Deep Convolutional Neural Networks Versus Multilayer Perceptron for Financial Prediction

Neagoe, Victor-Emil; Ciotec, Adrian-Dumitru; Cucu, George-Sorin

doi:10.1109/iccomm.2018.8453730

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…Li et al [16] conducted a study of deep learning with clustering and merging and the results showed high prediction accuracies. Deep Multi-Layer Perceptron (DMLP) and Deep CNN (DCNN) were used by Neagoe et al [17] to assess the credit worthiness of applicants. The DCNN significantly outperformed DMLP.…”

Section: Related Workmentioning

confidence: 99%

Making Deep Learning-Based Predictions for Credit Scoring Explainable

Dastile

Çelik

2021

IEEE Access

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Credit scoring has become an important risk management tool for money lending institutions. Over the years, statistical and classical machine learning models have been the most researched risk management tools in credit scoring literature, and recently the focus has turned to deep learning models. This transition is due to better performances that are shown by deep learning models in different domains. Despite deep learning models' superior performances, there is still a need for explaining how these models make their predictions. The non-transparency nature of deep learning models has created a bottleneck for their use in credit scoring. Explanations of decisions are important for lending institutions since it is a requirement for automated decisions that are generated by non-transparent models to be explained. The other issue in using deep learning models, specifically 2D Convolutional Neural Networks (CNNs), in credit scoring is the need to have the data in image format. We propose an explainable deep learning model for credit scoring which can harness the performance benefits offered by deep learning and yet comply with the legislation requirements for the automated decision-making processes. The proposed method converts tabular datasets into images and thus allowing the application of 2D CNNs in credit scoring. Each pixel of the image corresponds to a feature bin of the tabular dataset. The predictions from the 2D CNNs were explained using state-of-the-art explanation methods. Furthermore, explanations were evaluated using a sanity check methodology and also performances of the explanation methods were compared quantitatively. The proposed explainable deep learning model outperforms the other credit scoring methods on publicly available credit scoring datasets.

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Section: Related Workmentioning

confidence: 99%

Making Deep Learning-Based Predictions for Credit Scoring Explainable

Dastile

Çelik

2021

IEEE Access

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“…The proposed model incorporates a CNN model with high-dimensional business tabular data; the related works are described briefly as follows. Neagoe et al [22] studied a DCNN model versus an MLP model for financial predictions. Their experimental results confirmed the effectiveness of the DCNN model for credit scoring using bank transaction data.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Na²,

Park

et al. 2021

Applied Sciences

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Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

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“…Usually, a gradient-descent algorithm called back-propagation is used to train an MLP. In this algorithm, a maximum error is defined to be used as a criterion to stop the iterative weight update process [14].…”

Section: Brief Review Of Machine Leaning-based Modelsmentioning

confidence: 99%

Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans

Tabrizchi

Mosavi

Szabo-Gali

et al. 2020

Preprint

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Several studies suggest that COVID-19 may be accompanied by symptoms such as a dry cough, muscle aches, sore throat, and mild to moderate respiratory illness. The symptoms of this disease indicate the fact that COVID-19 causes noticeable negative effects on the lungs. Therefore, considering the health status of the lungs using X-rays and CT scans of the chest can significantly help diagnose COVID-19 infection. Due to the fact that most of the methods that have been proposed to COVID-19 diagnose deal with the lengthy testing time and also might give more false positive and false negative results, this paper aims to review and implement artificial intelligence (AI) image-based diagnosis methods in order to detect coronavirus infection with zero or near to zero false positives and false negatives rates. Besides the already existing AI image-based medical diagnosis method for the other well-known disease, this study aims on finding the most accurate COVID-19 detection method among AI methods such as machine learning (ML) and artificial neural network (ANN), ensemble learning (EL) methods.

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Deep Convolutional Neural Networks Versus Multilayer Perceptron for Financial Prediction

Cited by 12 publications

References 6 publications

Making Deep Learning-Based Predictions for Credit Scoring Explainable

Making Deep Learning-Based Predictions for Credit Scoring Explainable

Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Rapid COVID-19 Diagnosis Using Deep Learning of the Computerized Tomography Scans

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