In the world of risk scoring and credit scoring models, SQL is a powerful tool for data processing and analysis. One useful function for building effective models is FIRST_VALUE, which allows us to retrieve the first value from a group of records based on a specific ordering.

What is FIRST_VALUE?

FIRST_VALUE is an analytic function in SQL that returns the first value in an ordered set of rows. It is typically used in conjunction with the OVER clause to define the partitioning and ordering of the data.

The basic syntax of FIRST_VALUE is as follows:

FIRST_VALUE (expression) OVER (PARTITION BY partition_expression ORDER BY sort_expression)

• expression is the column or expression you want to retrieve the first value from.
• PARTITION BY defines how to divide the result set into partitions.
• ORDER BY specifies the ordering of the data within each partition.

Leveraging FIRST_VALUE in risk scoring models

In risk scoring models, it is often crucial to consider the historical behavior of individuals or entities. For example, in credit scoring models, we may want to look at the first loan amount that an individual borrowed. This information can provide valuable insights into their financial behavior.

Let’s assume we have a table called loans with the following columns:

• loan_id: unique identifier for each loan
• borrower_id: identifier for the individual borrowing the loan
• loan_amount: the amount of the loan
• disbursement_date: the date the loan was disbursed

To find the first loan amount for each borrower, we can use the FIRST_VALUE function as follows:

SELECT DISTINCT
  borrower_id,
  FIRST_VALUE(loan_amount) OVER (PARTITION BY borrower_id ORDER BY disbursement_date) AS first_loan_amount
FROM
  loans

In the above SQL query, we first partition the data by borrower_id and then order it by disbursement_date. The FIRST_VALUE function retrieves the first loan amount for each borrower.

The resulting output will give us the distinct borrower IDs along with their corresponding first loan amounts.

Leveraging FIRST_VALUE in credit scoring models

In credit scoring models, understanding the pattern of credit utilization can be crucial. For example, we might want to determine the first time an individual’s credit card utilization exceeded a certain threshold. This information can help us assess their creditworthiness.

Let’s assume we have a table called credit_card_transactions with the following columns:

• transaction_id: unique identifier for each transaction
• card_number: the credit card number
• transaction_date: the date of the transaction
• transaction_amount: the amount of the transaction

To find the first transaction where credit card utilization exceeded a certain threshold for each card, we can use the FIRST_VALUE function as follows:

SELECT DISTINCT
  card_number,
  FIRST_VALUE(transaction_date) OVER (PARTITION BY card_number ORDER BY transaction_date) AS first_exceeding_threshold_date
FROM
  credit_card_transactions
WHERE
  transaction_amount > threshold_amount

In the above SQL query, we partition the data by card_number and order it by transaction_date. We then filter the records by the transaction_amount exceeding a specified threshold_amount. The FIRST_VALUE function retrieves the first transaction date where the credit card utilization exceeded the threshold.

The resulting output will give us the distinct card numbers along with the corresponding date when the threshold was first exceeded.

Conclusion

Leveraging the FIRST_VALUE function in SQL-based risk scoring and credit scoring models allows us to gain insights into historical behavior and make informed decisions. By partitioning and ordering the data based on specific criteria, we can effectively retrieve the first value from a group of records. This information can be invaluable in assessing risk, creditworthiness, and making data-driven decisions.

References

• SQL FIRST_VALUE Function
• Analytic Functions in SQL