Propensity scorecards allow forecasting, which bank customers would like to be granted new credits in the near future, through assessing their willingness to apply for new loans. Kalman filtering can help to monitor scorecard performance. Data from successive months are used to update the baseline model. The updated scorecard is the output of the Kalman filter. There is no assumption concerning...

Technology evaluation has played a crucial role in selecting and supporting companies with innovative technology. Previous studies have focused on developing technology evaluation methods such as scorecard. However, technology credit rating is rarely applied, despite its convenient usage for technology financing. In this paper, we propose a technology credit rating system, called cross matrix, ...

This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as ...

Selecting new and more predictive variables is fundamental for scorecards to perform well. This study makes use of a very large set of credit scoring data and investigates the application of several variable selection techniques for scorecard development. Among the four different techniques used, stepwise regression, which is currently the most popular technique used in practice, was found to p...

Fonkoze, a microfinance institution in Haiti, has used a poverty “scorecard” to benchmark existing client data to the $1-a-day extreme poverty line. By referring to data from a national survey of household income, 12 variables already routinely collected by Fonkoze were found to be suitable as indicators of whether a household is above or below the extreme poverty line. A scorecard was construc...

In the financial world there is a need for effectiveness and efficiency. The Farm Credit Quick loan analysis is a process created by Fair-Isaac. This analysis is referred to as scorecard lending. It uses a combination of financial data to determine the risk of the customer or candidate. Farm Credit fully adopted this process in 1997, for loans under $100,000.00. Efficiency and effectiveness is ...

Traditionally retail banks have supported the credit decision-making on scorecards developed for predicting default in a six-month period or more. However, the underlying pay/no pay cycles justify a decision in a 30-day period. In this work several classification models are built on this assumption. We start by assessing binary scorecards, assigning credit applicants to good or bad risk classes...

Traditionally the cluster analysis has been used as a descriptive tool, in which the algorithm is used to create groups of observations based on their characteristics. In this paper the use of cluster analysis as a part of a predictive algorithm is proposed. This methodology is applied by first determining to which cluster a prospect client belongs, and then calculate a specific credit risk sco...