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Title Description
01). A Balanced Approach to Forecasting Credit Risk

In a recessionary environment, companies are looking for ways to accurately predict revenues and losses as unemployment increases and consumers and businesses default on their payment obligations. This paper presents a case study which compares two approaches to predicting losses using econometric methods.

02). Forecasting Insurance Claims - An Application of Tobit Modeling

This paper introduces the reader to a technique know as 'Tobit Regression' as a way of modeling a variable with data clustered around a lower limit such as zero. Sorry, this paper is unavailable at this time.

03). Target Marketing with Logit Regression

This paper illustrates how you can more effectively determine which customers will respond to new product offers. The same approach can be used for developing new target marketing lists as are used by the credit card companies in extending pre-approved offers of credit in the mail.

04). Introducing C.A.R.T. to the Forecasting Process

This paper presents a simple and easy to follow discussion of a modeling process called "CART" (Classification and Regression Trees) which can be used to supplement the forecasting process and help to better understand the relationships in your data.

05). New Product Forecasting Tools Find a Home in Telecommunications Credit Scoring

This presentation discusses the application of new product forecasting techniques in credit scoring. This is a nontechnical paper written to give a broad overview while highlighting the advantages of using a certain statistical tool in the modeling process.

06). How to Stress Test Your Credit Portfolio

This paper discusses an approach to forecasting credit losses using an econometric technique called "Pooled Cross-Section Time Series Regression". An illustration is given where a company can aggregate its portfolio information at the geographic level and use it to model and forecast future trends.

07). An Interactive Gui Front-end for a Credit Scoring Modeling System

This paper discusses the development of a modeling system for credit scoring written in SAS /AF.

08). New Product Forecasting

Article in Marketing News.

09). An Introduction to New Product Forecasting - NPD Practices (VISIONS) - Part 1

This paper introduces how diffusion models can be used for new product forecasting. This nontechnical paper gives a readable illustration of how a product manager can begin the forecasting process within a systematic framework producing results which can be used to justify the roll-out of a new product.

10). An Introduction to New Product Forecasting - NPD Practices (VISIONS) - Part 2

Continuation of previous article in VISIONS publication.

11). Introduction to New Product Forecasting - NPD Practices (VISIONS) - Part 3

Continuation of previous article in VISIONS publication.

12). Forecasting New Product Acceptance: A Walk through the Basics

This paper summarizes a step by step procedure for forecasting new product sales from start to finish. Topics include preliminary data analysis, data preperation, basic statistics, hypothesis testing, correlations, regression, dummy variables, transformations, interaction terms, model validation, and forecast simulation.

13). Innovation in a Consulting Environment

This discussion centers around how a consulting company which offered financial services developed a new service which allowed companies to stress test their credit portfolios using econometric methods and data at the sub-state geography level.

14). Life-Cycle Approach to New Product Forecasting

This paper presents an approach to new product forecasting using the basics of the product life cycle to describe new product purchases over time. Attention is give as to pent-up demand, the inflection point of the life-cycle, and introduces a concept called the "delay-factor". Numeric examples illustrate simple equations to walk the reader through the process.

15). How to use Diffusion Models in New Product Forecasting

This paper is slightly more technical than other presentations on the subject as it gives equations for logistic diffusion as well as gompertz curves. It discusses how to forecast under a variety of data conditions - a). when you have no historical data, b). when you have a short partial history, and how to use similar products as analogies if there exists a complete data history of the product.

16). Preparing for Modeling Requirements in Basel II - Model Development: Part 1

The Basel II Capital Accord, ready for implementation around 2006, sets out detailed analytic requirements for risk assessment that will be based on data collected by banks throughout the life cycle of the loan. The purpose of Basel II is to introduce a better risk-sensitive capital framework with incentives for good risk management practices. The more sophisticated banks will be able to take advantage of significantly reduced capital requirements, and therefore be more competitive. This article, the first in a four part series, will give a managerial overview of statistical modeling - an area of analytics that is paramount to the Basel framework.

17). Preparing for Modeling Requirements in Basel II - Model Validation: Part 2


The purpose of Basel II is to introduce a better risk-sensitive capital framework with incentives for good risk management practices. The more sophisticated banks will be able to take advantage of significantly reduced capital requirements, and therefore be more competitive. Access to these new capital approaches will depend on the bank's ability to develop and implement statistical models that have a proven track record over time. This article, the second in a four part series, will discuss some approaches to the validating statistical models required by the new Capital Accord.

18). Preparing for Modeling Requirements in Basel II - Putting it All Together: Part 3

As the Basel Capital Accord nears its 2007 implementation date, bankers are examining its detailed analytic requirements for risk assessment based on data to be collected by banks throughout the life cycle of the loan. The aim of Basel II is to introduce a more risk-sensitive capital framework with incentives for good risk management practices. Basel references words like process or systems about 275 times in the 139-page document. This highlights the importance of a systematic perspective to the analytic side of the house - an approach that integrates programming requirements across different environments with standardized methods and procedures.

19). Preparing for Modeling Requirements in Basel II - Stress Testing: Part 4

The purpose of Basel II is to introduce a better risk-sensitive capital framework with incentives for good risk management practices. The more sophisticated banks will be able to take advantage of significantly reduced capital requirements, and therefore be more competitive. Part of these requirements highlights the need for banks to stress test their credit portfolios. The idea of stress testing should be of interest to banks not only because of Basel, but because developing such approaches can provide the institution with the tools necessary to better manage their capital. Earlier articles in this series have focused on the development and validation of PD and LGD models. In contrast, this article will introduce a different modeling approach that uses aggregated data to predict the impact of economic and portfolio changes on bank default losses.

20). Preparing for Modeling Requirements in Basel II - Missing Data

This paper continues the RMA Journal articles on modeling requirements for Basel II focusing on the problem of missing data. A number of approaches in dealing with the problem are presented here including Multiple Imputation. Illustrations use the SAS software package for missing data.

21). Preparing for Modeling Requirements in Basel II - Modeling Strategies

This paper continues the RMA Journal articles on modeling requirements for Basel II focusing on modeling strategies to improve PD and LGD models. Illustrations use the SAS software package.

22). Preparing for Modeling Requirements in Basel II - Special Topics in Model Validation

This paper continues the RMA Journal articles on modeling requirements for Basel II focusing on statistical techniques to improve validation results when dealing with PD models with relatively few defaults. Specific discussions of Bootstrapping and Jackknife Estimation are presented. Illustrations use the SAS software package.

23). Preparing for Modeling Requirements in Basel II - Predicting Time to Default

This paper continues the RMA Journal articles on modeling requirements for Basel II focusing on predicting time to default - a field of study more broadly defined as 'Survival Analysis'. Illustrations use the SAS software package and discuss PROC LIFEREG.

24). Preparing for Modeling Requirements in Basel II - Modeling Complex Data with Neural Networks

This paper concludes the five part series on practical solutions to challenging problems in developing modeling requirements for Basel II. The purpose of this article is to look at a completely different approach to modeling PD (probability of default) and LGD (loss given default) ? neural networks. The motivation for examining approaches other than the traditional techniques recommended so far is because there may be some instances where credit and repayment relationships are more complex.

25). Introduction to Survival Analysis in Business

As the field of credit scoring is focused on predicting 'if' an account will become delinquent over a certain span of time, Survival Analysis can tell us 'when.' Survival Analysis is called different things in different industries event history analysis, reliability analysis, time to failure, and even duration analysis. The purpose of this article is to give an introduction to the subject with an emphasis on how it can be used in banking and finance.

26). A Hybrid Modeling Platform to meet Basel II Requirements in Banking

This paper describes the development of a hybrid modeling platform designed especially for that purpose using VB6 and SAS's OLE Automation capabilities. The discussion will revolve around the motivations for such a platform, its features and benefits, some example code and screen captures, and its outlook for the future.

27). Forecasting Insurance Claims with an Exploration of Superior Alternatives

Predicting claim behavior is of considerable interest in the insurance and financial communities. When companies wish to predict whether or not an individual will file at least one claim over a period of time, binary classification models are often chosen. Sometimes, there is a need to go beyond the classification process and predict the actual number of claims. The purpose of this paper was to examine five approaches to modeling insurance claims: 1). OLS, 2). Poisson Regression, 3). Tobit Regression without prior knowledge, 4). Tobit Regression with prior knowledge, and 5). Neural Networks. Sorry, this article is currently not available.

28). Model Development - Portuguese Translation.

Translation of RMA Article describing Basel II Requirements.

29). Model Validation - Portuguese Translation.

Translation of RMA Article describing Basel II Requirements.

30). Combining Modeling Tools - Portuguese Translation.

Translation of RMA Article describing Basel II Requirements.

31). Stress Testing - Portuguese Translation.

Translation of RMA Article describing Basel II Requirements.

32). Missing Data - Portuguese Translation.

Translation of RMA Article for Basel II Requirements

33). Model Building Strategies - Portuguese Translation.

Translation of RMA Article for Basel II Requirements

34). Topics in Model Validation - Portuguese Translation.

Translation of RMA Article for Basel II Requirements

35). Time to Default - Portuguese Translation.

Translation of RMA Article for Basel II Requirements

36). Neural Networks - Portuguese Translation.

Translation of RMA Article for Basel II Requirements

37). Credit Scoring Applications in Marketing

The paper shows how you can use the lessons learned in developing credit scoring models in the world of Marketing. For example, common approaches for growing your business such as response models as well as lookalike models are discussed. Also discussed are how to develop cross sell models along with using Survival Analysis in marketing.

38). Credit Scoring Applications in Marketing

The paper shows how you can use the lessons learned in developing credit scoring models in the world of Marketing. This is the same as the above publication, but reprinted in an international Journal - Credit Technology - which has a translation in Portuguese.

39). Variable Selection in Modeling

This paper discusses some typical problems in deciding which variables to use in regression analysis. Topics discussed are stepwise selection, variable clustering, principle components, and partial least squares. This paper is presented in both English and Portuguese.

40). Clustering in Marketing and Risk

This paper discusses how to go about building your own clustering solution using SAS and offers some practical advice along the way. This paper is presented in both English and Portuguese.

41). Credit Decisions in a Changing Economic Environment

In this uncertain environment, institutions should adjust their lending strategies to accommodate for relative risk at the state, MSA, and county levels.

42). Forecasting Portfolio Performance in an Uncertain Economy

We are in the midst of a severe recession. Some are even venturing to call it the Great Recession. If only we had known! Where were the warning signs? Can the future be predicted? Would financial institutions have benefited from an in-house forecasting function highlighting potential weaknesses?

43). Marrying Time Series and Credit Scoring Data

With today?s economic landscape and the advent of Basel II requirements, risk practitioners are struggling to integrate economic time-series data with their existing credit-scoring models to better understand portfolio risk.

44). Leveraging Aggregated Credit Data in Portfolio Forecasting and Collections Scoring

In this article, we examine the individual relationships between liquidation and external factors, both economic and credit, along with their use in a portfolio-level econometric forecasting model. Second, we will present evidence that the use of aggregated credit information at the ZIP code level adds considerable value when prioritizing accounts for maximum collection effectiveness.


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