Credit scoring analysis using weighted k nearest neighbor

Mukid, Moch. Abdul; Widiharih, Tatik; Rusgiyono, Agus; Prahutama, Alan

doi:10.1088/1742-6596/1025/1/012114

Cited by 37 publications

(18 citation statements)

References 13 publications

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“…Based on past experience, credit scoring is the prediction of future behavior. An algorithm for this has been proposed using weighted k-NN [63]. The credit applicants are classified into one of two groups: a group whose members are likely to repay their debts and another group that should be denied credit because of high likelihood of defaulting.…”

Section: Credit Scoringmentioning

confidence: 99%

Quantum Machine Learning for Finance

Pistoia,

Ahmad,

Ajagekar

et al. 2021

Preprint

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Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term. This review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.

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Section: Credit Scoringmentioning

confidence: 99%

Quantum Machine Learning for Finance

Pistoia,

Ahmad,

Ajagekar

et al. 2021

Preprint

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“…O artigo em [7] propõe uma análise do método vizinho mais próximo ponderado, em que o mesmo foi aplicado na avaliac ¸ão de crédito. Para os experimentos utilizaram uma base de dados de um banco privado da Indonésia.…”

Section: Trabalhos Relacionadosunclassified

Avaliação de Técnicas de Aprendizado de Máquina Aplicadas à Análise de Risco de Crédito

Oliveira¹,

Santana²,

Rocha³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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O serviço de concessão de crédito ao consumidor final tem crescido de forma contundente nos últimos anos, sendo esta uma tendência se levarmos em consideração os juros mais baixos praticados com um cenário polı́tico/econômico cada vez mais conservador. Neste cenário, torna-se ainda mais relevante o processo de análise de risco de crédito, que ainda utiliza muitas técnicas arcaicas, com enfoque no tratamento individual e subjetivo dos dados do candidato à concessão. Visando otimizar a tarefa de análise de risco de crédito o presente trabalho tem como objetivo o estudo, implementação e avaliação de 10 dentre os principais algoritmos de classificação presentes na literatura aplicados à classificação de candidatos à concessão de crédito. Adicionalmente, a etapa de pré-processamento da base de dados utilizada incluiu a tarefa de seleção de caracterı́sticas através de filtros univariados e multivariados, com o intuito de encontrar os atributos mais relevantes bem como a eliminação de redundâncias. Os experimentos realizados demonstraram a importância da seleção de caracterı́sticas para melhoria dos algoritmos de classificação, além disso, foi possı́vel verificar que os métodos do tipo ensemble obtiveram os melhores resultados de maneira geral considerando-se a base de dados utilizada.

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“…Malekipirbazar and Aksakalli (2015) used a RF algorithm to predict the risk of borrowers of Lending Club, a foreign online lending platform. Mukid et al (2018) applied the k-nearest neighbor algorithm for credit evaluation under the premise of considering the role of core points. Ermpinis et al (2021) generated 50 multilayer perceptrons, 50 radial basis functions, 50 higher-order neural networks, and 50 recurrent neural networks for forecasting and trading economic index problems.…”

Section: Introductionmentioning

confidence: 99%

Risk Measurement of the Financial Credit Industry Driven by Data

Wang

et al. 2022

Journal of Global Information Management

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The risk measurement of financial credit industry is an important research issue in the field of financial risk assessment. The design of financial credit risk measurement algorithm can help investors avoid greater risks and obtain higher returns, so as to promote the benign development of financial credit industry. Based on the combined deep learning algorithm, this paper studies the risk measurement of financial and credit industry, and proposes a fusion algorithm of deep auto-encoder (DAE) and Long Short-Term Memory (LSTM) network. The algorithm recombines the value of fixed features by using the unsupervised mechanism of DAE, and extracts non fixed features for measurement combined with the memory characteristics of LSTM network. The experimental results show that: compared with single generalized regression neural network and LSTM network, the average accuracy of DAE-LSTM algorithm is improved by about 6.49% and 3.25% respectively, which has a better application effect in credit risk measurement.

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Credit scoring analysis using weighted k nearest neighbor

Cited by 37 publications

References 13 publications

Quantum Machine Learning for Finance

Quantum Machine Learning for Finance

Avaliação de Técnicas de Aprendizado de Máquina Aplicadas à Análise de Risco de Crédito

Risk Measurement of the Financial Credit Industry Driven by Data

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