scholarly journals A Discretized Enriched Technique to Enhance Machine Learning Performance in Credit Scoring

2019 ◽  
Author(s):  
Roberto Saia ◽  
Salvatore Carta ◽  
Diego Recupero ◽  
Gianni Fenu ◽  
Marco Saia
Keyword(s):  
Machine Learning ◽  
Credit Scoring ◽  
Learning Performance

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

scholarly journals Application of explainable machine learning based on Catboost in credit scoring

2021 ◽  
Vol 1955 (1) ◽  
pp. 012039
Author(s):  
Ji Qi ◽  
Ruicheng Yang ◽  
Pucong Wang
Keyword(s):  
Machine Learning ◽  
Credit Scoring

Algorithmic fairness in credit scoring

2021 ◽  
Vol 37 (3) ◽  
pp. 585-617
Author(s):  
Teresa Bono ◽  
Karen Croxson ◽  
Adam Giles
Keyword(s):  
Machine Learning ◽  
Credit Scoring ◽  
Large Data ◽  
Error Rates ◽  
The Past ◽  
Ensemble Machine Learning ◽  
Hidden Patterns ◽  
Credit Scoring Model ◽  
Distributional Impacts ◽  
Modelling Approach

Abstract The use of machine learning as an input into decision-making is on the rise, owing to its ability to uncover hidden patterns in large data and improve prediction accuracy. Questions have been raised, however, about the potential distributional impacts of these technologies, with one concern being that they may perpetuate or even amplify human biases from the past. Exploiting detailed credit file data for 800,000 UK borrowers, we simulate a switch from a traditional (logit) credit scoring model to ensemble machine-learning methods. We confirm that machine-learning models are more accurate overall. We also find that they do as well as the simpler traditional model on relevant fairness criteria, where these criteria pertain to overall accuracy and error rates for population subgroups defined along protected or sensitive lines (gender, race, health status, and deprivation). We do observe some differences in the way credit-scoring models perform for different subgroups, but these manifest under a traditional modelling approach and switching to machine learning neither exacerbates nor eliminates these issues. The paper discusses some of the mechanical and data factors that may contribute to statistical fairness issues in the context of credit scoring.

Machine Learning as a Silent Observer of Advanced Geoscience Interpretation

2021 ◽  
Author(s):  
Muhammad Sajid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Learning Performance ◽  
Learning Approach ◽  
Seismic Fault ◽  
Facies Classification ◽  
The World ◽  
Learning Machine ◽  
Fault Interpretation

Abstract Machine learning is proving its successes in all fields of life including medical, automotive, planning, engineering, etc. In the world of geoscience, ML showed impressive results in seismic fault interpretation, advance seismic attributes analysis, facies classification, and geobodies extraction such as channels, carbonates, and salt, etc. One of the challenges faced in geoscience is the availability of label data which is one of the most time-consuming requirements in supervised deep learning. In this paper, an advanced learning approach is proposed for geoscience where the machine observes the seismic interpretation activities and learns simultaneously as the interpretation progresses. Initial testing showed that through the proposed method along with transfer learning, machine learning performance is highly effective, and the machine accurately predicts features requiring minor post prediction filtering to be accepted as the optimal interpretation.

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