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Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE
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2020 | Information Systems Frontiers | Citations: 2
Authors: Smiti, Salima; Soui, Makram

Abstract: Imbalanced classification on bankruptcy prediction is considered as one of the m ... Expand

Abstract: Imbalanced classification on bankruptcy prediction is considered as one of the most important topics in financial institutions. In this context, various statistical and artificial intelligence methods have been proposed. Recently, deep learning algorithms are experiencing a resurgence of interest, and are widely used to build a prediction and classification models. To this end, we propose a novel deep learning-based approach called BSM-SAES. This approach combines Borderline Synthetic Minority oversampling technique (BSM) and Stacked AutoEncoder (SAE) based on the Softmax classifier. The aim is to develop an accurate and reliable bankruptcy prediction model which includes the features extraction process. To assess the classification performance of our proposed model, k- nearest neighbor, decision tree, support vector machine, and artificial neural network, C5.0 that are machine learning methods, are applied. We evaluate our proposed approach on the Polish imbalanced datasets. The obtained results confirm the efficiency of our proposed model compared to other machine learning models regarding predicting and classifying the financial status of a firm. Collapse

Semantic filters: deep belief network

Topics: decision making organizational productivity

Methods: computational algorithm autoencoder machine learning data transformation deep learning
Deep Learning for Improved Agricultural Risk Management

2019 | HICSS | Citations: 0
Authors: Newlands, Nathaniel K; Ghahari, Azar

Abstract: Deep learning provides many beneﬁts, including automation, speed, accuracy, and ... Expand

Abstract: Deep learning provides many beneﬁts, including automation, speed, accuracy, and intelligence, and it is delivering competitive performance now across a wide range of real-world operational applications – from credit card fraud detection to recommender systems and customer segmentation. Its potential in actuarial sciences and agricultural insurance/risk management, however, remains largely untapped. In this pilot study, we investigate deep learning in predicting agricultural yield in time and space under weather/climate uncertainty. We evaluate the predictive power of deep learning, benchmarking its performance against more conventional approaches alongside both weather station and climate. Our ﬁndings reveal that deep learning offers the highest predictive accuracy, outperforming all the other approaches. We infer that it also has great potential to reduce underwriting inefﬁciencies and insurance coverage costs associated with using more imprecise yield-based metrics of real risk exposure. Future work aims to further evaluate its performance, from municipal area-yield, to ﬁner-scale crop-speciﬁc producer-scale yield. Collapse

Semantic filters: deep belief network

Topics: information system use

Methods: deep learning computational algorithm principal component analysis data transformation machine learning

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Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

Deep Learning for Improved Agricultural Risk Management