369.DIGITAL TRANSACTIONS FRAUD RISK ANALYSIS USING SAS

DIGITAL TRANSACTIONS FRAUD RISK ANALYSIS USING SAS

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS PROC SQL | PROC MEANS | PROC FREQ | PROC SGPLOT | MACROS | INTCK | INTNX | MDY | MERGE | SET | APPEND | TRANSPOSE

1. Introduction – Why Digital Transaction Analytics Matters

In today’s digital economy, billions of financial transactions happen every day through:

1.UPI

2.Credit Cards

3.Debit Cards

4.Net Banking

5.Mobile Wallets

6.Crypto platforms

**With the explosion of online payments, fraud detection has become one of the most critical analytics use cases in the world.

**Banks, fintech companies, and payment gateways continuously analyze:

1.Transaction amount

2.Time of transaction

3.Platform used

4.Risk patterns

5.User behavior

**In this blog, we will build a complete SAS-based Digital Transactions Analytics System that mimics how real financial institutions detect fraud risk.

We will use:

1.DATA Step

2.PROC SQL

3.PROC MEANS

4.PROC FREQ

5.PROC SGPLOT

6.Macros

7.INTCK, INTNX, MDY

8.MERGE, SET, APPEND, TRANSPOSE

2. Creating the Digital Transactions Dataset

We first create a raw dataset of transactions.

Structure

Each transaction contains:

Variable	Meaning
Transaction_ID	Unique transaction number
Transaction_Type	UPI, Card, Wallet, etc
Platform	GooglePay, PhonePe, Paytm, BankApp
Amount	Transaction amount
Trans_Date	Date of transaction
Trans_Time	Time of transaction
Fraud_Risk	Risk score (0–100)

Data Creation

data digital_txn_raw;

    format Trans_Date date9.;

    input Transaction_ID Transaction_Type:$15. Platform:$15. Amount Trans_Date :date9. 

          Trans_Time $ Fraud_Risk;

    datalines;

1 UPI GooglePay 1200 05JAN2025 09:12 25

2 Card Paytm 45000 05JAN2025 23:45 88

3 Wallet PhonePe 3200 06JAN2025 11:15 30

4 UPI BankApp 15000 06JAN2025 02:30 75

5 NetBanking BankApp 78000 07JAN2025 01:10 92

6 Card GooglePay 5600 07JAN2025 14:05 40

7 Wallet Paytm 300 07JAN2025 03:40 60

8 UPI PhonePe 2200 08JAN2025 19:20 20

9 Card BankApp 92000 08JAN2025 00:50 95

10 NetBanking GooglePay 41000 09JAN2025 22:15 85

11 UPI PhonePe 600 09JAN2025 10:10 15

12 Wallet Paytm 1800 10JAN2025 18:30 25

13 Card GooglePay 75000 10JAN2025 23:59 90

14 UPI BankApp 1300 11JAN2025 09:05 18

15 NetBanking PhonePe 55000 11JAN2025 01:40 89

;

run;

proc print data=digital_txn_raw;

run;

OUTPUT:

Obs	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk
1	05JAN2025	1	UPI	GooglePay	1200	09:12	25
2	05JAN2025	2	Card	Paytm	45000	23:45	88
3	06JAN2025	3	Wallet	PhonePe	3200	11:15	30
4	06JAN2025	4	UPI	BankApp	15000	02:30	75
5	07JAN2025	5	NetBanking	BankApp	78000	01:10	92
6	07JAN2025	6	Card	GooglePay	5600	14:05	40
7	07JAN2025	7	Wallet	Paytm	300	03:40	60
8	08JAN2025	8	UPI	PhonePe	2200	19:20	20
9	08JAN2025	9	Card	BankApp	92000	00:50	95
10	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85
11	09JAN2025	11	UPI	PhonePe	600	10:10	15
12	10JAN2025	12	Wallet	Paytm	1800	18:30	25
13	10JAN2025	13	Card	GooglePay	75000	23:59	90
14	11JAN2025	14	UPI	BankApp	1300	09:05	18
15	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89

3. Creating Date-Based Intelligence Using MDY, INTNX, INTCK

MDY() – Create financial reporting month

data digital_dates;

    set digital_txn_raw;

    Report_Month = mdy(month(Trans_Date),1,year(Trans_Date));

    format Report_Month monyy7.;

run;

proc print data=digital_dates;

run;

OUTPUT:

Obs	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk	Report_Month
1	05JAN2025	1	UPI	GooglePay	1200	09:12	25	JAN2025
2	05JAN2025	2	Card	Paytm	45000	23:45	88	JAN2025
3	06JAN2025	3	Wallet	PhonePe	3200	11:15	30	JAN2025
4	06JAN2025	4	UPI	BankApp	15000	02:30	75	JAN2025
5	07JAN2025	5	NetBanking	BankApp	78000	01:10	92	JAN2025
6	07JAN2025	6	Card	GooglePay	5600	14:05	40	JAN2025
7	07JAN2025	7	Wallet	Paytm	300	03:40	60	JAN2025
8	08JAN2025	8	UPI	PhonePe	2200	19:20	20	JAN2025
9	08JAN2025	9	Card	BankApp	92000	00:50	95	JAN2025
10	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85	JAN2025
11	09JAN2025	11	UPI	PhonePe	600	10:10	15	JAN2025
12	10JAN2025	12	Wallet	Paytm	1800	18:30	25	JAN2025
13	10JAN2025	13	Card	GooglePay	75000	23:59	90	JAN2025
14	11JAN2025	14	UPI	BankApp	1300	09:05	18	JAN2025
15	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89	JAN2025

INTNX – Generate next review date

data digital_dates2;

    set digital_dates;

    Review_Date = intnx('day',Trans_Date,7,'same');

    format Review_Date date9.;

run;

proc print data=digital_dates2;

run;

OUTPUT:

Obs	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk	Report_Month	Review_Date
1	05JAN2025	1	UPI	GooglePay	1200	09:12	25	JAN2025	12JAN2025
2	05JAN2025	2	Card	Paytm	45000	23:45	88	JAN2025	12JAN2025
3	06JAN2025	3	Wallet	PhonePe	3200	11:15	30	JAN2025	13JAN2025
4	06JAN2025	4	UPI	BankApp	15000	02:30	75	JAN2025	13JAN2025
5	07JAN2025	5	NetBanking	BankApp	78000	01:10	92	JAN2025	14JAN2025
6	07JAN2025	6	Card	GooglePay	5600	14:05	40	JAN2025	14JAN2025
7	07JAN2025	7	Wallet	Paytm	300	03:40	60	JAN2025	14JAN2025
8	08JAN2025	8	UPI	PhonePe	2200	19:20	20	JAN2025	15JAN2025
9	08JAN2025	9	Card	BankApp	92000	00:50	95	JAN2025	15JAN2025
10	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85	JAN2025	16JAN2025
11	09JAN2025	11	UPI	PhonePe	600	10:10	15	JAN2025	16JAN2025
12	10JAN2025	12	Wallet	Paytm	1800	18:30	25	JAN2025	17JAN2025
13	10JAN2025	13	Card	GooglePay	75000	23:59	90	JAN2025	17JAN2025
14	11JAN2025	14	UPI	BankApp	1300	09:05	18	JAN2025	18JAN2025
15	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89	JAN2025	18JAN2025

INTCK – Calculate days between transaction and review

data digital_dates3;

    set digital_dates2;

    Days_To_Review = intck('day',Trans_Date,Review_Date);

run;

proc print data=digital_dates3;

run;

OUTPUT:

Obs	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk	Report_Month	Review_Date	Days_To_Review
1	05JAN2025	1	UPI	GooglePay	1200	09:12	25	JAN2025	12JAN2025	7
2	05JAN2025	2	Card	Paytm	45000	23:45	88	JAN2025	12JAN2025	7
3	06JAN2025	3	Wallet	PhonePe	3200	11:15	30	JAN2025	13JAN2025	7
4	06JAN2025	4	UPI	BankApp	15000	02:30	75	JAN2025	13JAN2025	7
5	07JAN2025	5	NetBanking	BankApp	78000	01:10	92	JAN2025	14JAN2025	7
6	07JAN2025	6	Card	GooglePay	5600	14:05	40	JAN2025	14JAN2025	7
7	07JAN2025	7	Wallet	Paytm	300	03:40	60	JAN2025	14JAN2025	7
8	08JAN2025	8	UPI	PhonePe	2200	19:20	20	JAN2025	15JAN2025	7
9	08JAN2025	9	Card	BankApp	92000	00:50	95	JAN2025	15JAN2025	7
10	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85	JAN2025	16JAN2025	7
11	09JAN2025	11	UPI	PhonePe	600	10:10	15	JAN2025	16JAN2025	7
12	10JAN2025	12	Wallet	Paytm	1800	18:30	25	JAN2025	17JAN2025	7
13	10JAN2025	13	Card	GooglePay	75000	23:59	90	JAN2025	17JAN2025	7
14	11JAN2025	14	UPI	BankApp	1300	09:05	18	JAN2025	18JAN2025	7
15	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89	JAN2025	18JAN2025	7

4. Fraud Risk Classification Using Macro

Macro for Risk Evaluation

%macro risk_eval;

data digital_risk;

    length risk_level $10.;

    set digital_dates3;

    if Fraud_Risk >= 80 then Risk_Level="HIGH";

    else if Fraud_Risk >= 40 then Risk_Level="MEDIUM";

    else Risk_Level="LOW";

run;

proc print data=digital_risk;

run;

%mend;

%risk_eval;

OUTPUT:

Obs	risk_level	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk	Report_Month	Review_Date	Days_To_Review
1	LOW	05JAN2025	1	UPI	GooglePay	1200	09:12	25	JAN2025	12JAN2025	7
2	HIGH	05JAN2025	2	Card	Paytm	45000	23:45	88	JAN2025	12JAN2025	7
3	LOW	06JAN2025	3	Wallet	PhonePe	3200	11:15	30	JAN2025	13JAN2025	7
4	MEDIUM	06JAN2025	4	UPI	BankApp	15000	02:30	75	JAN2025	13JAN2025	7
5	HIGH	07JAN2025	5	NetBanking	BankApp	78000	01:10	92	JAN2025	14JAN2025	7
6	MEDIUM	07JAN2025	6	Card	GooglePay	5600	14:05	40	JAN2025	14JAN2025	7
7	MEDIUM	07JAN2025	7	Wallet	Paytm	300	03:40	60	JAN2025	14JAN2025	7
8	LOW	08JAN2025	8	UPI	PhonePe	2200	19:20	20	JAN2025	15JAN2025	7
9	HIGH	08JAN2025	9	Card	BankApp	92000	00:50	95	JAN2025	15JAN2025	7
10	HIGH	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85	JAN2025	16JAN2025	7
11	LOW	09JAN2025	11	UPI	PhonePe	600	10:10	15	JAN2025	16JAN2025	7
12	LOW	10JAN2025	12	Wallet	Paytm	1800	18:30	25	JAN2025	17JAN2025	7
13	HIGH	10JAN2025	13	Card	GooglePay	75000	23:59	90	JAN2025	17JAN2025	7
14	LOW	11JAN2025	14	UPI	BankApp	1300	09:05	18	JAN2025	18JAN2025	7
15	HIGH	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89	JAN2025	18JAN2025	7

5. Using PROC SQL for Business-Level Summaries

Fraud exposure by platform

proc sql;

create table platform_risk as

select Platform,

       count(*) as Transactions,

       sum(Amount) as Total_Amount,

       avg(Fraud_Risk) as Avg_Risk

from digital_risk

group by Platform;

quit;

proc print data=platform_risk;

run;

OUTPUT:

Obs	Platform	Transactions	Total_Amount	Avg_Risk
1	BankApp	4	186300	70.0000
2	GooglePay	4	122800	60.0000
3	Paytm	3	47100	57.6667
4	PhonePe	4	61000	38.5000

6. PROC MEANS – Financial Statistics

proc means data=digital_risk mean min max sum;

    class Risk_Level;

    var Amount Fraud_Risk;

run;

OUTPUT:

The MEANS Procedure

risk_level	N Obs	Variable	Mean	Minimum	Maximum	Sum
HIGH	6	Amount Fraud_Risk	64333.33 89.8333333	41000.00 85.0000000	92000.00 95.0000000	386000.00 539.0000000
LOW	6	Amount Fraud_Risk	1716.67 22.1666667	600.0000000 15.0000000	3200.00 30.0000000	10300.00 133.0000000
MEDIUM	3	Amount Fraud_Risk	6966.67 58.3333333	300.0000000 40.0000000	15000.00 75.0000000	20900.00 175.0000000

-->Banks use this to understand:

1.Which risk band has highest money flow

2.How dangerous HIGH risk transactions are

7. PROC FREQ – Pattern Detection

proc freq data=digital_risk;

    tables Transaction_Type*Risk_Level / nocol norow;

run;

OUTPUT:

The FREQ Procedure

Frequency Percent

Table of Transaction_Type by risk_level Transaction_Type risk_level HIGH LOW MEDIUM Total Card 3 20.00 0 0.00 1 6.67 4 26.67 NetBanking 3 20.00 0 0.00 0 0.00 3 20.00 UPI 0 0.00 4 26.67 1 6.67 5 33.33 Wallet 0 0.00 2 13.33 1 6.67 3 20.00 Total 6 40.00 6 40.00 3 20.00 15 100.00

This tells us:

“Which transaction types generate most fraud?”

8. PROC SGPLOT – Visual Fraud Patterns

Amount vs Fraud Risk

proc sgplot data=digital_risk;

    scatter x=Amount y=Fraud_Risk;

    title "Transaction Amount vs Fraud Risk";

run;

OUTPUT:

-->High amounts clustering at high risk means fraud concentration.

9. MERGE – Joining Platform Risk Back

proc sort data=digital_risk; by Platform; run;

proc print data=digital_risk;

run;

OUTPUT:

Obs	risk_level	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk	Report_Month	Review_Date	Days_To_Review
1	MEDIUM	06JAN2025	4	UPI	BankApp	15000	02:30	75	JAN2025	13JAN2025	7
2	HIGH	07JAN2025	5	NetBanking	BankApp	78000	01:10	92	JAN2025	14JAN2025	7
3	HIGH	08JAN2025	9	Card	BankApp	92000	00:50	95	JAN2025	15JAN2025	7
4	LOW	11JAN2025	14	UPI	BankApp	1300	09:05	18	JAN2025	18JAN2025	7
5	LOW	05JAN2025	1	UPI	GooglePay	1200	09:12	25	JAN2025	12JAN2025	7
6	MEDIUM	07JAN2025	6	Card	GooglePay	5600	14:05	40	JAN2025	14JAN2025	7
7	HIGH	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85	JAN2025	16JAN2025	7
8	HIGH	10JAN2025	13	Card	GooglePay	75000	23:59	90	JAN2025	17JAN2025	7
9	HIGH	05JAN2025	2	Card	Paytm	45000	23:45	88	JAN2025	12JAN2025	7
10	MEDIUM	07JAN2025	7	Wallet	Paytm	300	03:40	60	JAN2025	14JAN2025	7
11	LOW	10JAN2025	12	Wallet	Paytm	1800	18:30	25	JAN2025	17JAN2025	7
12	LOW	06JAN2025	3	Wallet	PhonePe	3200	11:15	30	JAN2025	13JAN2025	7
13	LOW	08JAN2025	8	UPI	PhonePe	2200	19:20	20	JAN2025	15JAN2025	7
14	LOW	09JAN2025	11	UPI	PhonePe	600	10:10	15	JAN2025	16JAN2025	7
15	HIGH	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89	JAN2025	18JAN2025	7

proc sort data=platform_risk; by Platform; run;

proc print data=platform_risk;

run;

OUTPUT:

Obs	Platform	Transactions	Total_Amount	Avg_Risk
1	BankApp	4	186300	70.0000
2	GooglePay	4	122800	60.0000
3	Paytm	3	47100	57.6667
4	PhonePe	4	61000	38.5000

data digital_merge;

    merge digital_risk platform_risk;

    by Platform;

run;

proc print data=digital_merge;

run;

OUTPUT:

Obs	risk_level	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk	Report_Month	Review_Date	Days_To_Review	Transactions	Total_Amount	Avg_Risk
1	MEDIUM	06JAN2025	4	UPI	BankApp	15000	02:30	75	JAN2025	13JAN2025	7	4	186300	70.0000
2	HIGH	07JAN2025	5	NetBanking	BankApp	78000	01:10	92	JAN2025	14JAN2025	7	4	186300	70.0000
3	HIGH	08JAN2025	9	Card	BankApp	92000	00:50	95	JAN2025	15JAN2025	7	4	186300	70.0000
4	LOW	11JAN2025	14	UPI	BankApp	1300	09:05	18	JAN2025	18JAN2025	7	4	186300	70.0000
5	LOW	05JAN2025	1	UPI	GooglePay	1200	09:12	25	JAN2025	12JAN2025	7	4	122800	60.0000
6	MEDIUM	07JAN2025	6	Card	GooglePay	5600	14:05	40	JAN2025	14JAN2025	7	4	122800	60.0000
7	HIGH	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85	JAN2025	16JAN2025	7	4	122800	60.0000
8	HIGH	10JAN2025	13	Card	GooglePay	75000	23:59	90	JAN2025	17JAN2025	7	4	122800	60.0000
9	HIGH	05JAN2025	2	Card	Paytm	45000	23:45	88	JAN2025	12JAN2025	7	3	47100	57.6667
10	MEDIUM	07JAN2025	7	Wallet	Paytm	300	03:40	60	JAN2025	14JAN2025	7	3	47100	57.6667
11	LOW	10JAN2025	12	Wallet	Paytm	1800	18:30	25	JAN2025	17JAN2025	7	3	47100	57.6667
12	LOW	06JAN2025	3	Wallet	PhonePe	3200	11:15	30	JAN2025	13JAN2025	7	4	61000	38.5000
13	LOW	08JAN2025	8	UPI	PhonePe	2200	19:20	20	JAN2025	15JAN2025	7	4	61000	38.5000
14	LOW	09JAN2025	11	UPI	PhonePe	600	10:10	15	JAN2025	16JAN2025	7	4	61000	38.5000
15	HIGH	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89	JAN2025	18JAN2025	7	4	61000	38.5000

10. APPEND – Adding New Transactions

data new_txn;

    input Transaction_ID Transaction_Type $ Platform $ Amount Trans_Date :date9. Trans_Time $ Fraud_Risk;

    format Trans_Date date9.;

    datalines;

16 Card PhonePe 89000 12JAN2025 00:30 93

;

run;

proc print data=new_txn;

run;

OUTPUT:

Obs	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Date	Trans_Time	Fraud_Risk
1	16	Card	PhonePe	89000	12JAN2025	00:30	93

proc append base=digital_txn_raw 

            data=new_txn force;

run;

proc print data=digital_txn_raw;

run;

OUTPUT:

Obs	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk
1	05JAN2025	1	UPI	GooglePay	1200	09:12	25
2	05JAN2025	2	Card	Paytm	45000	23:45	88
3	06JAN2025	3	Wallet	PhonePe	3200	11:15	30
4	06JAN2025	4	UPI	BankApp	15000	02:30	75
5	07JAN2025	5	NetBanking	BankApp	78000	01:10	92
6	07JAN2025	6	Card	GooglePay	5600	14:05	40
7	07JAN2025	7	Wallet	Paytm	300	03:40	60
8	08JAN2025	8	UPI	PhonePe	2200	19:20	20
9	08JAN2025	9	Card	BankApp	92000	00:50	95
10	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85
11	09JAN2025	11	UPI	PhonePe	600	10:10	15
12	10JAN2025	12	Wallet	Paytm	1800	18:30	25
13	10JAN2025	13	Card	GooglePay	75000	23:59	90
14	11JAN2025	14	UPI	BankApp	1300	09:05	18
15	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89
16	12JAN2025	16	Card	PhonePe	89000	00:30	93

11. SET – Rebuilding Master Table

data digital_master;

    set digital_txn_raw;

run;

proc print data=digital_master;

run;

OUTPUT:

Obs	Trans_Date	Transaction_ID	Transaction_Type	Platform	Amount	Trans_Time	Fraud_Risk
1	05JAN2025	1	UPI	GooglePay	1200	09:12	25
2	05JAN2025	2	Card	Paytm	45000	23:45	88
3	06JAN2025	3	Wallet	PhonePe	3200	11:15	30
4	06JAN2025	4	UPI	BankApp	15000	02:30	75
5	07JAN2025	5	NetBanking	BankApp	78000	01:10	92
6	07JAN2025	6	Card	GooglePay	5600	14:05	40
7	07JAN2025	7	Wallet	Paytm	300	03:40	60
8	08JAN2025	8	UPI	PhonePe	2200	19:20	20
9	08JAN2025	9	Card	BankApp	92000	00:50	95
10	09JAN2025	10	NetBanking	GooglePay	41000	22:15	85
11	09JAN2025	11	UPI	PhonePe	600	10:10	15
12	10JAN2025	12	Wallet	Paytm	1800	18:30	25
13	10JAN2025	13	Card	GooglePay	75000	23:59	90
14	11JAN2025	14	UPI	BankApp	1300	09:05	18
15	11JAN2025	15	NetBanking	PhonePe	55000	01:40	89
16	12JAN2025	16	Card	PhonePe	89000	00:30	93

12. PROC TRANSPOSE – Risk Profile by Platform

proc sql;

    create table risk_summary as

    select

           Platform,

           Risk_Level,

           sum(Amount) as Total_Amount

    from digital_risk

    group by Platform, Risk_Level;

quit;

proc print data=risk_summary;

run;

OUTPUT:

Obs	Platform	risk_level	Total_Amount
1	BankApp	HIGH	170000
2	BankApp	LOW	1300
3	BankApp	MEDIUM	15000
4	GooglePay	HIGH	116000
5	GooglePay	LOW	1200
6	GooglePay	MEDIUM	5600
7	Paytm	HIGH	45000
8	Paytm	LOW	1800
9	Paytm	MEDIUM	300
10	PhonePe	HIGH	55000
11	PhonePe	LOW	6000

proc transpose data=risk_summary out=risk_wide(drop=_name_);

    by Platform;

    id Risk_Level;

    var Total_Amount;

run;

proc print data=risk_wide;

run;

OUTPUT:

Obs	Platform	HIGH	LOW	MEDIUM
1	BankApp	170000	1300	15000
2	GooglePay	116000	1200	5600
3	Paytm	45000	1800	300
4	PhonePe	55000	6000	.

This converts rows into columns for dashboarding.

13. Conclusion

We now built a complete Digital Transactions Fraud Analytics System using SAS using:

PROC SQL

PROC MEANS

PROC FREQ

PROC SGPLOT

MACROS

INTNX, INTCK, MDY

MERGE, APPEND, SET, TRANSPOSE

This project is suitable for:

SAS Programmer Interviews

SAS Programmer Job Seekers

SAS Analysts

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1.SPACE INVENTIONS DATASET DEVELOPMENT USING PROC SQL | PROC MEANS | PROC FREQ | PROC FORMAT | PROC SORT | PROC PRINT | PROC UNIVARIATE WITH MACROS AND DATE FUNCTIONS

2.OCEAN SHIP FUEL EFFICIENCY AND VOYAGE ANALYSIS USING PROC SQL | PROC MEANS | PROC CORR | PROC UNIVARIATE | PROC SGPLOT WITH INTCK | INTNX AND MACRO-BASED GROUPING

3.BRIDGE ANALYSIS USING PROC PRINT | PROC SQL | PROC FREQ | PROC MEANS | PROC REPORT | PROC TRANSPOSE | SAS MACROS FOR AUTOMATED BRIDGE DATA INSIGHTS