386.Can SAS Really Predict Microfinance Loan Risk and Fraud Before It Happens?

Can SAS Really Predict Microfinance Loan Risk and Fraud Before It Happens?

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HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —
DATA STEP | PROC SQL |  PROC PRINT | PROC SGPLOT | MACROS | PROC CORR | PROC MEANS | PROC FREQ | PROC UNIVARIATE | APPEND | PROC SORT | MERGE | PROC DATASETS DELETE | DATA FUNCTIONS

Table Of Contents

1. Introduction
2. Business Context
3. Dataset Creation (15+ observations with dates)
4. Data Manipulation (SET, MERGE, APPEND, TRANSPOSE)
5. Character & Numeric Functions
6. Utilization Classification Macro
7. Fraud Detection Macro
8. PROC SQL Analytics
9. PROC FREQ
10. PROC MEANS
11. PROC UNIVARIATE
12. PROC CORR
13. PROC SGPLOT
14. PROC DATASETS (Delete)

1. INTRODUCTION

Microfinance plays a critical role in financial inclusion, especially in countries like India where millions of people lack access to formal banking systems. Microfinance institutions (MFIs) provide small loans to low-income individuals for:

• Agriculture
• Small businesses
• Education
• Livelihood activities

However, MFIs face serious challenges:

• Loan defaults
• Fraudulent borrowers
• High-risk regions
• Poor repayment discipline

This project simulates a real microfinance loan system and demonstrates how SAS analytics can be used to:

• Identify high-risk customers
• Detect fraud
• Analyze loan behavior
• Support business decisions

2. BUSINESS CONTEXT 

Imagine you are a SAS Programmer at a Microfinance Company.

Your management asks:

"We have thousands of loans. Which ones are risky? Which regions default more? Are we giving loans to fraud customers?"

You are given loan data and asked to build:

• Risk classification logic
• Fraud detection model
• Analytical dashboards
• Statistical insights

This project is exactly that.

3. DATASET CREATION

data microfinance_loans;

    input Loan_ID $ Region $ Loan_Amount Interest_Rate Repayment_Duration Default_Flag 

          Loan_Date:date9.;

    format Loan_Date date9.;

    datalines;

L001 South 50000 12.5 24 0 01JAN2023

L002 North 75000 14.2 36 1 15FEB2023

L003 East 30000 10.8 12 0 10MAR2023

L004 West 120000 16.5 48 1 20APR2023

L005 South 45000 11.2 18 0 05MAY2023

L006 North 95000 17.8 60 1 11JUN2023

L007 East 38000 9.5 12 0 02JUL2023

L008 West 200000 19.2 72 1 19AUG2023

L009 South 60000 13.5 24 0 10SEP2023

L010 North 82000 15.1 36 1 21OCT2023

L011 East 28000 10.0 12 0 11NOV2023

L012 West 150000 18.4 60 1 15DEC2023

L013 South 55000 12.0 24 0 05JAN2024

L014 North 90000 16.0 48 1 22FEB2024

L015 East 35000 11.0 18 0 10MAR2024

L016 West 180000 20.0 72 1 01APR2024

;

run;

proc print data=microfinance_loans;

run;

OUTPUT:

Obs	Loan_ID	Region	Loan_Amount	Interest_Rate	Repayment_Duration	Default_Flag	Loan_Date
1	L001	South	50000	12.5	24	0	01JAN2023
2	L002	North	75000	14.2	36	1	15FEB2023
3	L003	East	30000	10.8	12	0	10MAR2023
4	L004	West	120000	16.5	48	1	20APR2023
5	L005	South	45000	11.2	18	0	05MAY2023
6	L006	North	95000	17.8	60	1	11JUN2023
7	L007	East	38000	9.5	12	0	02JUL2023
8	L008	West	200000	19.2	72	1	19AUG2023
9	L009	South	60000	13.5	24	0	10SEP2023
10	L010	North	82000	15.1	36	1	21OCT2023
11	L011	East	28000	10.0	12	0	11NOV2023
12	L012	West	150000	18.4	60	1	15DEC2023
13	L013	South	55000	12.0	24	0	05JAN2024
14	L014	North	90000	16.0	48	1	22FEB2024
15	L015	East	35000	11.0	18	0	10MAR2024
16	L016	West	180000	20.0	72	1	01APR2024

Used for row-level transformations.

Used to read dataset sequentially.

4. CHARACTER & NUMERIC FUNCTIONS

data loans_clean;

    set microfinance_loans;

    Region_Proper = propcase(strip(Region));

    Loan_Category = catx('-', Region_Proper, Loan_ID);

    Risk_Score = Loan_Amount * Interest_Rate / Repayment_Duration;

    Loan_Amount_Filled = coalesce(Loan_Amount, 0);

    Region_Upper = upcase(Region);

    Region_Lower = lowcase(Region);

run;

proc print data=loans_clean;

 var Loan_ID Region Region_Proper Loan_Category Risk_Score Loan_Amount_Filled 

     Region_Upper Region_Lower;

run;

OUTPUT:

Obs	Loan_ID	Region	Region_Proper	Loan_Category	Risk_Score	Loan_Amount_Filled	Region_Upper	Region_Lower
1	L001	South	South	South-L001	26041.67	50000	SOUTH	south
2	L002	North	North	North-L002	29583.33	75000	NORTH	north
3	L003	East	East	East-L003	27000.00	30000	EAST	east
4	L004	West	West	West-L004	41250.00	120000	WEST	west
5	L005	South	South	South-L005	28000.00	45000	SOUTH	south
6	L006	North	North	North-L006	28183.33	95000	NORTH	north
7	L007	East	East	East-L007	30083.33	38000	EAST	east
8	L008	West	West	West-L008	53333.33	200000	WEST	west
9	L009	South	South	South-L009	33750.00	60000	SOUTH	south
10	L010	North	North	North-L010	34394.44	82000	NORTH	north
11	L011	East	East	East-L011	23333.33	28000	EAST	east
12	L012	West	West	West-L012	46000.00	150000	WEST	west
13	L013	South	South	South-L013	27500.00	55000	SOUTH	south
14	L014	North	North	North-L014	30000.00	90000	NORTH	north
15	L015	East	East	East-L015	21388.89	35000	EAST	east
16	L016	West	West	West-L016	50000.00	180000	WEST	west

Used to standardize text data for joins and reports.

Used for risk calculations and scoring.

5. DATE FUNCTIONS (MDY, INTCK, INTNX)

data loans_dates;

    set loans_clean;

    Review_Date = intnx('month', Loan_Date, 6);

    Loan_Age_Months = intck('month', Loan_Date, today());

    format Review_Date date9.;

run;

proc print data=loans_dates;

run;

OUTPUT:

Obs	Loan_ID	Region	Loan_Amount	Interest_Rate	Repayment_Duration	Default_Flag	Loan_Date	Region_Proper	Loan_Category	Risk_Score	Loan_Amount_Filled	Region_Upper	Region_Lower	Review_Date	Loan_Age_Months
1	L001	South	50000	12.5	24	0	01JAN2023	South	South-L001	26041.67	50000	SOUTH	south	01JUL2023	36
2	L002	North	75000	14.2	36	1	15FEB2023	North	North-L002	29583.33	75000	NORTH	north	01AUG2023	35
3	L003	East	30000	10.8	12	0	10MAR2023	East	East-L003	27000.00	30000	EAST	east	01SEP2023	34
4	L004	West	120000	16.5	48	1	20APR2023	West	West-L004	41250.00	120000	WEST	west	01OCT2023	33
5	L005	South	45000	11.2	18	0	05MAY2023	South	South-L005	28000.00	45000	SOUTH	south	01NOV2023	32
6	L006	North	95000	17.8	60	1	11JUN2023	North	North-L006	28183.33	95000	NORTH	north	01DEC2023	31
7	L007	East	38000	9.5	12	0	02JUL2023	East	East-L007	30083.33	38000	EAST	east	01JAN2024	30
8	L008	West	200000	19.2	72	1	19AUG2023	West	West-L008	53333.33	200000	WEST	west	01FEB2024	29
9	L009	South	60000	13.5	24	0	10SEP2023	South	South-L009	33750.00	60000	SOUTH	south	01MAR2024	28
10	L010	North	82000	15.1	36	1	21OCT2023	North	North-L010	34394.44	82000	NORTH	north	01APR2024	27
11	L011	East	28000	10.0	12	0	11NOV2023	East	East-L011	23333.33	28000	EAST	east	01MAY2024	26
12	L012	West	150000	18.4	60	1	15DEC2023	West	West-L012	46000.00	150000	WEST	west	01JUN2024	25
13	L013	South	55000	12.0	24	0	05JAN2024	South	South-L013	27500.00	55000	SOUTH	south	01JUL2024	24
14	L014	North	90000	16.0	48	1	22FEB2024	North	North-L014	30000.00	90000	NORTH	north	01AUG2024	23
15	L015	East	35000	11.0	18	0	10MAR2024	East	East-L015	21388.89	35000	EAST	east	01SEP2024	22
16	L016	West	180000	20.0	72	1	01APR2024	West	West-L016	50000.00	180000	WEST	west	01OCT2024	21

Used for:

·       Loan aging

·       Review cycles

·       Compliance timelines

6. UTILIZATION CLASSIFICATION MACRO

%macro utilization;

data utilization_flag;

    set loans_dates;

length Utilization $8.;

    if Loan_Amount >= 100000 then Loan_Amount = "High";

    else if Loan_Amount >= 50000 then Utilization = "Medium";

    else Utilization = "Low";

run;

proc print data=utilization_flag;

run;

%mend;

%utilization;

OUTPUT:

Obs	Loan_ID	Region	Loan_Amount	Interest_Rate	Repayment_Duration	Default_Flag	Loan_Date	Region_Proper	Loan_Category	Risk_Score	Loan_Amount_Filled	Region_Upper	Region_Lower	Review_Date	Loan_Age_Months	Utilization
1	L001	South	50000	12.5	24	0	01JAN2023	South	South-L001	26041.67	50000	SOUTH	south	01JUL2023	36	Medium
2	L002	North	75000	14.2	36	1	15FEB2023	North	North-L002	29583.33	75000	NORTH	north	01AUG2023	35	Medium
3	L003	East	30000	10.8	12	0	10MAR2023	East	East-L003	27000.00	30000	EAST	east	01SEP2023	34	Low
4	L004	West	.	16.5	48	1	20APR2023	West	West-L004	41250.00	120000	WEST	west	01OCT2023	33
5	L005	South	45000	11.2	18	0	05MAY2023	South	South-L005	28000.00	45000	SOUTH	south	01NOV2023	32	Low
6	L006	North	95000	17.8	60	1	11JUN2023	North	North-L006	28183.33	95000	NORTH	north	01DEC2023	31	Medium
7	L007	East	38000	9.5	12	0	02JUL2023	East	East-L007	30083.33	38000	EAST	east	01JAN2024	30	Low
8	L008	West	.	19.2	72	1	19AUG2023	West	West-L008	53333.33	200000	WEST	west	01FEB2024	29
9	L009	South	60000	13.5	24	0	10SEP2023	South	South-L009	33750.00	60000	SOUTH	south	01MAR2024	28	Medium
10	L010	North	82000	15.1	36	1	21OCT2023	North	North-L010	34394.44	82000	NORTH	north	01APR2024	27	Medium
11	L011	East	28000	10.0	12	0	11NOV2023	East	East-L011	23333.33	28000	EAST	east	01MAY2024	26	Low
12	L012	West	.	18.4	60	1	15DEC2023	West	West-L012	46000.00	150000	WEST	west	01JUN2024	25
13	L013	South	55000	12.0	24	0	05JAN2024	South	South-L013	27500.00	55000	SOUTH	south	01JUL2024	24	Medium
14	L014	North	90000	16.0	48	1	22FEB2024	North	North-L014	30000.00	90000	NORTH	north	01AUG2024	23	Medium
15	L015	East	35000	11.0	18	0	10MAR2024	East	East-L015	21388.89	35000	EAST	east	01SEP2024	22	Low
16	L016	West	.	20.0	72	1	01APR2024	West	West-L016	50000.00	180000	WEST	west	01OCT2024	21

Used for:

·       Automation

·       Reusability

·       Enterprise scale processing

7. FRAUD DETECTION MACRO

%macro fraud_detection;

data fraud_flag;

    set utilization_flag;

    if Default_Flag=1 and Loan_Amount>100000 and Interest_Rate>15 then Fraud="Yes";

    else Fraud="No";

run;

proc print data=fraud_flag;

run;

%mend;

%fraud_detection;

OUTPUT:

Obs	Loan_ID	Region	Loan_Amount	Interest_Rate	Repayment_Duration	Default_Flag	Loan_Date	Region_Proper	Loan_Category	Risk_Score	Loan_Amount_Filled	Region_Upper	Region_Lower	Review_Date	Loan_Age_Months	Utilization	Fraud
1	L001	South	50000	12.5	24	0	01JAN2023	South	South-L001	26041.67	50000	SOUTH	south	01JUL2023	36	Medium	No
2	L002	North	75000	14.2	36	1	15FEB2023	North	North-L002	29583.33	75000	NORTH	north	01AUG2023	35	Medium	No
3	L003	East	30000	10.8	12	0	10MAR2023	East	East-L003	27000.00	30000	EAST	east	01SEP2023	34	Low	No
4	L004	West	.	16.5	48	1	20APR2023	West	West-L004	41250.00	120000	WEST	west	01OCT2023	33		No
5	L005	South	45000	11.2	18	0	05MAY2023	South	South-L005	28000.00	45000	SOUTH	south	01NOV2023	32	Low	No
6	L006	North	95000	17.8	60	1	11JUN2023	North	North-L006	28183.33	95000	NORTH	north	01DEC2023	31	Medium	No
7	L007	East	38000	9.5	12	0	02JUL2023	East	East-L007	30083.33	38000	EAST	east	01JAN2024	30	Low	No
8	L008	West	.	19.2	72	1	19AUG2023	West	West-L008	53333.33	200000	WEST	west	01FEB2024	29		No
9	L009	South	60000	13.5	24	0	10SEP2023	South	South-L009	33750.00	60000	SOUTH	south	01MAR2024	28	Medium	No
10	L010	North	82000	15.1	36	1	21OCT2023	North	North-L010	34394.44	82000	NORTH	north	01APR2024	27	Medium	No
11	L011	East	28000	10.0	12	0	11NOV2023	East	East-L011	23333.33	28000	EAST	east	01MAY2024	26	Low	No
12	L012	West	.	18.4	60	1	15DEC2023	West	West-L012	46000.00	150000	WEST	west	01JUN2024	25		No
13	L013	South	55000	12.0	24	0	05JAN2024	South	South-L013	27500.00	55000	SOUTH	south	01JUL2024	24	Medium	No
14	L014	North	90000	16.0	48	1	22FEB2024	North	North-L014	30000.00	90000	NORTH	north	01AUG2024	23	Medium	No
15	L015	East	35000	11.0	18	0	10MAR2024	East	East-L015	21388.89	35000	EAST	east	01SEP2024	22	Low	No
16	L016	West	.	20.0	72	1	01APR2024	West	West-L016	50000.00	180000	WEST	west	01OCT2024	21		No

8. PROC SQL ANALYTICS

proc sql;

    select Region,

           count(*) as Total_Loans,

           avg(Loan_Amount) as Avg_Loan,

           sum(Default_Flag) as Defaults

    from fraud_flag

    group by Region;

quit;

OUTPUT:

Region	Total_Loans	Avg_Loan	Defaults
East	4	32750	0
North	4	85500	4
South	4	52500	0
West	4	.	4

Used for:

·       Business aggregation

·       Executive dashboards

9. PROC FREQ

proc freq data=fraud_flag;

    tables Region*Fraud / nocum;

run;

OUTPUT:

The FREQ Procedure

Frequency Percent Row Pct Col Pct

Table of Region by Fraud Region Fraud No Total East 4 25.00 100.00 25.00 4 25.00     North 4 25.00 100.00 25.00 4 25.00     South 4 25.00 100.00 25.00 4 25.00     West 4 25.00 100.00 25.00 4 25.00     Total 16 100.00 16 100.00

Used for:

·       Fraud pattern detection

·       Risk segmentation

10. PROC MEANS

proc means data=fraud_flag mean min max std;

    var Loan_Amount Interest_Rate Risk_Score;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum	Std Dev
Loan_Amount Interest_Rate Risk_Score	56916.67 14.2312500 33115.10	28000.00 9.5000000 21388.89	95000.00 20.0000000 53333.33	23554.42 3.4381621 9529.30

Used for:

·       Portfolio health

·       Exposure analysis

11. PROC UNIVARIATE

proc univariate data=fraud_flag;

    var Loan_Amount;

    histogram Loan_Amount;

run;

OUTPUT:

The UNIVARIATE Procedure

Variable: Loan_Amount

Moments
N	12	Sum Weights	12
Mean	56916.6667	Sum Observations	683000
Std Deviation	23554.418	Variance	554810606
Skewness	0.41873121	Kurtosis	-1.2640358
Uncorrected SS	4.4977E10	Corrected SS	6102916667
Coeff Variation	41.3840433	Std Error Mean	6799.57478

Basic Statistical Measures
Location		Variability
Mean	56916.67	Std Deviation	23554
Median	52500.00	Variance	554810606
Mode	.	Range	67000
		Interquartile Range	42000

Tests for Location: Mu0=0
Test	Statistic		p Value
Student's t	t	8.370621	Pr > |t|	<.0001
Sign	M	6	Pr >= |M|	0.0005
Signed Rank	S	39	Pr >= |S|	0.0005

Quantiles (Definition 5)
Level	Quantile
100% Max	95000
99%	95000
95%	95000
90%	90000
75% Q3	78500
50% Median	52500
25% Q1	36500
10%	30000
5%	28000
1%	28000
0% Min	28000

Extreme Observations
Lowest		Highest
Value	Obs	Value	Obs
28000	11	60000	9
30000	3	75000	2
35000	15	82000	10
38000	7	90000	14
45000	5	95000	6

Missing Values
Missing Value	Count	Percent Of
		All Obs	Missing Obs
.	4	25.00	100.00

The UNIVARIATE Procedure

Used for:

·       Outlier detection

·       Distribution analysis

12. PROC CORR

proc corr data=fraud_flag;

    var Loan_Amount Interest_Rate Risk_Score Default_Flag;

run;

OUTPUT:

The CORR Procedure

4 Variables:	Loan_Amount Interest_Rate Risk_Score Default_Flag

Simple Statistics
Variable	N	Mean	Std Dev	Sum	Minimum	Maximum
Loan_Amount	12	56917	23554	683000	28000	95000
Interest_Rate	16	14.23125	3.43816	227.70000	9.50000	20.00000
Risk_Score	16	33115	9529	529842	21389	53333
Default_Flag	16	0.50000	0.51640	8.00000	0	1.00000

Pearson Correlation Coefficients Prob > |r| under H0: Rho=0 Number of Observations
	Loan_Amount	Interest_Rate	Risk_Score	Default_Flag
Loan_Amount	1.00000   12	0.96508 <.0001 12	0.57848 0.0488 12	0.89623 <.0001 12
Interest_Rate	0.96508 <.0001 12	1.00000   16	0.81914 0.0001 16	0.87677 <.0001 16
Risk_Score	0.57848 0.0488 12	0.81914 0.0001 16	1.00000   16	0.64790 0.0066 16
Default_Flag	0.89623 <.0001 12	0.87677 <.0001 16	0.64790 0.0066 16	1.00000   16

Used for:

·       Understanding relationships

·       Model building inputs

13. PROC SGPLOT

proc sgplot data=fraud_flag;

    vbar Region / response=Loan_Amount stat=mean;

run;

OUTPUT:

Used for:

·       Management visualization

·       KPI dashboards

14. TRANSPOSE, MERGE, APPEND

proc transpose data=fraud_flag out=loan_t;

    var Loan_Amount Interest_Rate;

run;

proc print data=loan_t;

run;

OUTPUT:

Obs	_NAME_	COL1	COL2	COL3	COL4	COL5	COL6	COL7	COL8	COL9	COL10	COL11	COL12	COL13	COL14	COL15	COL16
1	Loan_Amount	50000.0	75000.0	30000.0	.	45000.0	95000.0	38000.0	.	60000.0	82000.0	28000	.	55000	90000	35000	.
2	Interest_Rate	12.5	14.2	10.8	16.5	11.2	17.8	9.5	19.2	13.5	15.1	10	18.4	12	16	11	20

proc append base=microfinance_loans 

            data=fraud_flag force;

run;

proc print data=microfinance_loans;

run;

OUTPUT:

Obs	Loan_ID	Region	Loan_Amount	Interest_Rate	Repayment_Duration	Default_Flag	Loan_Date
1	L001	South	50000	12.5	24	0	01JAN2023
2	L002	North	75000	14.2	36	1	15FEB2023
3	L003	East	30000	10.8	12	0	10MAR2023
4	L004	West	120000	16.5	48	1	20APR2023
5	L005	South	45000	11.2	18	0	05MAY2023
6	L006	North	95000	17.8	60	1	11JUN2023
7	L007	East	38000	9.5	12	0	02JUL2023
8	L008	West	200000	19.2	72	1	19AUG2023
9	L009	South	60000	13.5	24	0	10SEP2023
10	L010	North	82000	15.1	36	1	21OCT2023
11	L011	East	28000	10.0	12	0	11NOV2023
12	L012	West	150000	18.4	60	1	15DEC2023
13	L013	South	55000	12.0	24	0	05JAN2024
14	L014	North	90000	16.0	48	1	22FEB2024
15	L015	East	35000	11.0	18	0	10MAR2024
16	L016	West	180000	20.0	72	1	01APR2024
17	L001	South	50000	12.5	24	0	01JAN2023
18	L002	North	75000	14.2	36	1	15FEB2023
19	L003	East	30000	10.8	12	0	10MAR2023
20	L004	West	.	16.5	48	1	20APR2023
21	L005	South	45000	11.2	18	0	05MAY2023
22	L006	North	95000	17.8	60	1	11JUN2023
23	L007	East	38000	9.5	12	0	02JUL2023
24	L008	West	.	19.2	72	1	19AUG2023
25	L009	South	60000	13.5	24	0	10SEP2023
26	L010	North	82000	15.1	36	1	21OCT2023
27	L011	East	28000	10.0	12	0	11NOV2023
28	L012	West	.	18.4	60	1	15DEC2023
29	L013	South	55000	12.0	24	0	05JAN2024
30	L014	North	90000	16.0	48	1	22FEB2024
31	L015	East	35000	11.0	18	0	10MAR2024
32	L016	West	.	20.0	72	1	01APR2024

Used to add new data into historical tables.

proc sort data=microfinance_loans;by Loan_ID;run;

proc print data=microfinance_loans;

run;

OUTPUT:

Obs	Loan_ID	Region	Loan_Amount	Interest_Rate	Repayment_Duration	Default_Flag	Loan_Date
1	L001	South	50000	12.5	24	0	01JAN2023
2	L001	South	50000	12.5	24	0	01JAN2023
3	L002	North	75000	14.2	36	1	15FEB2023
4	L002	North	75000	14.2	36	1	15FEB2023
5	L003	East	30000	10.8	12	0	10MAR2023
6	L003	East	30000	10.8	12	0	10MAR2023
7	L004	West	120000	16.5	48	1	20APR2023
8	L004	West	.	16.5	48	1	20APR2023
9	L005	South	45000	11.2	18	0	05MAY2023
10	L005	South	45000	11.2	18	0	05MAY2023
11	L006	North	95000	17.8	60	1	11JUN2023
12	L006	North	95000	17.8	60	1	11JUN2023
13	L007	East	38000	9.5	12	0	02JUL2023
14	L007	East	38000	9.5	12	0	02JUL2023
15	L008	West	200000	19.2	72	1	19AUG2023
16	L008	West	.	19.2	72	1	19AUG2023
17	L009	South	60000	13.5	24	0	10SEP2023
18	L009	South	60000	13.5	24	0	10SEP2023
19	L010	North	82000	15.1	36	1	21OCT2023
20	L010	North	82000	15.1	36	1	21OCT2023
21	L011	East	28000	10.0	12	0	11NOV2023
22	L011	East	28000	10.0	12	0	11NOV2023
23	L012	West	150000	18.4	60	1	15DEC2023
24	L012	West	.	18.4	60	1	15DEC2023
25	L013	South	55000	12.0	24	0	05JAN2024
26	L013	South	55000	12.0	24	0	05JAN2024
27	L014	North	90000	16.0	48	1	22FEB2024
28	L014	North	90000	16.0	48	1	22FEB2024
29	L015	East	35000	11.0	18	0	10MAR2024
30	L015	East	35000	11.0	18	0	10MAR2024
31	L016	West	180000	20.0	72	1	01APR2024
32	L016	West	.	20.0	72	1	01APR2024

proc sort data=fraud_flag;by Loan_ID;run;

proc print data=fraud_flag;

run;

OUTPUT:

Obs	Loan_ID	Region	Loan_Amount	Interest_Rate	Repayment_Duration	Default_Flag	Loan_Date	Region_Proper	Loan_Category	Risk_Score	Loan_Amount_Filled	Region_Upper	Region_Lower	Review_Date	Loan_Age_Months	Utilization	Fraud
1	L001	South	50000	12.5	24	0	01JAN2023	South	South-L001	26041.67	50000	SOUTH	south	01JUL2023	36	Medium	No
2	L002	North	75000	14.2	36	1	15FEB2023	North	North-L002	29583.33	75000	NORTH	north	01AUG2023	35	Medium	No
3	L003	East	30000	10.8	12	0	10MAR2023	East	East-L003	27000.00	30000	EAST	east	01SEP2023	34	Low	No
4	L004	West	.	16.5	48	1	20APR2023	West	West-L004	41250.00	120000	WEST	west	01OCT2023	33		No
5	L005	South	45000	11.2	18	0	05MAY2023	South	South-L005	28000.00	45000	SOUTH	south	01NOV2023	32	Low	No
6	L006	North	95000	17.8	60	1	11JUN2023	North	North-L006	28183.33	95000	NORTH	north	01DEC2023	31	Medium	No
7	L007	East	38000	9.5	12	0	02JUL2023	East	East-L007	30083.33	38000	EAST	east	01JAN2024	30	Low	No
8	L008	West	.	19.2	72	1	19AUG2023	West	West-L008	53333.33	200000	WEST	west	01FEB2024	29		No
9	L009	South	60000	13.5	24	0	10SEP2023	South	South-L009	33750.00	60000	SOUTH	south	01MAR2024	28	Medium	No
10	L010	North	82000	15.1	36	1	21OCT2023	North	North-L010	34394.44	82000	NORTH	north	01APR2024	27	Medium	No
11	L011	East	28000	10.0	12	0	11NOV2023	East	East-L011	23333.33	28000	EAST	east	01MAY2024	26	Low	No
12	L012	West	.	18.4	60	1	15DEC2023	West	West-L012	46000.00	150000	WEST	west	01JUN2024	25		No
13	L013	South	55000	12.0	24	0	05JAN2024	South	South-L013	27500.00	55000	SOUTH	south	01JUL2024	24	Medium	No
14	L014	North	90000	16.0	48	1	22FEB2024	North	North-L014	30000.00	90000	NORTH	north	01AUG2024	23	Medium	No
15	L015	East	35000	11.0	18	0	10MAR2024	East	East-L015	21388.89	35000	EAST	east	01SEP2024	22	Low	No
16	L016	West	.	20.0	72	1	01APR2024	West	West-L016	50000.00	180000	WEST	west	01OCT2024	21		No

data merged_data;

    merge microfinance_loans

          fraud_flag;

    by Loan_ID;

run;

proc print data=fraud_flag;

run;

OUTPUT:

Obs	Loan_ID	Region	Loan_Amount	Interest_Rate	Repayment_Duration	Default_Flag	Loan_Date	Region_Proper	Loan_Category	Risk_Score	Loan_Amount_Filled	Region_Upper	Region_Lower	Review_Date	Loan_Age_Months	Utilization	Fraud
1	L001	South	50000	12.5	24	0	01JAN2023	South	South-L001	26041.67	50000	SOUTH	south	01JUL2023	36	Medium	No
2	L002	North	75000	14.2	36	1	15FEB2023	North	North-L002	29583.33	75000	NORTH	north	01AUG2023	35	Medium	No
3	L003	East	30000	10.8	12	0	10MAR2023	East	East-L003	27000.00	30000	EAST	east	01SEP2023	34	Low	No
4	L004	West	.	16.5	48	1	20APR2023	West	West-L004	41250.00	120000	WEST	west	01OCT2023	33		No
5	L005	South	45000	11.2	18	0	05MAY2023	South	South-L005	28000.00	45000	SOUTH	south	01NOV2023	32	Low	No
6	L006	North	95000	17.8	60	1	11JUN2023	North	North-L006	28183.33	95000	NORTH	north	01DEC2023	31	Medium	No
7	L007	East	38000	9.5	12	0	02JUL2023	East	East-L007	30083.33	38000	EAST	east	01JAN2024	30	Low	No
8	L008	West	.	19.2	72	1	19AUG2023	West	West-L008	53333.33	200000	WEST	west	01FEB2024	29		No
9	L009	South	60000	13.5	24	0	10SEP2023	South	South-L009	33750.00	60000	SOUTH	south	01MAR2024	28	Medium	No
10	L010	North	82000	15.1	36	1	21OCT2023	North	North-L010	34394.44	82000	NORTH	north	01APR2024	27	Medium	No
11	L011	East	28000	10.0	12	0	11NOV2023	East	East-L011	23333.33	28000	EAST	east	01MAY2024	26	Low	No
12	L012	West	.	18.4	60	1	15DEC2023	West	West-L012	46000.00	150000	WEST	west	01JUN2024	25		No
13	L013	South	55000	12.0	24	0	05JAN2024	South	South-L013	27500.00	55000	SOUTH	south	01JUL2024	24	Medium	No
14	L014	North	90000	16.0	48	1	22FEB2024	North	North-L014	30000.00	90000	NORTH	north	01AUG2024	23	Medium	No
15	L015	East	35000	11.0	18	0	10MAR2024	East	East-L015	21388.89	35000	EAST	east	01SEP2024	22	Low	No
16	L016	West	.	20.0	72	1	01APR2024	West	West-L016	50000.00	180000	WEST	west	01OCT2024	21		No

Used to combine datasets by key.

15. PROC DATASETS DELETE

proc datasets library=work;

    delete loan_t;

quit;

LOG:

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

Used for:

·       Memory optimization

·       Housekeeping in production

HOW THIS HELPS IN INTERVIEWS

This single project covers:

Topic	Covered
Base SAS	Yes
SQL	Yes
Macros	Yes
Dates	Yes
Statistics	Yes
Business Logic	Yes
Fraud Detection	Yes
Risk Modeling	Yes
Visualization	Yes
Data Engineering	Yes

Conclusion

This project clearly shows how SAS can be used as a powerful business tool, not just a programming language. By using a simple microfinance loan dataset, we were able to simulate real-world problems like loan risk, customer default, and fraud detection.

Through procedures like PROC SQL, PROC MEANS, PROC FREQ, PROC UNIVARIATE, PROC CORR, and PROC SGPLOT, we converted raw loan data into meaningful business insights. The use of macros helped automate complex logic, making the system reusable and scalable for thousands of records in real companies.

In simple words, this project proves that:

·       SAS helps identify risky customers early

·       SAS supports data-driven lending decisions

·       SAS reduces financial loss and fraud

·       SAS improves portfolio performance

INTERVIEW QUESTIONS FOR YOU

·  What is a macro variable?

·  What is the difference between %LET and CALL SYMPUT?

·  What is the difference between SYMGET and SYMPUT?

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 Microfinanace Loan 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 and smart cities

·  EV and energy industry professionals

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