utilization classification

·  No date derivation

·  No cleaning of character strings

2. Corrected Full-Length SAS Code

data public_events_clean;

    length Event_Name $50 Location $30 Fraud_Flag $3 Utilization_Class $10;

    set public_events_raw;

    Event_Name = propcase(strip(Event_Name));

    Location = upcase(strip(Location));

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

    Crowd_Risk_Index = coalesce(Crowd_Risk_Index, 0);

    if Crowd_Risk_Index > 100 then Crowd_Risk_Index = 100;

    Event_Date = datepart(Entry_Time);

    Event_Month = intnx('month', Event_Date, 0, 'b');

    Days_Since_Event = intck('day', Event_Date, today());

    Staff_Utilization = (Attendees / Staff_Deployed);    

    if Staff_Utilization > 400 then Utilization_Class="High";

    else if Staff_Utilization >200 then Utilization_Class="Medium";

    else Utilization_Class="Low";

    Crowd_Risk_Index = round((Security_Incidents*5 + Staff_Utilization/10),0.1);

    format Event_Date date9. Event_Month monyy7.;

run;

proc print data=public_events_clean;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization
1	Musicfest	HYDERABAD		Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000
2	Techexpo	BANGALORE		Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000
3	Cricketfinal	MUMBAI		Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000
4	Politicalrally	DELHI		High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667
5	Foodcarnival	CHENNAI		Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000
6	Artexpo	PUNE		Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500

·  Removes older versions

·  Prevents overwrite confusion

·  Maintains clean WORK library

3. Macro for Fraud Detection

%macro detect_fraud(input=,output=);

data &output.;

    set &input.;

    length Fraud_Flag $3;

    if Crowd_Risk_Index > 90 or Security_Incidents > 20 then Fraud_Flag="YES";

    else Fraud_Flag="NO";

run;

proc print data=&output.;

run;

%mend;

%detect_fraud(input=public_events_clean,output=public_events_final);

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization
1	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000
2	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000
3	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000
4	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667
5	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500

4. Append Example

data new_events;

    set public_events_final;

    where Location="HYDERABAD";

run;

proc print data=new_events;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization
1	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50	2500000	01JAN2026	JAN2026	60	250

proc append base=public_events_final 

            data=new_events force;

run;

proc print data=public_events_final;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization
1	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000
2	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000
3	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000
4	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667
5	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500
7	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000

5. Merge Example

data staff_bonus;

    input Location:$30. Bonus;

datalines;

HYDERABAD 50000

MUMBAI 100000

;

run;

proc print data=staff_bonus;

run;

OUTPUT:

Obs	Location	Bonus
1	HYDERABAD	50000
2	MUMBAI	100000

proc sort data=public_events_final; by Location; run;

proc print data=public_events_final;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000
5	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000
6	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000
7	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500

proc sort data=staff_bonus; by Location; run;

proc print data=staff_bonus;

run;

OUTPUT:

Obs	Location	Bonus
1	HYDERABAD	50000
2	MUMBAI	100000

data merged_data;

    merge public_events_final staff_bonus;

    by Location;

run;

proc print data=merged_data;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Bonus
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	.
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	.
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	.
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	50000
5	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	50000
6	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	100000
7	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.

6. Transpose Example

proc sort data=merged_data; by Location; run;

proc print data=merged_data;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Bonus
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	.
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	.
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	.
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	50000
5	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	50000
6	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	100000
7	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.

proc transpose data=merged_data out=transposed_data;

    by Location;

    var Attendees Staff_Deployed Security_Incidents;

run;

proc print data=transposed_data;

run;

OUTPUT:

Obs	Location	_NAME_	COL1	COL2
1	BANGALORE	Attendees	30000	.
2	BANGALORE	Staff_Deployed	150	.
3	BANGALORE	Security_Incidents	2	.
4	CHENNAI	Attendees	5000	.
5	CHENNAI	Staff_Deployed	100	.
6	CHENNAI	Security_Incidents	1	.
7	DELHI	Attendees	200000	.
8	DELHI	Staff_Deployed	300	.
9	DELHI	Security_Incidents	25	.
10	HYDERABAD	Attendees	50000	50000
11	HYDERABAD	Staff_Deployed	200	200
12	HYDERABAD	Security_Incidents	5	5
13	MUMBAI	Attendees	120000	.
14	MUMBAI	Staff_Deployed	500	.
15	MUMBAI	Security_Incidents	15	.
16	PUNE	Attendees	15000	.
17	PUNE	Staff_Deployed	80	.
18	PUNE	Security_Incidents	.	.

7. Datasets Delete/Unwanted Datasets

proc datasets library=work nolist;

   delete public_events_clean fraud_summary transposed_data;

quit;

LOG:

NOTE: The file WORK.FRAUD_SUMMARY (memtype=DATA) was not found, but appears on a DELETE statement.

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

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

8. PROC FORMAT – Risk Band Classification

proc format;

    value riskband

        low-50 = "Low Risk"

        50<-80 = "Moderate Risk"

        80<-high = "High Risk";

run;

LOG:

NOTE: Format RISKBAND has been output.

·  Standardizes classification logic

·  Avoids hardcoding IF-THEN repeatedly

·  Regulatory-style mapping (like CDISC controlled terminology)

9. Apply Risk Format

data public_events_enhanced;

    set public_events_final;

    Risk_Band = put(Crowd_Risk_Index, riskband.);

run;

proc print data=public_events_enhanced;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk
5	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk
6	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk
7	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.

·  Converts numeric risk to descriptive category

·  Improves interpretability

10. Duplicate Detection Logic

proc sort data=public_events_enhanced out=dedup_check nodupkey dupout=duplicates;

    by Event_Name Event_Date Location;

run;

proc print data=dedup_check;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band
1	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.
2	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk
3	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk
5	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk
6	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk

proc print data=duplicates;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band
1	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50	2500000	01JAN2026	JAN2026	60	250	Low Risk

·  Prevents duplicate operational entries

·  dupout= captures duplicate dataset for audit

11. Revenue Per Capita Derivation

data revenue_analysis;

    set dedup_check;

    Revenue_Per_Capita = Ticket_Revenue / Attendees;

run;

proc print data=revenue_analysis;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita
1	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000
2	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667
3	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000
5	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000
6	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000

·  Detects ticket manipulation fraud

·  Low revenue per head = potential illegal entries

12. Peak Hour Detection

data peak_analysis;

    set revenue_analysis;

    Entry_Hour = hour(Entry_Time);

    if 15 < Entry_Hour < 21 then Peak_Flag="YES";

    else Peak_Flag="NO";

run;

proc print data=peak_analysis;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag
1	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO
2	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO
3	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO
5	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO
6	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO

·  Identifies rush-hour crowd risk

·  Useful for deployment strategy

13. Rolling 30-Day Risk Monitoring

proc sort data=peak_analysis;

    by Location Event_Date;

run;

proc print data=peak_analysis;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO
5	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO

data rolling_risk;

    set peak_analysis;

    by Location;

    retain Prev_Date;

    Days_Gap = intck('day', Prev_Date, Event_Date);

    if Days_Gap <= 30 then Rolling_Risk_Flag="YES";

    else Rolling_Risk_Flag="NO";

    Prev_Date = Event_Date;

run;

proc print data=rolling_risk;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag	Prev_Date	Days_Gap	Rolling_Risk_Flag
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO	24142	.	YES
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES	24241	99	NO
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO	24216	-25	YES
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO	24107	-109	YES
5	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO	24175	68	NO
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO	24282	107	NO

·  Detects frequent risky events in short duration

·  Prevents repeated security lapses

14. HASH Object Lookup 

data bonus_lookup;

    if _N_=1 then do;

        declare hash h(dataset:"staff_bonus");

        h.defineKey("Location");

        h.defineData("Bonus");

        h.defineDone();

    end;

    set rolling_risk;

    if h.find()=0 then Bonus_Assigned=Bonus;

    else Bonus_Assigned=0;

run;

proc print data=bonus_lookup;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag	Prev_Date	Days_Gap	Rolling_Risk_Flag	Bonus_Assigned	Bonus
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO	24142	.	YES	0	.
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES	24241	99	NO	0	.
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO	24216	-25	YES	0	.
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO	24107	-109	YES	50000	50000
5	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO	24175	68	NO	100000	100000
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO	24282	107	NO	0	.

·  Faster than MERGE

·  Efficient in large datasets

·  Real-world production optimization

15. Monthly Risk Summary Macro

%macro monthly_summary;

proc sql;

    create table monthly_risk_summary as

    select Location,

           month(Event_Date) as Month,

           mean(Crowd_Risk_Index) as Avg_Risk,

           sum(Security_Incidents) as Total_Incidents

    from bonus_lookup

    group by Location, calculated Month;

quit;

proc print data=monthly_risk_summary;

run;

%mend;

%monthly_summary;

OUTPUT:

Obs	Location	Month	Avg_Risk	Total_Incidents
1	BANGALORE	2	30.0	2
2	CHENNAI	5	10.0	1
3	DELHI	4	191.7	25
4	HYDERABAD	1	50.0	5
5	MUMBAI	3	99.0	15
6	PUNE	6	.	.

·  Automated aggregation

·  Reusable reporting logic

16. PROC REPORT 

proc report data=monthly_risk_summary nowd;

    column Location Month Avg_Risk Total_Incidents;

run;

OUTPUT:

Location	Month	Avg_Risk	Total_Incidents
BANGALORE	2	30	2
CHENNAI	5	10	1
DELHI	4	191.7	25
HYDERABAD	1	50	5
MUMBAI	3	99	15
PUNE	6	.	.

·  Management reporting

·  Structured tabular output

17. PROC SQL Fraud Cross Validation

proc sql;

  create table fraud_cross_check as

  select *,

    case 

     when Revenue_Per_Capita < 20 and Crowd_Risk_Index > 85 then "Revenue Mismatch"

     when Security_Incidents > 20 then "High Security Threat"

     else "Normal"

     end as Fraud_Reason

  from bonus_lookup;

quit;

proc print data=fraud_cross_check;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag	Prev_Date	Days_Gap	Rolling_Risk_Flag	Bonus_Assigned	Bonus	Fraud_Reason
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO	24142	.	YES	0	.	Normal
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES	24241	99	NO	0	.	Normal
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO	24216	-25	YES	0	.	High Security Threat
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO	24107	-109	YES	50000	50000	Normal
5	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO	24175	68	NO	100000	100000	Normal
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO	24282	107	NO	0	.	Normal

·  Alternative validation method

·  SQL-style case logic

18. Integrity Check Dataset

data integrity_check;

    set fraud_cross_check;

    length Issue $20.;

    if missing(Attendees) then Issue="Missing Attendees";

    else if Attendees=0 then Issue="Zero Attendance";

    else if Staff_Deployed=0 then Issue="No Staff Assigned";

run;

proc print data=integrity_check;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag	Prev_Date	Days_Gap	Rolling_Risk_Flag	Bonus_Assigned	Bonus	Fraud_Reason	Issue
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO	24142	.	YES	0	.	Normal
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES	24241	99	NO	0	.	Normal
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO	24216	-25	YES	0	.	High Security Threat
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO	24107	-109	YES	50000	50000	Normal
5	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO	24175	68	NO	100000	100000	Normal
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO	24282	107	NO	0	.	Normal

19. Automated Alert Dataset

data alert_dataset;

    set fraud_cross_check;

    if Fraud_Flag="YES" or Risk_Band="High Risk";

run;

proc print data=alert_dataset;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag	Prev_Date	Days_Gap	Rolling_Risk_Flag	Bonus_Assigned	Bonus	Fraud_Reason
1	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.0000	11	NO	24216	-25	YES	0	.	High Security Threat
2	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.6667	14	NO	24175	68	NO	100000	100000	Normal

20. Recalculated Weighted Risk Model

data weighted_model;

    set fraud_cross_check;

    Weighted_Risk =

          (Security_Incidents*0.4)+ (Staff_Utilization*0.3)

        + (Revenue_Per_Capita*0.3);

run;

proc print data=weighted_model;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag	Prev_Date	Days_Gap	Rolling_Risk_Flag	Bonus_Assigned	Bonus	Fraud_Reason	Weighted_Risk
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO	24142	.	YES	0	.	Normal	78.8
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES	24241	99	NO	0	.	Normal	45.4
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO	24216	-25	YES	0	.	High Security Threat	225.0
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO	24107	-109	YES	50000	50000	Normal	92.0
5	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO	24175	68	NO	100000	100000	Normal	98.0
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO	24282	107	NO	0	.	Normal	.

21. Event Growth Trend Using LAG( )

data growth_trend;

    set weighted_model;

    by Location;

    Prev_Attendees = lag(Attendees);

    Growth = Attendees - Prev_Attendees;

run;

proc print data=growth_trend;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag	Prev_Date	Days_Gap	Rolling_Risk_Flag	Bonus_Assigned	Bonus	Fraud_Reason	Weighted_Risk	Prev_Attendees	Growth
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO	24142	.	YES	0	.	Normal	78.8	.	.
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES	24241	99	NO	0	.	Normal	45.4	30000	-25000
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO	24216	-25	YES	0	.	High Security Threat	225.0	5000	195000
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO	24107	-109	YES	50000	50000	Normal	92.0	200000	-150000
5	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO	24175	68	NO	100000	100000	Normal	98.0	50000	70000
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO	24282	107	NO	0	.	Normal	.	120000	-105000

22. Final Audit Trail Dataset

data audit_trail;

    set growth_trend;

    Audit_Timestamp = datetime();

    format Audit_Timestamp datetime20.;

run;

proc print data=audit_trail;

run;

OUTPUT:

Obs	Event_Name	Location	Fraud_Flag	Utilization_Class	Entry_Time	Attendees	Security_Incidents	Staff_Deployed	Crowd_Risk_Index	Ticket_Revenue	Event_Date	Event_Month	Days_Since_Event	Staff_Utilization	Risk_Band	Revenue_Per_Capita	Entry_Hour	Peak_Flag	Prev_Date	Days_Gap	Rolling_Risk_Flag	Bonus_Assigned	Bonus	Fraud_Reason	Weighted_Risk	Prev_Attendees	Growth	Audit_Timestamp
1	Techexpo	BANGALORE	NO	Low	05FEB2026:09:30:00	30000	2	150	30.0	1800000	05FEB2026	FEB2026	25	200.000	Low Risk	60.000	9	NO	24142	.	YES	0	.	Normal	78.8	.	.	02MAR2026:20:03:28
2	Foodcarnival	CHENNAI	NO	Low	15MAY2026:16:00:00	5000	1	100	10.0	500000	15MAY2026	MAY2026	-74	50.000	Low Risk	100.000	16	YES	24241	99	NO	0	.	Normal	45.4	30000	-25000	02MAR2026:20:03:28
3	Politicalrally	DELHI	YES	High	20APR2026:11:00:00	200000	25	300	191.7	10000000	20APR2026	APR2026	-49	666.667	High Risk	50.000	11	NO	24216	-25	YES	0	.	High Security Threat	225.0	5000	195000	02MAR2026:20:03:28
4	Musicfest	HYDERABAD	NO	Medium	01JAN2026:10:00:00	50000	5	200	50.0	2500000	01JAN2026	JAN2026	60	250.000	Low Risk	50.000	10	NO	24107	-109	YES	50000	50000	Normal	92.0	200000	-150000	02MAR2026:20:03:28
5	Cricketfinal	MUMBAI	YES	Medium	10MAR2026:14:00:00	120000	15	500	99.0	8000000	10MAR2026	MAR2026	-8	240.000	High Risk	66.667	14	NO	24175	68	NO	100000	100000	Normal	98.0	50000	70000	02MAR2026:20:03:28
6	Artexpo	PUNE	NO	Low	25JUN2026:10:30:00	15000	.	80	.	600000	25JUN2026	JUN2026	-115	187.500	.	40.000	10	NO	24282	107	NO	0	.	Normal	.	120000	-105000	02MAR2026:20:03:28

Numeric & Character Functions 

·       ABS()

·       ROUND()

·       COALESCE()

·       STRIP()

·       TRIM()

·       CAT()

·       CATX()

·       PROPCASE()

·       UPCASE()

·       LOWCASE()

Fraud Logic

Fraud if:

·       Risk Index > 90
OR

·       Security Incidents > 20

Explanation

DATA Step Philosophy

DATA step reads row by row. Similar to clinical ADaM derivation.

SET Statement

Used to read existing dataset.

LENGTH Statement

Defines character variable storage.

PROPCASE

Standardizes Event_Name (Musicfest → Musicfest)

STRIP

Removes trailing blanks.

UPCASE

Ensures Location consistency for merges.

ABS

Corrects negative attendance error.

COALESCE

Handles missing numeric values.

INTNX

Derives beginning of month.

INTCK

Calculates duration difference.

DATEPART

Extracts date from datetime.

ROUND

Controls decimal precision.

MACRO

Reusable logic block.

APPEND

Adds new records.

MERGE

Combines datasets by key.

TRANSPOSE

Converts rows to columns.

PROC DATASETS DELETE

Deletes datasets efficiently.

Common Errors Explained and Debugged

Error	Cause	Fix
Negative Attendees	Data entry error	ABS()
Risk > 100	Invalid scale	Cap at 100
Missing Incidents	Null value	COALESCE
Merge mismatch	Case sensitivity	UPCASE
Overwritten dataset	No delete	PROC DATASETS

Quality Control Checks

·       Frequency check for Fraud_Flag

·       Mean utilization

·       Check duplicates

·       Missing value check

·       Risk distribution

10 Key Points About This Project

1.     The project simulates a real-time public event monitoring system for large-scale crowd management.

2.     Intentional data errors such as negative attendance, missing incidents, and risk values above 100 were introduced and systematically corrected.

3.     Character standardization was implemented using STRIP, PROPCASE, UPCASE, LOWCASE, CAT, and CATX functions to ensure merge compatibility.

4.     Date functions like MDY, INTCK, INTNX, and DATEPART were used for event date derivations and rolling surveillance calculations.

5.     Staff utilization was calculated to determine operational efficiency and classify events into Low, Medium, or High utilization categories.

6.     A reusable fraud detection macro flagged events based on high risk index and abnormal security incidents.

7.     Revenue Per Capita analysis helped identify ticket fraud or illegal crowd entries.

8.     Rolling 30-day monitoring logic identified repeated high-risk events in short time windows.

9.     SQL cross-validation and hash lookup techniques improved validation speed and integrity.

10.  The system generated alert datasets and audit trails, ensuring traceability and compliance readiness.

Summary

This project demonstrates how SAS can be used to build a comprehensive large public event monitoring and fraud detection system. Starting from raw data with intentional errors, we applied structured debugging, cleaning, derivation logic, and validation checks. Advanced SAS techniques including macros, SQL, hash objects, rolling date logic, and classification models were integrated to create a scalable surveillance framework. Operational metrics such as staff utilization, revenue per capita, and crowd risk index were derived to support decision-making. The project also incorporated duplicate detection, integrity checks, and audit trails to ensure governance and compliance. Overall, it reflects a real-world operational intelligence system suitable for government, security, or enterprise risk management applications.

Conclusion

This integrated SAS project successfully designed, debugged, and optimized a large public event surveillance system. By combining structured dataset creation, intentional error correction, fraud detection macros, rolling risk monitoring, and operational performance metrics, we built a scalable and audit-ready analytical framework. Advanced programming techniques such as SET, MERGE, APPEND, PROC SQL, HASH objects, TRANSPOSE, and DATE functions strengthened both performance and validation. The inclusion of utilization classification, weighted risk modeling, and automated alerts enhanced real-world applicability. This project not only demonstrates strong SAS technical capability but also reflects business-oriented thinking in risk analytics, operational intelligence, and fraud detection making it highly valuable for interviews and enterprise-level implementation.

SAS INTERVIEW QUESTIONS

·  What is the difference between SET, MERGE, and UPDATE?

·  What happens if you do not use RUN statement?

·  How does SAS handle missing numeric values in comparisons?

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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 PUBLIC EVENT 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.Can SAS Macros Automate Fraud Case Analysis and Reporting?

2.How Can PROC MEANS and PROC UNIVARIATE Reveal Key Statistics About Major Indian Rivers?

3.Can We Build a Smart Cylinder Analytics System Using Simple SAS Procedures?

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