Several modelling procedures have been suggested in the literature that aim to help credit granting decisions. Most of these utilize statistical, operational research and artificial intelligence techniques to identify patterns among past applications, in order to enable a more well-informed assessment of risk as well as the automation of credit scoring. For some types of loans, we find that the modelling procedure must permit the consideration of qualitative expert judgements concerning the performance attractiveness of the applications. In this paper, we describe in detail the various steps taken to build such a model in the context of the banking sector, using the macbeth interactive approach. The model addresses the scoring of medium and...
Development of credit scoring models is important for financial institutions to identify defaulters ...
This master work describes the most widely used artificial intelligence methods and the possibilitie...
The application of statistical techniques in decision making, and more specifically for classificati...
Several modelling procedures have been suggested in the literature that aim to help credit granting ...
Tremendous growth in the credit industry has spurred the need for Credit Scoring and Its Application...
This paper presents a brief review on the current available techniques for credit scoring model, nam...
The use of credit scoring - the quantitative and statistical techniques to assess the credit risks i...
This book focuses on the alternative techniques and data leveraged for credit risk, describing and a...
The aim of this paper is to present how credit scoring models can be used in financial institutions,...
In this paper, a mixed model for credit scoring is orchestrated which applies bundle learning for cr...
The relevance of designing, implementing and using scoring systems for credit risk management today ...
The volume Credit scoring in context of interpretable machine learning presents a unique, and simult...
Abstract: Credit scoring is a numerical expression of the credit worthiness of an individual. A Valu...
Purpose: This paper aims to present a literature review of the most recent optimisation methods appl...
Machine learning is becoming a part of everyday life and has an indisputable impact across large arr...
Development of credit scoring models is important for financial institutions to identify defaulters ...
This master work describes the most widely used artificial intelligence methods and the possibilitie...
The application of statistical techniques in decision making, and more specifically for classificati...
Several modelling procedures have been suggested in the literature that aim to help credit granting ...
Tremendous growth in the credit industry has spurred the need for Credit Scoring and Its Application...
This paper presents a brief review on the current available techniques for credit scoring model, nam...
The use of credit scoring - the quantitative and statistical techniques to assess the credit risks i...
This book focuses on the alternative techniques and data leveraged for credit risk, describing and a...
The aim of this paper is to present how credit scoring models can be used in financial institutions,...
In this paper, a mixed model for credit scoring is orchestrated which applies bundle learning for cr...
The relevance of designing, implementing and using scoring systems for credit risk management today ...
The volume Credit scoring in context of interpretable machine learning presents a unique, and simult...
Abstract: Credit scoring is a numerical expression of the credit worthiness of an individual. A Valu...
Purpose: This paper aims to present a literature review of the most recent optimisation methods appl...
Machine learning is becoming a part of everyday life and has an indisputable impact across large arr...
Development of credit scoring models is important for financial institutions to identify defaulters ...
This master work describes the most widely used artificial intelligence methods and the possibilitie...
The application of statistical techniques in decision making, and more specifically for classificati...