Intelligent credit scoring using deep learning methods

Gicić, Adaleta; Ðonko, Dženana; Subaşı, Abdülhamit

doi:10.1002/cpe.7637

Cited by 6 publications

(4 citation statements)

References 51 publications

(119 reference statements)

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“…In the case of the "Statlog (German Credit Data)" [22], the current study achieved an accuracy of 78.30%. While this accuracy is an improvement over some earlier studies, it falls short of the highest accuracy reported for this dataset, which was 88.89% by Gicić et al [14]. For "South German Credit " [23], this study achieved an accuracy of 77.80%.…”

Section: Related Studies Comparisoncontrasting

confidence: 69%

“…The outcome of the experiments showed that the presented method achieved significant results in Australian credit score data and some progress on the German loan approval data. In Gicić et al [14], stacked unidirectional and bidirectional LSTM networks were applied to solve credit scoring tasks. The proposed model exploited the full potential of the three-layer stacked LSTM and BiLSTM architecture with the treatment and modeling of public datasets.…”

Section: Feature Selection Machine Learning and Credit Scoring: A Rev...mentioning

confidence: 99%

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Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

Aljadani,

Alharthi,

Farsi

et al. 2023

Mathematics

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Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The advent of machine learning heralds a transformative era, offering data-driven solutions that transcend these limitations. This research delves into a comprehensive analysis of various machine learning algorithms, emphasizing their mathematical underpinnings and their applicability in credit score classification. A comprehensive evaluation is conducted on a range of algorithms, including logistic regression, decision trees, support vector machines, and neural networks, using publicly available credit datasets. Within the research, a unified mathematical framework is introduced, which encompasses preprocessing techniques and critical algorithms such as Particle Swarm Optimization (PSO), the Light Gradient Boosting Model, and Extreme Gradient Boosting (XGB), among others. The focal point of the investigation is the LIME (Local Interpretable Model-agnostic Explanations) explainer. This study offers a comprehensive mathematical model using the LIME explainer, shedding light on its pivotal role in elucidating the intricacies of complex machine learning models. This study’s empirical findings offer compelling evidence of the efficacy of these methodologies in credit scoring, with notable accuracies of 88.84%, 78.30%, and 77.80% for the Australian, German, and South German datasets, respectively. In summation, this research not only amplifies the significance of machine learning in credit scoring but also accentuates the importance of mathematical modeling and the LIME explainer, providing a roadmap for practitioners to navigate the evolving landscape of credit assessment.

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Section: Related Studies Comparisoncontrasting

confidence: 69%

Section: Feature Selection Machine Learning and Credit Scoring: A Rev...mentioning

confidence: 99%

Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

Aljadani,

Alharthi,

Farsi

et al. 2023

Mathematics

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“…This architecture enables the automatic extraction of hierarchical features from data. In credit scoring, deep learning models, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), enhance the understanding of complex relationships within diverse datasets (Gicić et al, 2023). Deep learning excels when confronted with large and unstructured data, providing a robust framework for credit risk assessment.…”

Section: Ai Models In Credit Scoringmentioning

confidence: 99%

AI in credit scoring: A comprehensive review of models and predictive analytics

Wilhelmina Afua Addy,

Adeola Olusola Ajayi-Nifise,

Binaebi Gloria Bello

et al. 2024

Global J. Eng. Technol. Adv.

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This review provides a succinct overview of the comprehensive review exploring the integration of Artificial Intelligence (AI) in credit scoring. The analysis delves into diverse AI models and predictive analytics shaping the contemporary landscape of credit assessment. The review begins by examining the historical context of credit scoring and progresses through the transformative impact of AI on traditional credit assessment methodologies. It scrutinizes various AI models employed in credit scoring, ranging from machine learning algorithms to advanced predictive analytics. Emphasis is placed on elucidating the strengths and limitations of each model, considering factors such as interpretability, accuracy, and scalability. The evolution of credit scoring is discussed, emphasizing the transition from rule-based systems to sophisticated AI-driven approaches. The integration of alternative data sources, such as social media and unconventional financial indicators, is explored, showcasing the expanding scope of AI in capturing a more holistic view of an individual's creditworthiness. The Review underscores the significance of predictive analytics in credit scoring, outlining the nuanced techniques used to forecast credit risk. It elucidates the role of explainable AI, addressing the need for transparency in complex credit scoring models, especially in the context of regulatory compliance and consumer trust. Furthermore, the review highlights the real-world implications of AI in credit scoring, discussing its impact on financial inclusion, risk management, and decision-making processes. The ethical considerations and potential biases associated with AI models are explored, shedding light on the importance of fairness and responsible AI practices in the credit industry. In conclusion, this comprehensive review navigates the intricate landscape of AI in credit scoring, offering a holistic understanding of the models and predictive analytics that underpin modern credit assessment. The synthesis of historical perspectives, model intricacies, and real-world implications makes this review an essential resource for practitioners, researchers, and policymakers in the ever-evolving domain of AI-driven credit evaluation.

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“…The development of rule-based and evolutionary models, such as the one proposed by Soui, Gasmi, Smiti, and Ghédira (2019), and the application of deep learning by Gicić, Ðonko, and Subasi (2023), have signiĄcantly improved the interpretability and accuracy of credit risk assessments. Mahajan et al (2022) contributed to the Ąeld by employing a Gaussian process-based approach for uncertainty handling in credit risk predictions.…”

Section: Advances In Credit Risk Modeling Techniquesmentioning

confidence: 99%

Ensemble Learning with Feature Optimization for Credit Risk Assessment

Zeng,

Su,

Hong

2024

Preprint

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Credit risk assessment stands as a cornerstone in financial decision-making, with significant implications for economic stability and growth. This paper highlights the transformative advantages of credit big data over traditional methods, particularly in enhancing the creditworthiness evaluation of small and medium-sized enterprises (SMEs). We delineate the distinctive features of the big data financial innovation model across six economic dimensions, showcasing its potential to reshape financial practices. To address the inefficiencies of traditional expert-driven approaches, we introduce an innovative 'Feature Selector-classifier Optimization Framework' that streamlines the credit risk prediction process. This framework not only refines the accuracy and efficiency of predictions but also integrates seamlessly with economic analysis, offering a robust tool for financial decision-makers. Our ensemble classifier delivers remarkable performance, exemplified by its high accuracy and AUC scores across multiple datasets, thereby validating the framework's efficacy in enhancing predictive power while ensuring operational efficiency.

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Intelligent credit scoring using deep learning methods

Cited by 6 publications

References 51 publications

Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

AI in credit scoring: A comprehensive review of models and predictive analytics

Ensemble Learning with Feature Optimization for Credit Risk Assessment

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