A multi-level classification and modified PSO clustering based ensemble approach for credit scoring

Indu, S.; Kumar, Narendra; Srinivasa, K. G.; Maini, Shivam; Ahuja, Umang; Jain, Siddhant

doi:10.1016/j.asoc.2021.107687

Cited by 19 publications

(3 citation statements)

References 48 publications

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“…In this context, AI methods are implemented to assess the probability of customers failing to repay a loan or debt, which would result in losses for the financial institution. All the reviewed studies focusing on credit scoring and risk assessment involved technical analysis of the effectiveness and accuracy of different AI and ML algorithms (Ala'raj and Abbod, 2016;Correa Bahnsen et al, 2016;Singh et al, 2021a;Trivedi, 2019;Zhu et al, 2013), except for Aggarwal (2021), who discusses algorithmic credit scoring from a regulatory perspective instead and Mhlanga (2021), who reviews the literature on credit risk assessment in the context of financial inclusion and emerging economies. 4.2.5 Regulation.…”

Section: Contextmentioning

confidence: 99%

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Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

Hentzen

Hoffmann

Dolan

et al. 2021

IJBM

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PurposeThe objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.Design/methodology/approachCombining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.FindingsThe results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.Research limitations/implicationsThe review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.Originality/valueThe authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.

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Section: Contextmentioning

confidence: 99%

“…All the reviewed studies focusing on credit scoring and risk assessment involved technical analysis of the effectiveness and accuracy of different AI and ML algorithms (Ala'raj and Abbod, 2016; Correa Bahnsen et al. , 2016; Singh et al. , 2021a; Trivedi, 2019; Zhu et al.…”

Section: General Overviewmentioning

confidence: 99%

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

Hentzen

Hoffmann

Dolan

et al. 2021

IJBM

View full text Add to dashboard Cite

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“…The experiment was conducted on financial datasets provided by North American commercial banks, and researchers also combined supervised and unsupervised algorithms for the experiment. By comparing the unsupervised model with the supervised model, as well as the single model and the mixed model, the mixed model combining kmeans and random forest has the best prediction effect on the MSE evaluation index [28]. Indu Singh et al used neural network, KNN, support vector machine and random forest as benchmark classifiers, and then used Bagging, boosting, stacking and other methods to aggregate the results of different benchmark classifiers to form a more robust integrated classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Reports Classification Based on Semi-Supervised Learning Methods

Feng¹,

Han²,

Chen³

2023

Preprint

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Commercial banks usually classify customers according to their credit reports when making loans. In this study, we put our focus on classifying customers based on their credit reports from the People's Bank of China (PBC). Since there are no target labels of users in the credit report of the People's Bank of China, we put forward the fuzzy clustering method for the initial label, and then Construct ant colony search to optimize intelligent recognition. Finally, this study uses SVM, BP neural network, and random forest to classify users and compare their results. The research re-sults indicate that using ant colony clustering algorithm and random forest for classification is the most effective method with the PBC credit reports.

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An Adaptive and Dynamic Heterogeneous Ensemble Model for Credit Scoring

Museba

2023

Communications in Computer and Information Science

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A multi-level classification and modified PSO clustering based ensemble approach for credit scoring

Cited by 19 publications

References 48 publications

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

Credit Reports Classification Based on Semi-Supervised Learning Methods

An Adaptive and Dynamic Heterogeneous Ensemble Model for Credit Scoring

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