Improving Credit Risk Scorecards with Memory-Based ... - SAS Support
Recommend Documents
Praise for Credit Risk Scorecards "Scorecard development is important to retail financial services in terms of credit ri
(Institute of Chartered Financial Analysts of India, November 2006). From the Inside Flap. Using credit risk scorecards
... e-book collections are providing right here. Page 3 of 6. pdf-2669\credit-risk-scorecards-development-and-implementa
Management for Banking do? SAS Credit Risk Management for. Banking offers
built-in methodolo- gies and preconfigured capabilities that support all aspects of
...
Credit Risk Scorecards (Wiley and SAS Business Series) Book Free ... process plays out in the real world using sample da
The influential management consultant W. Edwards Deming advocated an ..... Of the 37 data points in the chart, 3 points
optimization software uses them, in conjunction with the costs of the offer and other ... business rules and advanced an
power of SAS analytics while providing secure access to data and IT resources ... If this program were run as a stored p
Paper 340-2011. Modeling Financial Risk Factor Correlation with the COPULA Procedure. Jan Chvosta ... Statistics and Data Analysis. SAS Global Forum 2011 ...
with Ð>1. MODELING WITH THE COPULA PROCEDURE. This paper uses a hypothetical portfolio that consists of four technology stocks: Apple Inc. (AAPL), Dell ...
SAS/STAT functionality. The enhancements of the 13.1,. 13.2, and 14.1 releases are summarized below. Missing Data Analys
banks and certain retailers, the ability to discriminate good customers from bad ... emerging artificial intelligent (AI) techniques, such as artificial neural networks.
May 27, 2014 - real-world credit dataset is used to test the performance of the. LS-FSVM to ..... McNemar's test for the credit dataset to statistically compare.
Reject inference is used to assign a target class (that is, a good or bad designation) to applications that were rejected by ..... [email protected] Email: Susan.
Data Mining and Text Analytics. SAS Global Forum 2011 .... r. E t. L t. : the exposure at the start of the cohort divided by the advised credit limit at the start of the ...
to call the same mathematical functions that are provided in the DATA step, .... Three types of SAS/IML operations enabl
Chapter 1 ⢠Introduction to Text Mining and SAS Text Miner 4.1 . .... Text mining applications have two phases: explor
Oct 6, 1999 - SAS® and all other SAS Institute Inc. product or service names are ... Creating More Than One Data Set in
has provided a wealth of tools for Bayesian analysis, with convenient access through several popular procedures as well
ESRI (a GIS software company) .... In 9.1 and earlier, SAS/GRAPH used SAS/GRAPH software fonts by default. .... different regions of the United States. The bar ...
credit risk data that is maintained in the SAS Detail Data Store for Banking and
the ..... that the SAS Web Administrator is associated with the authentication
domain (for ...... tutorials that are integrated into the product, reference
documentat
data, models, and tools that you use to determine them. ... In this example-driven book, Dr. Brown breaks down the requi
credit risk analysts and managers alike, as well as analysts working in fraud, ... SAS Certification Prep Guide: Advance
ESRI (a GIS software company) ...... proc gchart data=my_data (where=(continent='North America')); ... âStretching the Bounds of SAS/GRAPH Software.
Improving Credit Risk Scorecards with Memory-Based ... - SAS Support
Reject inference is used to assign a target class (that is, a good or bad designation) to applications ... This paper uses real-world data to present an example of using memory- based reasoning as a reject ..... Email: [email protected].
SAS Global Forum 2010
SAS Presents... Solutions
Paper 344-2010
Improving Credit Risk Scorecards with Memory-Based Reasoning to Reject Inference with SAS® Enterprise Miner™ Billie Anderson, Susan Haller, Naeem Siddiqi, James Cox, and David Duling SAS Institute Inc. ABSTRACT Many business elements are used to develop credit scorecards. Reject inference, related to the issue of sample bias, is one of the key processes required to build relevant application scorecards and is vital in creating successful scorecards. Reject inference is used to assign a target class (that is, a good or bad designation) to applications that were rejected by the financial institution and to applicants who refused the financial institution’s offer. This paper uses real-world data to present an example of using memorybased reasoning as a reject inference technique. SAS® Enterprise Miner™ software is used to perform the analysis. The paper discusses the technical concepts in reject inference and the methodology behind using memory-based reasoning as a reject inference technique. Several misclassification measures are reported to determine how well memory-based reasoning performs as a reject inference technique. In addition, a macro to determine how to pick the number of neighbors for the memory-based reasoning technique is given and discussed. This macro is implemented in a SAS® Enterprise Miner™ code node. OVERVIEW OF SCORECARDS Credit scorecard development is a method of modeling potential risk of credit applicants. It involves using different statistical techniques and past historical data to create a scorecard that financial institutions use to assess credit applicants in terms of risk. A scorecard model is built from a number of characteristic inputs. Each characteristic can have a number of attributes. In the example scorecard shown in Figure 1, age is a characteristic and “25
