A relative evaluation of the performance of ensemble learning in credit scoring

Devi, C. R. Durga; Chezian, R. Manicka

doi:10.1109/icaca.2016.7887943

Cited by 15 publications

(4 citation statements)

References 3 publications

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“…Another way to improve the accuracy of models based on machine learning in credit scoring is to eliminate unwanted features by implementing parallel random forest and also enhancing its performance having 76.2% on a German dataset and 89.4% on an Australian credit dataset according to Van Sang et al [12]. An ensemble method like AdaBoost, Bagging, and Random Forest are three algorithms that produced a higher performance by classifying good from bad loan customers [13].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Effectiveness of Random Forest Algorithm in Financial Credit Risk Management for Improving Output Accuracy and Loan Classification Prediction

Kuyoro¹,

Ogunyolu²,

Ayanwola³

et al. 2022

ISI

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With technology impacting several sectors, it can be imagined that the financial sector has a lot to benefit from the increasing level of technological innovations. These institutions take from the surplus of the economy and lend to the deficit sectors of the economy. Individuals and organizations obtain credit facilities from financial institutions to meet basic needs and boost their businesses. However, the stability of the economy is better guaranteed when borrowers pay back the loans availed to them rather than default. This study aims to identify the effectiveness of Random Forest in credit scoring using 32,581 observations. The study proved that Random Forest provides better output accuracy of 91% based on Gini Index for variable selection according to the level of importance when compared to Decision Tree with an output of 83%. It offers better credit scoring accuracy and credit rating as a result of its classification power. The objective of the study is to point out the random forest predictive strength using an unprocessed German credit dataset from Kaggle and to provide an explainable framework sufficient for Financial Institutions and banks to make decisions when granting loans to existing and new applicants.

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Section: Related Workmentioning

confidence: 99%

Dynamic Effectiveness of Random Forest Algorithm in Financial Credit Risk Management for Improving Output Accuracy and Loan Classification Prediction

Kuyoro¹,

Ogunyolu²,

Ayanwola³

et al. 2022

ISI

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“…Ensemble merupakan beberapa set pelatihan yang digunakan untuk memecahkan masalah yang sama dari hasil dari klasifikasi tunggal kemudian digabungkan dengan teknik Ensemble menjadi pengelompokan tunggal untuk meningkatkan kinerja dari klasifikasi tunggal (Tsai, 2014). Penelitian (Devi & Chezian, 2016) mengusulkan untuk menganalisis keakuratan metode Ensemble dalam mengklasifikasikan pelanggan menggunakan tiga metode Ensemble yaitu AdaBoost, Bagging, dan Random Forest tetapi dalam penelitian ini masih menggunakan klasifikasi standar tunggal dari metode Ensemble yaitu metode Decision Tree.…”

Section: Pendahuluanunclassified

Peningkatan performance Logistic Regression menggunakan teknik Ensemble Bagging pada kasus Credit Scoring

Aziz

2020

JSCE

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Pengembangan model credit scoring yang efektif menjadi sangat penting karena volume data pelanggan industri kredit saat ini sangat besar. Penyelesaian masalah credit scoring berhasil mendapatkan kinerja prediktif terbaik menggunakan model metode statistik tetapi kinerjanya masih dapat ditingkatkan dengan memperkirakan parameter menggunakan persamaan nonlinear. Penelitian ini mengusulkan peningkatan metode Logistic Regression dengan melakukan teknik Ensemble dengan memperkirakan parameter Newton Raphson. Teknik Ensemble yang digunakan adalah bagging. Dua data yang akan digunakan dalam penelitian ini adalah German dan Australian Dataset. Hasil penelitian menunjukkan bahwa metode yang diusulkan berhasil mencapai kinerja terbaik dengan meningkatkan kinerja klasifikasi tunggal dengan akurasi sebesar 79.6 % untuk German Dataset dan 86.9 % untuk Australian Dataset.

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“…With the rapid development of the financial technology industry, the data of the financial credit industry have shown the characteristics of huge volume, various types, low-value density, and high timeliness. Whether a model can be designed by extracting valuable indicators from complex data indicators is the key to evaluating the measurement effect of the model (Devi & Chezian, 2016). Obviously, it is difficult for traditional credit evaluation methods to extract more effective value information under a new situation.…”

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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A relative evaluation of the performance of ensemble learning in credit scoring

Cited by 15 publications

References 3 publications

Dynamic Effectiveness of Random Forest Algorithm in Financial Credit Risk Management for Improving Output Accuracy and Loan Classification Prediction

Dynamic Effectiveness of Random Forest Algorithm in Financial Credit Risk Management for Improving Output Accuracy and Loan Classification Prediction

Peningkatan performance Logistic Regression menggunakan teknik Ensemble Bagging pada kasus Credit Scoring

Risk Measurement of the Financial Credit Industry Driven by Data

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