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   Articles with "credit scoring" as a keyword



A novel augmentation strategy for credit scoring modeling

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recommendations! Published in 2025 at "Neural Computing and Applications"

DOI: 10.1007/s00521-024-10452-3

Abstract: In last years, social lending platforms have been increasingly used as virtual environments where borrowers can directly interact with lenders without any intermediary. As a result, a reliable credit scoring strategy, i.e., assessing whether a… read more here.

Keywords: augmentation strategy; novel augmentation; credit scoring; credit ... See more keywords
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Best practices for responsible machine learning in credit scoring

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recommendations! Published in 2024 at "Neural Computing and Applications"

DOI: 10.1007/s00521-025-11520-y

Abstract: The widespread use of machine learning in credit scoring has brought significant advancements in risk assessment and decision-making. However, it has also raised concerns about potential biases, discrimination, and lack of transparency in these automated… read more here.

Keywords: credit scoring; learning credit; machine; machine learning ... See more keywords

Deep reinforcement learning based on balanced stratified prioritized experience replay for customer credit scoring in peer-to-peer lending

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recommendations! Published in 2024 at "Artificial Intelligence Review"

DOI: 10.1007/s10462-023-10697-9

Abstract: In recent years, deep reinforcement learning (DRL) models have been successfully utilised to solve various classification problems. However, these models have never been applied to customer credit scoring in peer-to-peer (P2P) lending. Moreover, the imbalanced… read more here.

Keywords: experience replay; credit scoring; customer credit; credit ... See more keywords

An Efficient Multi-layer Ensemble Framework with BPSOGSA-Based Feature Selection for Credit Scoring Data Analysis

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recommendations! 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
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Spatial dependence in microfinance credit default

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recommendations! 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

NOTE: non-parametric oversampling technique for explainable credit scoring

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recommendations! Published in 2024 at "Scientific Reports"

DOI: 10.1038/s41598-024-78055-5

Abstract: Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic… read more here.

Keywords: non linear; oversampling technique; credit scoring; non parametric ... See more keywords

Credit scoring model based on a novel group feature selection method: The case of Chinese small-sized manufacturing enterprises

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recommendations! 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

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recommendations! 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

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recommendations! 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

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recommendations! 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

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recommendations! 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