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...

Credit scoring is one of the most successful applications in banking and finance. However, most studies do not explain the whole process of scorecard development, probably due to the difficulty in obtaining credit scoring data. This study addresses some of the gaps that are present in the existing literature in that it explains in detail the processes, as performed in practice, of scorecard dev...

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...

The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards...

Credit risk scoring has gone a long way since Fair Isaac introduced the first commercial scorecard to assist banks in making their credit lending decisions over 50 years ago. It now becomes the cornerstone in modern credit risk management thanks to the advancement in computing technologies and availability of affordable computing power. Credit scoring is no longer only applied in assessing lend...

Credit scoring model have been developed by banks and researchers to improve the process of assessing credit worthiness during the credit evaluation process. The objective of credit scoring models is to assign credit risk to either a ‘‘good risk’’ group that is likely to repay financial obligation or a ‘‘bad risk’’ group who has high possibility of defaulting on the financial obligation. Constr...

Data based scorecards, such as those used in credit scoring, age with time and need to be rebuilt or readjusted. Unlike the huge literature on modelling the replacement and maintenance of equipment there have been hardly any models which deal with this problem for scorecards. This paper identifies an effective way of describing the predictive ability of the scorecard and from this describes a s...

Background In consumer lending, portfolio managers typically have access to a scorecard used to forecast default probability for each applicant. Scorecards are built using historical data on loan accounts and their respective performance data. The inputs into the scorecard include financial, demographic and other personal information about each applicant. The output of the scorecard is a real-v...

Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical ...

Abstract: The paper shows how introducing economic variables into a credit scorecard improves the predictive power of the scorecard. Such a scorecard can forecast default rates accurately even when economic conditions change This means one can develop a single step approach to estimating the Point in Time PDs which are requirements of the Basel Accord banking regulations. A one step approach ha...