SQL Magic with Prompt Engineering: Generate Test Data and KPIs for Insurance Claims

In the insurance industry, managing claims data is essential for accurate analysis and reporting. Prompt engineering can be your powerful assistant, not just for querying data but also for generating test data. This guide walks you through the process of creating a claims data model, inserting sample data into the model, and generating SQL queries to extract critical Key Performance Indicators (KPIs). By following these steps, you’ll ensure referential integrity between your fact and dimension tables, leading to reliable and actionable insights.

Step 1: Creating a Claims Data Model for the Insurance Domain

1.1 Fact Table: Claims_Fact

The Claims_Fact table is central to your data model, containing transactional data related to insurance claims. Key columns include:

• Claim_ID (Primary Key): Unique identifier for each claim.
• Policy_ID (Foreign Key): Links to the insurance policy associated with the claim.
• Claim_Date: The date the claim was filed.
• Claim_Amount: The financial value of the claim.
• Claim_Status: Current status of the claim (e.g., Open, Closed, Rejected).
• Adjuster_ID (Foreign Key): Links to the adjuster handling the claim.

1.2 Dimension Tables:

a. Policy_Dimension

Holds detailed policy information.

• Policy_ID (Primary Key): Unique identifier for each policy.
• Policy_Type: Type of the policy (e.g., Auto, Health, Life).
• Policy_Start_Date: Date when the policy became active.
• Policy_End_Date: Date when the policy expires.
• Customer_ID (Foreign Key): Links to the policyholder's details.

b. Customer_Dimension

Stores customer data.

• Customer_ID (Primary Key): Unique identifier for each customer.
• Customer_Name: Full name of the customer.
• Customer_Age: Age of the customer.
• Customer_Gender: Gender of the customer.
• Customer_Address: Residential address of the customer.

c. Adjuster_Dimension

Captures details about the claim adjusters.

• Adjuster_ID (Primary Key): Unique identifier for each adjuster.
• Adjuster_Name: Name of the adjuster.
• Adjuster_Experience: Experience of the adjuster in years.

1.3 Referential Integrity

Referential integrity is maintained by establishing foreign key relationships between the Claims_Fact table and its associated dimension tables (Policy_Dimension, Customer_Dimension, Adjuster_Dimension). This ensures consistent and accurate linkage of claims to policies, customers, and adjusters.

Step 2: Creating Sample Insert Queries to Supply Data

To populate the tables with test data, you can use the following sample insert queries:

Insert Data into Policy_Dimension

sql
INSERT INTO Policy_Dimension (Policy_ID, Policy_Type, Policy_Start_Date, Policy_End_Date, Customer_ID) VALUES (101, 'Auto', '2022-01-01', '2023-01-01', 1), (102, 'Health', '2022-05-15', '2023-05-15', 2);        

Insert Data into Customer_Dimension

sql
INSERT INTO Customer_Dimension (Customer_ID, Customer_Name, Customer_Age, Customer_Gender, Customer_Address) VALUES (1, 'John Doe', 34, 'Male', '123 Elm Street'), (2, 'Jane Smith', 28, 'Female', '456 Oak Avenue');        

Insert Data into Adjuster_Dimension

sql
INSERT INTO Adjuster_Dimension (Adjuster_ID, Adjuster_Name, Adjuster_Experience) VALUES (201, 'Alice Johnson', 5), (202, 'Bob Martin', 8);        

Insert Data into Claims_Fact

sql
INSERT INTO Claims_Fact (Claim_ID, Policy_ID, Claim_Date, Claim_Amount, Claim_Status, Adjuster_ID) VALUES (1001, 101, '2023-01-10', 5000, 'Open', 201), (1002, 102, '2023-02-20', 15000, 'Closed', 202);        

These insert queries ensure that each table is populated with relevant data, maintaining referential integrity between them.

Step 3: Generating Sample Queries to Pull Key KPIs

Query 1: Total Claim Amount by Policy Type

sql
SELECT P.Policy_Type, SUM(CF.Claim_Amount) AS Total_Claim_Amount FROM Claims_Fact CF JOIN Policy_Dimension P ON CF.Policy_ID = P.Policy_ID GROUP BY P.Policy_Type;        

Description: This query calculates the total claim amount for each policy type, allowing you to assess which types of policies are generating the most claims.

Query 2: Average Claim Amount by Adjuster

sql
SELECT A.Adjuster_Name, AVG(CF.Claim_Amount) AS Average_Claim_Amount FROM Claims_Fact CF JOIN Adjuster_Dimension A ON CF.Adjuster_ID = A.Adjuster_ID GROUP BY A.Adjuster_Name;        

Description: This query calculates the average claim amount handled by each adjuster, providing insights into the efficiency and workload of adjusters.

Query 3: Number of Claims by Customer Gender

sql
SELECT C.Customer_Gender, COUNT(CF.Claim_ID) AS Number_of_Claims FROM Claims_Fact CF JOIN Customer_Dimension C ON CF.Customer_ID = C.Customer_ID GROUP BY C.Customer_Gender;        

Description: This query provides a breakdown of the number of claims filed by customers based on gender, helping to identify trends and patterns.

Query 4: Claims Status Distribution

sql
SELECT CF.Claim_Status, COUNT(CF.Claim_ID) AS Number_of_Claims FROM Claims_Fact CF GROUP BY CF.Claim_Status;        

Description: This query gives a snapshot of the distribution of claims statuses (e.g., Open, Closed, Rejected), aiding in understanding the current claims processing pipeline.

Conclusion:

Using prompt engineering, you can seamlessly act as both a SQL test data generator and a SQL editor. This approach enables you to build robust insurance claims data models, populate them with accurate test data, and generate insightful queries—all while maintaining referential integrity across your tables. These skills are crucial for driving effective data analysis and informed decision-making in the insurance sector.

Bonus text :

All the above examples were created using a simple prompt in chat-gpt without using any model, table or data being created manually. Try it yourself and enjoy the world of AI by customizing the prompt based on your use cases and create wonders.

"Act as a SQL test data generator and a sql editor and follow the steps. 1. Create a claims data model for insurance domain with relevant fact and dimension tables. 2. Create sample insert queries to supply the data to the relevant data. 3. Generate sample queries to pull the output for the key KPIs"

#SQL #DataModeling #InsuranceAnalytics #PromptEngineering #TestDataGeneration #KPIs #DataIntegrity #SQLQueries #DataScience #InsuranceData