top of page

This site was designed with the

website builder. Create your website today.Start Now

Final- semester actuarial science student
seeking for career opportunities in
finance,consulting or data analytics.

Specialised in statistical modelling

and quantitative analysis.

Jason Jie Sheng Lim

View my portfolio

SAS Algorithms

SAS is a statistical software developed by SAS institute for the purpose of advanced analytics, multivariate analyses, business intelligence, data management and predictive analytics.

Here are some SAS codes that I have written for model building purpose in the banking industry based on my experience as a credit risk modeller intern.

Please note that all data sets and methodologies used here are just simulated examples that reflect the banking inudtsry as the real- world data sets and methodologies could not be revealed due to privacy rights.

Here are 2 input data sets that I have simulated to start with:

Code that generates the following input data sets: Input data.docx

1. Monthly loan borrower accounts (from Jan 2013 to Dec 2014)

2. Name of loan borrowers

Task 1:

Append monthly loan borrower accounts to yearly loan borrower accounts, sorted by account number then date

Code that generates the output data associated with task 1: Task 1.docx

Output data:

Task 2:

Append all yearly loan borrower accounts into one big data set

Code that generates the output data associated with task 2: Task 2.docx

Output data:

Task 3:

Merge the appended loan borrower accounts with the name of loan borrowers

Code that generates the output data associated with task 3: Task 3.docx

Output data:

Task 4:

Output all default accounts.
Default accounts are recognised as accounts with ps = N consecutively for ≥ 8 times.

Code that generates the output data associated with task 4: Task 4.docx

Output data:

The default accounts are identified as followed:

Task 5:

Extract the very first default date of those default accounts

Code that generates the output data associated with task 5: Task 5.docx

Output data:

Task 6:

Track the discounted CR and DB outstanding balances of those accounts up to the default date.
Discounting to Jan 2013. Discount factor is 12%

Code that generates the output data associated with task 6: Task 6.docx

Output data:

Column 1- column 8

Column 8 – column 11

{ dcrbaln: discounted CR outstanding balance n month(s) from Jan 2014 }

Column 12 – column 15

{ dcrbaln: discounted CR outstanding balance n month(s) from Jan 2014 }

Column 62 – column 65

{ ddbbaln: discounted DB outstanding balance n month(s) from Jan 2014 }

Task 7:

Summing all discounted CR and DB outstanding balances of all default accounts

Code that generates the output data associated with task 7: Task 7.docx

Output data:

{ tdiscrb: Total discounted CR outstanding balance }

{ tdiscrb: Total discounted DB outstanding balance }

bottom of page