379.CAN SAS DELIVER END-TO-END TLF (TABLES, LISTINGS, AND FIGURES) FOR INSURANCE CLAIMS ANALYSIS?

CAN SAS DELIVER END-TO-END TLF (TABLES, LISTINGS, AND FIGURES) FOR INSURANCE CLAIMS ANALYSIS?

HERE IN THIS PROJECT WE USED THESE SAS STATEMENTS —
DATA STEP | PROC SQL | PROC REPORT | PROC PRINT | PROC SGPLOT | MACROS | TABLES | LISTINGS | FIGURES

1. What is TLF in Insurance Analytics?

In clinical trials, TLF means:

• Tables
• Listings
• Figures

In insurance analytics, TLF means:

Clinical Trials	Insurance Analytics
Tables	Claim summaries, fraud exposure
Listings	Claim-level audit reports
Figures	Risk distribution charts

Insurance companies must submit:

• Fraud exposure reports
• Claim approval summaries
• High-risk case listings
• Settlement turnaround reports

2. Where TLF fits in the Insurance Claim Lifecycle

Real insurance workflow:

1.     Claims submitted

2.     Risk calculated

3.     Fraud flagged

4.     Claims settled

5.     Regulatory reporting

Auditors, regulators, and management never see raw SAS datasets — they see:

• Tables
• Listings
• Figures

3. Insurance ADaM-like Analysis Dataset

Just like clinical ADaM, we use claims_flagged as the analysis dataset.

It contains:

• Claim_ID
• Policy_Type
• Claim_Amount
• Approval_Time
• Days_To_Submit
• Risk_Score
• Fraud_Category
• Settlement_Status

4. TLF Plan (Just like SAP in Clinical Trials)

Before writing code, professionals write a TLF Plan.

No	Output Type	Purpose
T1	Fraud Summary Table	Management risk view
T2	Settlement Summary Table	Operations efficiency
L1	High-Risk Claims Listing	Investigator review
L2	Pending Claims Listing	Operations team
F1	Risk Distribution Figure	Fraud modeling
F2	Claim Amount vs Risk	Financial exposure

5. Creating Raw Insurance Claims Dataset

data claims_raw;

    format Claim_Date Submission_Date date9.;

    input Claim_ID $ Policy_Type $ Claim_Amount Approval_Time Fraud_Flag$ Settlement_Status $ 

          Claim_Date:date9. Submission_Date:date9.;

    datalines;

C001 Health 120000 5 N Approved 01JAN2024 28DEC2023

C002 Motor 450000 12 Y Pending 05JAN2024 20DEC2023

C003 Travel 60000 3 N Approved 10JAN2024 08JAN2024

C004 Property 950000 25 Y Rejected 12JAN2024 01JAN2024

C005 Health 180000 7 N Approved 15JAN2024 10JAN2024

C006 Motor 320000 15 Y Pending 18JAN2024 02JAN2024

C007 Health 75000 4 N Approved 20JAN2024 17JAN2024

C008 Property 1500000 30 Y Rejected 25JAN2024 05JAN2024

C009 Travel 45000 2 N Approved 28JAN2024 27JAN2024

C010 Motor 280000 10 N Approved 30JAN2024 20JAN2024

C011 Health 220000 9 N Approved 02FEB2024 25JAN2024

C012 Property 680000 18 Y Pending 05FEB2024 15JAN2024

C013 Travel 55000 3 N Approved 07FEB2024 06FEB2024

C014 Motor 410000 14 Y Pending 10FEB2024 28JAN2024

C015 Health 195000 8 N Approved 12FEB2024 05FEB2024

;

run;

proc print data=claims_raw;

run;

OUTPUT:

Obs	Claim_Date	Submission_Date	Claim_ID	Policy_Type	Claim_Amount	Approval_Time	Fraud_Flag	Settlement_Status
1	01JAN2024	28DEC2023	C001	Health	120000	5	N	Approved
2	05JAN2024	20DEC2023	C002	Motor	450000	12	Y	Pending
3	10JAN2024	08JAN2024	C003	Travel	60000	3	N	Approved
4	12JAN2024	01JAN2024	C004	Property	950000	25	Y	Rejected
5	15JAN2024	10JAN2024	C005	Health	180000	7	N	Approved
6	18JAN2024	02JAN2024	C006	Motor	320000	15	Y	Pending
7	20JAN2024	17JAN2024	C007	Health	75000	4	N	Approved
8	25JAN2024	05JAN2024	C008	Property	1500000	30	Y	Rejected
9	28JAN2024	27JAN2024	C009	Travel	45000	2	N	Approved
10	30JAN2024	20JAN2024	C010	Motor	280000	10	N	Approved
11	02FEB2024	25JAN2024	C011	Health	220000	9	N	Approved
12	05FEB2024	15JAN2024	C012	Property	680000	18	Y	Pending
13	07FEB2024	06FEB2024	C013	Travel	55000	3	N	Approved
14	10FEB2024	28JAN2024	C014	Motor	410000	14	Y	Pending
15	12FEB2024	05FEB2024	C015	Health	195000	8	N	Approved

6. Date Calculations using INTCK

data claims_dates;

    set claims_raw;

    Days_To_Submit = intck('day', Submission_Date, Claim_Date);

run;

proc print data=claims_dates;

var Claim_ID  Policy_Type Days_To_Submit;

run;

OUTPUT:

Obs	Claim_ID	Policy_Type	Days_To_Submit
1	C001	Health	4
2	C002	Motor	16
3	C003	Travel	2
4	C004	Property	11
5	C005	Health	5
6	C006	Motor	16
7	C007	Health	3
8	C008	Property	20
9	C009	Travel	1
10	C010	Motor	10
11	C011	Health	8
12	C012	Property	21
13	C013	Travel	1
14	C014	Motor	13
15	C015	Health	7

7. Risk Score Calculation

data claims_risk;

    set claims_dates;

    Risk_Score = (Claim_Amount/10000) + Approval_Time + (Days_To_Submit*2);

run;

proc print data=claims_risk;

 var Claim_ID  Policy_Type Days_To_Submit Risk_Score;

run;

OUTPUT:

Obs	Claim_ID	Policy_Type	Days_To_Submit	Risk_Score
1	C001	Health	4	25.0
2	C002	Motor	16	89.0
3	C003	Travel	2	13.0
4	C004	Property	11	142.0
5	C005	Health	5	35.0
6	C006	Motor	16	79.0
7	C007	Health	3	17.5
8	C008	Property	20	220.0
9	C009	Travel	1	8.5
10	C010	Motor	10	58.0
11	C011	Health	8	47.0
12	C012	Property	21	128.0
13	C013	Travel	1	10.5
14	C014	Motor	13	81.0
15	C015	Health	7	41.5

8. Character Cleaning

data claims_clean;

    set claims_risk;

    Policy_Type = propcase(strip(Policy_Type));

    Settlement_Status = upcase(trim(Settlement_Status));

run;

proc print data=claims_clean;

 var Claim_ID  Policy_Type Days_To_Submit Risk_Score Policy_Type Settlement_Status;

run;

OUTPUT:

Obs	Claim_ID	Policy_Type	Days_To_Submit	Risk_Score	Policy_Type	Settlement_Status
1	C001	Health	4	25.0	Health	APPROVED
2	C002	Motor	16	89.0	Motor	PENDING
3	C003	Travel	2	13.0	Travel	APPROVED
4	C004	Property	11	142.0	Property	REJECTED
5	C005	Health	5	35.0	Health	APPROVED
6	C006	Motor	16	79.0	Motor	PENDING
7	C007	Health	3	17.5	Health	APPROVED
8	C008	Property	20	220.0	Property	REJECTED
9	C009	Travel	1	8.5	Travel	APPROVED
10	C010	Motor	10	58.0	Motor	APPROVED
11	C011	Health	8	47.0	Health	APPROVED
12	C012	Property	21	128.0	Property	PENDING
13	C013	Travel	1	10.5	Travel	APPROVED
14	C014	Motor	13	81.0	Motor	PENDING
15	C015	Health	7	41.5	Health	APPROVED

9. Fraud Detection Macro

%macro fraud_flag(input=, output=);

data &output;

    set &input;

    length Fraud_Category $12;

    if Risk_Score > 80 then Fraud_Category = "HIGH";

    else if Risk_Score > 50 then Fraud_Category = "MEDIUM";

    else Fraud_Category = "LOW";

run;

proc print data=&output;

run;

%mend;

%fraud_flag(input=claims_clean, output=claims_flagged);

OUTPUT:

Obs	Claim_Date	Submission_Date	Claim_ID	Policy_Type	Claim_Amount	Approval_Time	Fraud_Flag	Settlement_Status	Days_To_Submit	Risk_Score	Fraud_Category
1	01JAN2024	28DEC2023	C001	Health	120000	5	N	APPROVED	4	25.0	LOW
2	05JAN2024	20DEC2023	C002	Motor	450000	12	Y	PENDING	16	89.0	HIGH
3	10JAN2024	08JAN2024	C003	Travel	60000	3	N	APPROVED	2	13.0	LOW
4	12JAN2024	01JAN2024	C004	Property	950000	25	Y	REJECTED	11	142.0	HIGH
5	15JAN2024	10JAN2024	C005	Health	180000	7	N	APPROVED	5	35.0	LOW
6	18JAN2024	02JAN2024	C006	Motor	320000	15	Y	PENDING	16	79.0	MEDIUM
7	20JAN2024	17JAN2024	C007	Health	75000	4	N	APPROVED	3	17.5	LOW
8	25JAN2024	05JAN2024	C008	Property	1500000	30	Y	REJECTED	20	220.0	HIGH
9	28JAN2024	27JAN2024	C009	Travel	45000	2	N	APPROVED	1	8.5	LOW
10	30JAN2024	20JAN2024	C010	Motor	280000	10	N	APPROVED	10	58.0	MEDIUM
11	02FEB2024	25JAN2024	C011	Health	220000	9	N	APPROVED	8	47.0	LOW
12	05FEB2024	15JAN2024	C012	Property	680000	18	Y	PENDING	21	128.0	HIGH
13	07FEB2024	06FEB2024	C013	Travel	55000	3	N	APPROVED	1	10.5	LOW
14	10FEB2024	28JAN2024	C014	Motor	410000	14	Y	PENDING	13	81.0	HIGH
15	12FEB2024	05FEB2024	C015	Health	195000	8	N	APPROVED	7	41.5	LOW

10. TABLE 1 – Fraud Exposure Summary

Business Question

How much money is exposed to fraud?

proc sql;

create table tlf_fraud_summary as

select 

    Fraud_Category,

    count(*) as Total_Claims,

    sum(Claim_Amount) as Total_Exposure,

    mean(Risk_Score) as Avg_Risk

from claims_flagged

group by Fraud_Category;

quit;

proc print data=tlf_fraud_summary;

run;

OUTPUT:

Obs	Fraud_Category	Total_Claims	Total_Exposure	Avg_Risk
1	HIGH	5	3990000	132.00
2	LOW	8	950000	24.75
3	MEDIUM	2	600000	68.50

Why this Table Matters

• Regulators want fraud exposure
• Finance wants loss estimates
• Risk teams want severity

11. Display Table 1

proc report data=tlf_fraud_summary nowd;

columns Fraud_Category Total_Claims Total_Exposure Avg_Risk;

define Fraud_Category / "Fraud Level";

define Total_Claims / "Number of Claims";

define Total_Exposure / "Exposure Amount";

define Avg_Risk / "Average Risk Score";

run;

OUTPUT:

Fraud Level	Number of Claims	Exposure Amount	Average Risk Score
HIGH	5	3990000	132
LOW	8	950000	24.75
MEDIUM	2	600000	68.5

12. TABLE 2 – Settlement Efficiency Table

Business Question

Are risky claims taking longer to close?

proc sql;

create table tlf_settlement as

select 

    Fraud_Category,

    Settlement_Status,

    count(*) as Claims,

    mean(Approval_Time) as Avg_Days

from claims_flagged

group by Fraud_Category, Settlement_Status;

quit;

proc print data=tlf_settlement;

run;

OUTPUT:

Obs	Fraud_Category	Settlement_Status	Claims	Avg_Days
1	HIGH	PENDING	3	14.6667
2	HIGH	REJECTED	2	27.5000
3	LOW	APPROVED	8	5.1250
4	MEDIUM	APPROVED	1	10.0000
5	MEDIUM	PENDING	1	15.0000

This is used by:

• Claims operations
• SLA monitoring
• Audit teams

13. Display Settlement Table

proc report data=tlf_settlement nowd;

columns Fraud_Category Settlement_Status Claims Avg_Days;

define Fraud_Category / "Fraud Level";

define Settlement_Status / "Settlement";

define Claims / "Count";

define Avg_Days / "Average Approval Days";

run;

OUTPUT:

Fraud Level	Settlement	Count	Average Approval Days
HIGH	PENDING	3	14.666667
HIGH	REJECTED	2	27.5
LOW	APPROVED	8	5.125
MEDIUM	APPROVED	1	10
MEDIUM	PENDING	1	15

14. LISTING 1 – High-Risk Claim Listing

Business Question

Which specific claims must be investigated?

proc sql;

create table listing_highrisk as

 select Claim_ID,Policy_Type,Claim_Amount,Days_To_Submit,Risk_Score,Settlement_Status

 from claims_flagged

 where Fraud_Category="HIGH"

 order by Risk_Score desc;

quit;

proc print data=listing_highrisk;

run;

OUTPUT:

Obs	Claim_ID	Policy_Type	Claim_Amount	Days_To_Submit	Risk_Score	Settlement_Status
1	C008	Property	1500000	20	220	REJECTED
2	C004	Property	950000	11	142	REJECTED
3	C012	Property	680000	21	128	PENDING
4	C002	Motor	450000	16	89	PENDING
5	C014	Motor	410000	13	81	PENDING

15. Print High-Risk Listing

proc print data=listing_highrisk label noobs;

label Claim_ID="Claim ID"

      Policy_Type="Policy"

      Claim_Amount="Amount"

      Days_To_Submit="Delay Days"

      Risk_Score="Risk Score"

      Settlement_Status="Status";

run;

OUTPUT:

Claim ID	Policy	Amount	Delay Days	Risk Score	Status
C008	Property	1500000	20	220	REJECTED
C004	Property	950000	11	142	REJECTED
C012	Property	680000	21	128	PENDING
C002	Motor	450000	16	89	PENDING
C014	Motor	410000	13	81	PENDING

This is sent to:

• Fraud investigators
• Internal audit
• Regulators

16. LISTING 2 – Pending Claims

proc sql;

create table listing_pending as

 select Claim_ID,Policy_Type,Claim_Amount,Fraud_Category,Approval_Time

 from claims_flagged

 where Settlement_Status="PENDING";

quit;

proc print data=listing_pending;

run;

OUTPUT:

Obs	Claim_ID	Policy_Type	Claim_Amount	Fraud_Category	Approval_Time
1	C002	Motor	450000	HIGH	12
2	C006	Motor	320000	MEDIUM	15
3	C012	Property	680000	HIGH	18
4	C014	Motor	410000	HIGH	14

17. FIGURE 1 – Risk Score Distribution

proc sgplot data=claims_flagged;

 histogram Risk_Score;

 density Risk_Score;

 title "Distribution of Insurance Claim Risk Scores";

run;

OUTPUT:

Used for:

• Fraud modeling
• Risk calibration
• Model validation

18. FIGURE 2 – Claim Amount vs Risk

proc sgplot data=claims_flagged;

 scatter x=Risk_Score y=Claim_Amount;

 title "Claim Amount vs Fraud Risk";

run;

OUTPUT:

Used by:

• Finance
• Fraud analytics
• Loss modeling

14. Why TLF is Mandatory in Insurance

Insurance is regulated by:

• IRDAI (India)
• NAIC (USA)
• EIOPA (Europe)

They do not accept datasets — they accept:

• Tables
• Listings
• Figures

15. How This Matches Clinical Trial TLF

Clinical Trials Insurance AE Summary Table Fraud Exposure Table Patient Listing Claim Listing Safety Plots Risk Plots

This is why SAS clinical programmers easily move into insurance analytics.

16. Interview Value

If you explain this TLF:

You can answer:

• Regulatory reporting
• Risk modeling
• Audit compliance
• Data transparency
• End-to-end SAS pipelines

FINAL CONCLUSION

This project shows how raw insurance claim data is transformed into regulatory-grade Tables, Listings, and Figures using SAS.
The same workflow used in clinical trials was applied to fraud detection, risk scoring, and claim settlement analysis.
By building TLFs, we converted raw transactional data into audit-ready, management-ready, and regulator-ready outputs.
This is how real insurance companies operate their SAS analytics platforms.

INTERVIEW QUESTIONS FOR YOU

1.How to use MDY in SAS date handling?

2.How does PROC Copy work and when is it used?

3.What is the role of PROC DATASETS compared to deleting datasets using a DATA step?

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 Insurance Fraud TLF data.

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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 REALLY DETECT INSURANCE FRAUD AND CONTROL CLAIM LOSSES USING REAL DATA ANALYTICS?

2.Can Sas Analyze E-Commerce Data Using Basic Procs And Date Functions?

3.CAN SAS SIMPLY MANAGE AND ANALYZE A GLOBAL AUDI AQUARIUM FISH INVENTORY?

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