Editor’s note: This is the first article in a series by Conor Hogan, a Solutions Architect at SAS, on SAS and database and storage options on cloud technologies. This article covers the SAS offerings available to connect to and interact with the various database options available in Amazon Web Services. [...]Read More
Important Credit Risk Modeling Projects
Probability of Default (PD)tells us the likelihood that a borrower will default on the debt (loan or credit card). In simple words, it returns the expected probability of customers fail to repay the loan.
Loss Given Default (LGD)is a proportion of the total exposure when borrower defaults. It is calculated by (1 - Recovery Rate). For example someone takes $200,000 loan from bank for purchase of flat. He/She paid some installments before he stopped paying installments further. When he defaults, loan has an outstanding balance of $100,000. Bank took possession of flat and was able to sell it for $90,000. Net loss to the bank is $10,000 which is 100,000-90,000, and the LGD is 10% i.e. $10,000/$100,000.
Exposure at Default (EAD)is the amount that the borrower has to pay the bank at the time of default. In the above example shown in LGD, outstanding balance of $100,000 is EAD
Datasets for Credit Risk Modeling ProjectsWe have gathered data from several sources. See the list below. The following websites own the copyright on these data and authorizes their reproduction.
- UCI Machine Learning Repository
- Econometric Analysis Book by William H. Greene
- Credit scoring and its applications Book by Lyn C. Thomas
- Credit Risk Analytics Book by Harald, Daniel and Bart
- Lending Club
- PAKDD 2009 Data Mining Competition, organized by NeuroTech Ltd. and Center for Informatics of the Federal University of Pernambuco
An important application of nonlinear optimization is finding parameters of a model that fit data. For some models, the parameters are constrained by the data. A canonical example is the maximum likelihood estimation of a so-called "threshold parameter" for the three-parameter lognormal distribution. For this distribution, the objective function is [...]
The post Two tips for optimizing a function that has a restricted domain appeared first on The DO Loop.Read More
You can now easily embed a python script in a SAS decision with SAS Intelligent Decisioning. If you want to execute in MAS, you do not need to wrap it in DS2 anymore. The python code node does it for you. Here is how you can achieve it in less than 5 minutes.Read More
One of my friends likes to remind me that "there is no such thing as a free lunch," which he abbreviates by "TINSTAAFL" (or TANSTAAFL). The TINSTAAFL principle applies to computer programming because you often end up paying a cost (in performance) when you call a convenience function that simplifies [...]
The post Timing performance in SAS/IML: Built-in functions versus Base SAS functions appeared first on The DO Loop.Read More
You have data that you want to visualize. You need to explore your graphing options using SAS, but you don’t know where to start. Help is here with the recently revised Base SAS guide, Introduction to SAS® Platform Graphing. This guide is helpful for novice SAS users as well as [...]
The post SAS has a guide to help when you need to create a graph and don't know where to start! appeared first on Graphically Speaking.Read More
A few examples to demonstrate some of the common output-related problems with ODS Graphics Procedures. If your graphical output does not appear as you wanted, consider the options that you are using and make sure that you are using the correct option.
How to fix common problems in output from ODS Graphics procedures was published on SAS Users.Read More