Using neural network ensembles for bankruptcy prediction and credit scoring

By Chih-Fong Tsai, Jhen-Wei Wu. Expert systems with applications, 2008

526 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/10dedb59fdbe635aa12c850f83e4bb8aa9c3f9eb

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In Accounting

In Artificial Intelligence

In Management Science and Operations Research

Citations in the corpus

Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates (2018, methodology): “Neural network is widely used for enterprise credit evaluation modeling [3, 20, 35].”
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “Tsai and Wu [122] constructed an NN ensemble by mainly using different parameters and also using different versions of the same training set.”
A privacy-preserving algorithm for distributed training of neural network ensembles (2012, methodology): “Since neural network ensemble can improve the generalization ability of the learning model significantly, it has been widely used in machine learning and applied successfully to many other areas such as medical diagnosis [13, 14] and financial analysis [11, 12].”
Credit Scoring, Statistical Techniques and Evaluation Criteria: A Review of the Literature (2011, background): “The number of applications has increased during the last couple of decades, such as bankruptcy prediction (Tsai & Wu, 2008; Etemadi et al, 2009; Min & Lee, 2008; Nanni & Lumini, 2009; Atiya, 2001) and bankruptcy classification (Min & Jeong, 2009; Lensberg et al, 2006; MaKee & Lensberg, 2002;…”
Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, methodology): “To test the statistical significance of the difference obtained between two models, a p-value of the paired t-test applied to the cross-validation error rates (Doumpos and Zopounidis 2007; West et al. 2005; Tsai and Wu 2008) or McNemar’s test (Ripley 1996; Gestel et al.”
A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems (2010, methodology): “Because the gradient search process proceeds in a point-to-point mode and is intrinsically local in scope, convergence to local rather than global minima is a very possibility [58].”
Artificial neural networks in business: Two decades of research (2016, background): “Page 10 of 37 Ac ce pt ed M an us cr ip t focused on improving the overall accuracy of prediction [147, 167, 253, 374].”
Predicting Credit Risk in Peer-to-Peer Lending: A Neural Network Approach (2015, methodology): “The output layer transmits the information outside the network as the target output ,which corresponds to a dependent variable in the data [18,21].”
Machine behaviour (2019, background): “Credit scoring algorithms determine loan decisions 19–22 .”