An Efficient Multi-layer Ensemble Framework with BPSOGSA-Based Feature Selection for Credit Scoring Data Analysis
Sign Up to like & getrecommendations! Published in 2018 at "Arabian Journal for Science and Engineering"
DOI: 10.1007/s13369-017-2905-4
Abstract: Credit scoring is extensively used by credit industries and financial institutions for financial decision-making. It is a way to assess the risk associated with an applicant based on historical data. However, the historical data may… read more here.
Keywords: credit; feature selection; multi layer; layer ensemble ... See more keywords
Spatial dependence in microfinance credit default
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Forecasting"
DOI: 10.1016/j.ijforecast.2021.05.009
Abstract: Abstract Credit scoring model development is very important for the lending decisions of financial institutions. The creditworthiness of borrowers is evaluated by assessing their hard and soft information. However, microfinance borrowers are very sensitive to… read more here.
Keywords: microfinance; spatial dependence; default; credit scoring ... See more keywords
Credit scoring model based on a novel group feature selection method: The case of Chinese small-sized manufacturing enterprises
Sign Up to like & getrecommendations! Published in 2021 at "Journal of the Operational Research Society"
DOI: 10.1080/01605682.2021.1880295
Abstract: In building a predictive credit scoring model, feature selection is an essential pre-processing step that can improve the predictive accuracy and comprehensibility of models. In this study, we sele... read more here.
Keywords: scoring model; feature selection; credit scoring;
Transparency, auditability, and explainability of machine learning models in credit scoring
Sign Up to like & getrecommendations! Published in 2021 at "Journal of the Operational Research Society"
DOI: 10.1080/01605682.2021.1922098
Abstract: Abstract A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such… read more here.
Keywords: learning models; transparency auditability; credit scoring; machine learning ... See more keywords
Reject inference methods in credit scoring
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Applied Statistics"
DOI: 10.1080/02664763.2021.1929090
Abstract: The granting process is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability, also called score, is learnt based on a dataset in which rejected applicants are excluded.… read more here.
Keywords: reject inference; inference methods; methods credit; credit scoring ... See more keywords
A Novel Noise-Adapted Two-Layer Ensemble Model for Credit Scoring Based on Backflow Learning
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2930332
Abstract: Recently, the machine learning method and artificial intelligence algorithm have become increasingly important in classification problems, such as credit scoring. Building an ensemble learning model that has been proven to be typically more accurate and… read more here.
Keywords: layer ensemble; two layer; model; credit scoring ... See more keywords
An Online Transfer Learning Framework with Extreme Learning Machine for Automated Credit Scoring
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3171569
Abstract: Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data. It involves analysing and predicting the association between the data and particular credit values based on similar data. Recently, ACS… read more here.
Keywords: credit; credit scoring; machine; automated credit ... See more keywords
Model-agnostic Counterfactual Explanations in Credit Scoring
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3177783
Abstract: The past decade has shown a surge in the use and application of machine learning and deep learning models across different domains. One such domain is the credit scoring domain, where applicants are scored to… read more here.
Keywords: counterfactual explanations; credit; credit scoring; explanations credit ... See more keywords
Internet Financial Credit Scoring Models Based on Deep Forest and Resampling Methods
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3239889
Abstract: In recent years, deep learning credit scoring models have become a hot research topic in Internet finance. However, most of the existing studies are based on deep neural network models, whose structure is difficult to… read more here.
Keywords: credit; credit scoring; resampling methods; deep forest ... See more keywords
Differential Replication for Credit Scoring in Regulated Environments
Sign Up to like & getrecommendations! Published in 2021 at "Entropy"
DOI: 10.3390/e23040407
Abstract: Differential replication is a method to adapt existing machine learning solutions to the demands of highly regulated environments by reusing knowledge from one generation to the next. Copying is a technique that allows differential replication… read more here.
Keywords: regulated environments; replication credit; differential replication; credit scoring ... See more keywords
Which Curve Fits Best: Fitting ROC Curve Models to Empirical Credit-Scoring Data
Sign Up to like & getrecommendations! Published in 2022 at "Risks"
DOI: 10.3390/risks10100184
Abstract: In the practice of credit-risk management, the models for receiver operating characteristic (ROC) curves are helpful in describing the shape of an ROC curve, estimating the discriminatory power of a scorecard, and generating ROC curves… read more here.
Keywords: roc curve; curve models; credit; credit scoring ... See more keywords