Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Feature selection with Deep learning

Ha, Van-Sang; Lu, Dang-Nhac; Choi, Gyoo Seok; Nguyen, Ha-Nam; Yoon, Byoung-Su

doi:10.23919/icact.2019.8701943

Cited by 34 publications

(18 citation statements)

References 7 publications

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“…This is the first collection of images designed for continual object recognition, i.e., learning new classes online. It is composed of 11 sessions of 300 RGB-D images that can be classified by objects (50) or by categories (10). The objects are held and moved by the operator who is This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: B Datasetsmentioning

confidence: 99%

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Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

et al. 2020

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Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning (DL) is already present in many applications ranging from computer vision for medicine to autonomous driving of modern cars as well as other sectors in security, healthcare, and finance. However, to achieve impressive performance, these algorithms employ very deep networks, requiring a significant computational power, both during the training and inference time. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute-and energy-hungry. In a scenario where several sophisticated algorithms need to be executed with limited energy and low latency, the need for cost-effective hardware platforms capable of implementing energy-efficient DL execution arises. This paper first introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and Spiking Neural Network (SNN), and then analyzes techniques to produce efficient and high-performance designs. This work summarizes and compares the works for four leading platforms for the execution of algorithms such as CPU, GPU, FPGA and ASIC describing the main solutions of the state-of-the-art, giving much prominence to the last two solutions since they offer greater design flexibility and bear the potential of high energy-efficiency, especially for the inference process. In addition to hardware solutions, this paper discusses some of the important security issues that these DNN and SNN models may have during their execution, and offers a comprehensive section on benchmarking, explaining how to assess the quality of different networks and hardware systems designed for them.

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Section: B Datasetsmentioning

confidence: 99%

“…Their use is valuable for controlling web traffic [6] or for example, video surveillance [7]. • Business and Finance: Financial techs deploy such models to forecast market behavior [8], including insurance [9] and lending [10].…”

Section: Introductionmentioning

confidence: 99%

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

et al. 2020

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“…In this section, we conduct a case study with the dataset of the south German credit to show the explainability of the CBR system. The south German credit dataset is publicly available and widely used in the scientific field for research on credit risk prediction, such as the recent studies of Ha et al (2019), Alam et al (2020) and Trivedi (2020). The dataset provider offered a detailed description of the features, which are essential information to explain the results.…”

Section: Interpretation Of Resultsmentioning

confidence: 99%

A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection

Li¹,

Paraschiv²,

Sermpinis³

2021

Preprint

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The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field.

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“…While the lack of preprocessing makes the process more streamlined, the alternation of a large number of layers increases the accuracy making the DL the technique par excellence. These networks are called Deep Neural Networks (DNNs) and cover a wide range of applications, for instance, business and finance [2][3][4], healthcare such as cancer detection [5][6][7], up to robotics [8,9], and computer vision [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks

et al. 2020

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Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial Intelligence (AI) applications. Their ability to go beyond human precision has made these networks a milestone in the history of AI. However, while on the one hand they present cutting edge performance, on the other hand they require enormous computing power. For this reason, numerous optimization techniques at the hardware and software level, and specialized architectures, have been developed to process these models with high performance and power/energy efficiency without affecting their accuracy. In the past, multiple surveys have been reported to provide an overview of different architectures and optimization techniques for efficient execution of Deep Learning (DL) algorithms. This work aims at providing an up-to-date survey, especially covering the prominent works from the last 3 years of the hardware architectures research for DNNs. In this paper, the reader will first understand what a hardware accelerator is, and what are its main components, followed by the latest techniques in the field of dataflow, reconfigurability, variable bit-width, and sparsity.

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Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Feature selection with Deep learning

Cited by 34 publications

References 7 publications

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead

A Data-driven Explainable Case-based Reasoning Approach for Financial Risk Detection

An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks

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