Numbers Don’t Lie—Until Dates Do: Rebuilding Accuracy in Financial Data

Global Loans, Broken Dates & Hidden Risks: Mastering SAS Data Cleaning for Reliable Analytics

1. Introduction – When Data Lies, Decisions Collapse

Imagine you’re working on a global financial dataset tracking loans issued by top countries. Everything looks fine until you discover that some loan dates are “2023-15-45”, others are “NULL”, and a few are even in future centuries.

Now picture this in a clinical trial instead of finance. A patient’s treatment start date is recorded incorrectly. That single error can misalign treatment timelines, skew efficacy analysis, and potentially mislead regulatory submissions.

This is the silent danger of bad data.

In the real world, datasets are messy:

• Missing values creep in
• Dates get corrupted
• Duplicate records inflate metrics
• Text fields become inconsistent

And the worst part? These issues don’t throw errors they quietly distort your insights.

This is where SAS (especially in regulated domains like clinical trials) and R (for flexible data manipulation) become powerful allies. They help transform chaotic datasets into analysis-ready, audit-compliant data.

In this blog, we’ll walk through a “Top Countries Loans” dataset, intentionally inject errors, and systematically clean it using SAS and R focusing especially on handling invalid dates, a critical and often overlooked problem.

2. Raw Data Creation in SAS and R

We create a dataset with intentional issues:

• Invalid dates
• Missing values
• Duplicates
• Inconsistent text

SAS Code – Raw Dataset Creation

DATA loans_raw;

INPUT Country $ Loan_ID Loan_Amount Loan_Date:$10. Approval_Date:$10. 

      Interest_Rate Duration Borrower_Count Status $;

DATALINES;

India 101 500000 2023-01-15 2023-01-20 7.5 5 100 Approved

USA 102 800000 2023-15-45 2023-02-01 6.5 10 200 Approved

china 103 . 2022-12-10 2022-12-15 5.5 8 150 NULL

India 101 500000 2023-01-15 2023-01-20 7.5 5 100 Approved

UK 104 -300000 2023-03-12 2023-03-18 6.0 7 120 Approved

Germany 105 700000 NULL 2023-04-01 5.0 6 130 Approved

France 106 650000 2023-02-30 2023-03-05 5.8 9 140 Approved

Brazil 107 400000 2023-05-10 2023-05-15 6.2 4 110 Approved

Japan 108 900000 2023-06-01 2023-06-05 4.5 12 180 Approved

India 109 300000 2023-07-20 2023-07-25 7.0 3 90 Approved

;

RUN;

PROC PRINT DATA = loans_raw;

RUN;

OUTPUT:

Obs	Country	Loan_ID	Loan_Amount	Loan_Date	Approval_Date	Interest_Rate	Duration	Borrower_Count	Status
1	India	101	500000	2023-01-15	2023-01-20	7.5	5	100	Approved
2	USA	102	800000	2023-15-45	2023-02-01	6.5	10	200	Approved
3	china	103	.	2022-12-10	2022-12-15	5.5	8	150	NULL
4	India	101	500000	2023-01-15	2023-01-20	7.5	5	100	Approved
5	UK	104	-300000	2023-03-12	2023-03-18	6.0	7	120	Approved
6	Germany	105	700000	NULL	2023-04-01	5.0	6	130	Approved
7	France	106	650000	2023-02-30	2023-03-05	5.8	9	140	Approved
8	Brazil	107	400000	2023-05-10	2023-05-15	6.2	4	110	Approved
9	Japan	108	900000	2023-06-01	2023-06-05	4.5	12	180	Approved
10	India	109	300000	2023-07-20	2023-07-25	7.0	3	90	Approved

Explanation & Key Points

This dataset intentionally mimics real-world inconsistencies:

• 2023-15-45 → invalid date
• 2023-02-30 → non-existent date
• NULL values → missing data
• Duplicate record (India 101)
• Negative loan amount (-300000)
• Inconsistent casing (“china”)

In financial or clinical datasets, such issues can break downstream derivations (e.g., duration calculations, exposure windows). SAS reads everything as raw input, but does not validate correctness automatically, making manual cleaning essential.

R Code – Equivalent Raw Dataset

loans_raw <- data.frame(

  Country = c("India","USA","china","India","UK","Germany",

              "France","Brazil","Japan","India"),

  Loan_ID = c(101,102,103,101,104,105,106,107,108,109),

  Loan_Amount = c(500000,800000,NA,500000,-300000,700000,

                  650000,400000,900000,300000),

  Loan_Date = c("2023-01-15","2023-15-45","2022-12-10",

                "2023-01-15","2023-03-12","NULL","2023-02-30",

                "2023-05-10","2023-06-01","2023-07-20"),

  Approval_Date = c("2023-01-20","2023-02-01","2022-12-15",

                    "2023-01-20","2023-03-18","2023-04-01",

                    "2023-03-05","2023-05-15","2023-06-05",

                    "2023-07-25"),

  Interest_Rate = c(7.5,6.5,5.5,7.5,6.0,5.0,5.8,6.2,4.5,7.0),

  Duration = c(5,10,8,5,7,6,9,4,12,3),

  Borrower_Count = c(100,200,150,100,120,130,140,110,180,90),

  Status = c("Approved","Approved","NULL","Approved","Approved",

             "Approved","Approved","Approved","Approved","Approved")

)

OUTPUT:

	Country	Loan_ID	Loan_Amount	Loan_Date	Approval_Date	Interest_Rate	Duration	Borrower_Count	Status
1	India	101	500000	15-01-2023	20-01-2023	7.5	5	100	Approved
2	USA	102	800000	2023-15-45	01-02-2023	6.5	10	200	Approved
3	china	103	NA	10-12-2022	15-12-2022	5.5	8	150	NULL
4	India	101	500000	15-01-2023	20-01-2023	7.5	5	100	Approved
5	UK	104	-300000	12-03-2023	18-03-2023	6	7	120	Approved
6	Germany	105	700000	NULL	01-04-2023	5	6	130	Approved
7	France	106	650000	2023-02-30	05-03-2023	5.8	9	140	Approved
8	Brazil	107	400000	10-05-2023	15-05-2023	6.2	4	110	Approved
9	Japan	108	900000	01-06-2023	05-06-2023	4.5	12	180	Approved
10	India	109	300000	20-07-2023	25-07-2023	7	3	90	Approved

Explanation & Key Points

R allows flexible dataset creation but does not enforce strict data validation. Invalid dates remain as strings, and missing values (NA, "NULL") coexist. This flexibility is powerful but risky without cleaning, analysis functions may silently fail or produce incorrect outputs.

3. Phase 1: Data Cleaning in SAS

SAS Cleaning Code

DATA loans_clean;

SET loans_raw;

Country = UPCASE(STRIP(Country));

IF Status IN ("", "NULL", ".", "NA") THEN 

   Status = "UNKNOWN";

IF Loan_Amount < 0 THEN Loan_Amount = ABS(Loan_Amount);

IF Loan_Date = "NULL" THEN Loan_Date = "";

Loan_Date_num = INPUT(Loan_Date,??YYMMDD10.);

Approval_Date_num = INPUT(Approval_Date,??YYMMDD10.);

FORMAT Loan_Date_num Approval_Date_num DATE9.;

IF Loan_Date_num = . THEN Loan_Date_num = Approval_Date_num;

RUN;

PROC PRINT DATA = loans_clean;

RUN;

OUTPUT:

Obs	Country	Loan_ID	Loan_Amount	Loan_Date	Approval_Date	Interest_Rate	Duration	Borrower_Count	Status	Loan_Date_num	Approval_Date_num
1	INDIA	101	500000	2023-01-15	2023-01-20	7.5	5	100	Approved	15JAN2023	20JAN2023
2	USA	102	800000	2023-15-45	2023-02-01	6.5	10	200	Approved	01FEB2023	01FEB2023
3	CHINA	103	.	2022-12-10	2022-12-15	5.5	8	150	UNKNOWN	10DEC2022	15DEC2022
4	INDIA	101	500000	2023-01-15	2023-01-20	7.5	5	100	Approved	15JAN2023	20JAN2023
5	UK	104	300000	2023-03-12	2023-03-18	6.0	7	120	Approved	12MAR2023	18MAR2023
6	GERMANY	105	700000		2023-04-01	5.0	6	130	Approved	01APR2023	01APR2023
7	FRANCE	106	650000	2023-02-30	2023-03-05	5.8	9	140	Approved	05MAR2023	05MAR2023
8	BRAZIL	107	400000	2023-05-10	2023-05-15	6.2	4	110	Approved	10MAY2023	15MAY2023
9	JAPAN	108	900000	2023-06-01	2023-06-05	4.5	12	180	Approved	01JUN2023	05JUN2023
10	INDIA	109	300000	2023-07-20	2023-07-25	7.0	3	90	Approved	20JUL2023	25JUL2023

PROC SORT DATA=loans_clean NODUPKEY;

BY Loan_ID;

RUN;

PROC PRINT DATA = loans_clean;

RUN;

OUTPUT:

Obs	Country	Loan_ID	Loan_Amount	Loan_Date	Approval_Date	Interest_Rate	Duration	Borrower_Count	Status	Loan_Date_num	Approval_Date_num
1	INDIA	101	500000	2023-01-15	2023-01-20	7.5	5	100	Approved	15JAN2023	20JAN2023
2	USA	102	800000	2023-15-45	2023-02-01	6.5	10	200	Approved	01FEB2023	01FEB2023
3	CHINA	103	.	2022-12-10	2022-12-15	5.5	8	150	UNKNOWN	10DEC2022	15DEC2022
4	UK	104	300000	2023-03-12	2023-03-18	6.0	7	120	Approved	12MAR2023	18MAR2023
5	GERMANY	105	700000		2023-04-01	5.0	6	130	Approved	01APR2023	01APR2023
6	FRANCE	106	650000	2023-02-30	2023-03-05	5.8	9	140	Approved	05MAR2023	05MAR2023
7	BRAZIL	107	400000	2023-05-10	2023-05-15	6.2	4	110	Approved	10MAY2023	15MAY2023
8	JAPAN	108	900000	2023-06-01	2023-06-05	4.5	12	180	Approved	01JUN2023	05JUN2023
9	INDIA	109	300000	2023-07-20	2023-07-25	7.0	3	90	Approved	20JUL2023	25JUL2023

Explanation & Key Points

This is where SAS shines structured, auditable cleaning:

• UPCASE + STRIP standardizes text
• COALESCEC replaces missing character values
• ABS() corrects invalid numeric values
• INPUT() converts strings to proper SAS dates
• ?? modifier If conversion fails, don’t throw an error
• Invalid dates become missing (.), enabling detection
• Imputation: missing Loan_Date replaced with Approval_Date
• PROC SORT NODUPKEY removes duplicates

SAS enforces strict type conversion, making it ideal for regulated environments (CDISC/SDTM).

4. Phase 2: Data Cleaning in R

library(dplyr)

loans_clean <- loans_raw %>%

  mutate(

    Country = toupper(trimws(Country)),

    Status = ifelse(is.na(Status) | Status=="NULL","UNKNOWN",Status),

    Loan_Amount = ifelse(!is.na(Loan_Amount) & Loan_Amount < 0, abs(Loan_Amount), Loan_Amount),

    Loan_Date = as.Date(Loan_Date, origin = "1960-01-01"),

    Approval_Date = as.Date(Approval_Date),

    Loan_Date = if_else(is.na(Loan_Date), Approval_Date, Loan_Date)

  ) %>%

  distinct(Loan_ID, .keep_all = TRUE)

OUTPUT:

	Country	Loan_ID	Loan_Amount	Loan_Date	Approval_Date	Interest_Rate	Duration	Borrower_Count	Status
1	INDIA	101	500000	15-01-2023	20-01-2023	7.5	5	100	Approved
2	USA	102	800000	01-02-2023	01-02-2023	6.5	10	200	Approved
3	CHINA	103	NA	10-12-2022	15-12-2022	5.5	8	150	UNKNOWN
4	UK	104	300000	12-03-2023	18-03-2023	6	7	120	Approved
5	GERMANY	105	700000	01-04-2023	01-04-2023	5	6	130	Approved
6	FRANCE	106	650000	05-03-2023	05-03-2023	5.8	9	140	Approved
7	BRAZIL	107	400000	10-05-2023	15-05-2023	6.2	4	110	Approved
8	JAPAN	108	900000	01-06-2023	05-06-2023	4.5	12	180	Approved
9	INDIA	109	300000	20-07-2023	25-07-2023	7	3	90	Approved

Explanation & Key Points

R provides flexible transformation pipelines:

• mutate() for transformation
• as.Date() automatically flags invalid dates as NA
• origin  for numeric dates
• if_else()Preserves Date type
• distinct() removes duplicates

Unlike SAS, R requires more manual control but offers speed and flexibility, especially for exploratory analysis.

5. Phase 3: Additional SAS Processing

PROC MEANS DATA=loans_clean;

VAR Loan_Amount Interest_Rate;

RUN;

OUTPUT:

The MEANS Procedure

Variable	N	Mean	Std Dev	Minimum	Maximum
Loan_Amount Interest_Rate	8 9	568750.00 6.0000000	228250.33 0.9407444	300000.00 4.5000000	900000.00 7.5000000

PROC FREQ DATA=loans_clean;

TABLES Country Status;

RUN;

OUTPUT:

The FREQ Procedure

Country	Frequency	Percent	Cumulative Frequency	Cumulative Percent
BRAZIL	1	11.11	1	11.11
CHINA	1	11.11	2	22.22
FRANCE	1	11.11	3	33.33
GERMANY	1	11.11	4	44.44
INDIA	2	22.22	6	66.67
JAPAN	1	11.11	7	77.78
UK	1	11.11	8	88.89
USA	1	11.11	9	100.00

Status	Frequency	Percent	Cumulative Frequency	Cumulative Percent
Approved	8	88.89	8	88.89
UNKNOWN	1	11.11	9	100.00

PROC SQL;

SELECT Country, SUM(Loan_Amount) AS Total_Loan

FROM loans_clean

GROUP BY Country;

QUIT;

OUTPUT:

Country	Total_Loan
BRAZIL	400000
CHINA	.
FRANCE	650000
GERMANY	700000
INDIA	800000
JAPAN	900000
UK	300000
USA	800000

Explanation & Key Points

These procedures validate cleaned data:

• PROC MEANS → detects anomalies in numeric values
• PROC FREQ → identifies unexpected categories
• PROC SQL → aggregates business insights

This step ensures data is not just clean but analytically trustworthy.

6. 20 Data Cleaning Best Practices

1. Always validate date formats before conversion
2. Use controlled terminology (CDISC standards)
3. Maintain audit trails for transformations
4. Never overwrite raw data
5. Use metadata-driven programming
6. Validate ranges (age, amount, etc.)
7. Use PROC COMPARE for QC
8. Ensure referential integrity across datasets
9. Standardize variable naming conventions
10. Log all transformations
11. Avoid hardcoding logic
12. Validate against SAP specifications
13. Implement duplicate checks
14. Use macros for reusability
15. Perform cross-domain consistency checks
16. Track missing value patterns
17. Use validation reports
18. Follow regulatory guidelines (FDA/EMA)
19. Implement version control
20. Conduct peer reviews

7. Business Logic Behind Data Cleaning

Data cleaning is not just technical it’s deeply tied to business logic. Missing values are often replaced to preserve analytical continuity. For example, if a loan date is missing but approval date exists, using the approval date ensures timeline consistency.

Unrealistic values like negative loan amounts are corrected because they violate domain logic. In clinical trials, a negative patient age is impossible; similarly, in finance, a negative loan disbursement is invalid unless explicitly defined.

Date imputation is critical. If treatment start dates are incorrect, efficacy endpoints shift. In financial analytics, incorrect loan timelines distort repayment schedules and risk models.

Salary normalization (in HR datasets) or interest rate standardization ensures comparability across regions. Without this, models produce biased outputs.

Ultimately, every cleaning decision must align with real-world logic, not just code correctness. Poor cleaning leads to flawed insights, which can result in financial loss, regulatory rejection, or incorrect scientific conclusions.

8. 20 Key Insights

• Dirty data leads to wrong conclusions
• Dates are the backbone of timelines
• Missing data must be handled logically
• Standardization ensures reproducibility
• Duplicate records inflate metrics
• Validation is not optional
• Always keep raw data intact
• Audit trails are critical in clinical trials
• Automation reduces human error
• Text inconsistencies break joins
• Date formats must be consistent
• Outliers must be investigated, not ignored
• SAS ensures regulatory compliance
• R accelerates exploratory analysis
• Cleaning improves model accuracy
• Business rules guide transformations
• Always validate after cleaning
• Data quality defines analytics quality
• Small errors create big risks
• Clean data builds trust

9. Summary

This blog demonstrated how a seemingly simple Top Countries Loans dataset can become analytically unreliable due to issues like invalid dates, duplicates, and inconsistent values. Through structured cleaning in SAS and flexible transformations in R, we converted messy data into a reliable dataset.

SAS provides a robust, audit-friendly framework ideal for regulated environments such as clinical trials. Its strict typing and procedural clarity make it highly reliable for production-grade data pipelines. Functions like INPUT, COALESCEC, and PROC SORT enable systematic validation and cleaning.

R, on the other hand, excels in flexibility and speed. With packages like dplyr, it allows concise and readable transformations. However, it requires more manual vigilance to ensure data integrity.

Handling invalid dates was a central focus. SAS converts invalid dates into missing values, enabling structured correction. R flags them as NA, allowing flexible imputation strategies.

Ultimately, data cleaning is not just a preprocessing step it is the foundation of trustworthy analytics. Whether in finance or clinical trials, clean data ensures accurate insights, regulatory compliance, and confident decision-making.

10. Conclusion

In any data-driven organization, the quality of insights is directly proportional to the quality of data. This blog walked through a realistic scenario where a global loan dataset contained hidden issues invalid dates, duplicates, and inconsistent values that could easily mislead analysis.

By leveraging SAS, we implemented a structured, step-by-step cleaning pipeline that ensures traceability, reproducibility, and compliance key requirements in regulated domains like clinical research. SAS’s ability to enforce strict data types and provide detailed logs makes it indispensable for enterprise-level data management.

R complemented this process by offering a flexible and intuitive environment for data transformation. Its expressive syntax allows analysts to quickly iterate and explore datasets, making it highly effective for early-stage analysis.

However, tools alone are not enough. The real strength lies in understanding the business logic behind the data. Why should a missing date be imputed? Why must negative values be corrected? These decisions require domain knowledge, not just programming skills.

Handling invalid dates is especially critical because time-based analysis underpins most business and clinical decisions. A single incorrect date can cascade into flawed models, incorrect conclusions, and costly mistakes.

The key takeaway is clear: Data cleaning is not optional it is foundational. A disciplined, well-documented, and logically sound cleaning process transforms raw data into a strategic asset.

11. Interview Questions

Q1: How do you handle invalid dates in SAS?

Answer: Use INPUT() to convert character to date. Invalid values become missing (.), which can then be imputed or flagged.

Q2: How would you detect duplicates in SAS?

Answer: Use PROC SORT NODUPKEY or PROC SQL GROUP BY HAVING COUNT(*) > 1.

Q3: In R, how do you handle invalid date formats?

Answer: Use as.Date(). Invalid formats return NA, which can be handled using ifelse() or replace_na().

Q4: Scenario – Loan amount is negative. What will you do?

Answer: Validate business logic. If invalid, use ABS() or flag for review.

Q5: Why is date validation critical in clinical trials?

Answer: Because endpoints depend on timelines. Incorrect dates can invalidate study results and regulatory submissions.

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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.

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The datasets and analysis in this article are created for educational and demonstration purposes only. Here we learn about LOAN DATA.

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