Predicting credit card delinquencies: An application of deep neural networks

The objective of this paper is twofold. First, it develops a prediction system to help the credit card issuer model the credit card delinquency risk. Second, it seeks to explore the potential of deep learning (also called a deep neural network), an emerging artificial intelligence technology, in the credit risk domain. With real-life credit card data linked to 711,397 credit card holders from a large bank in Brazil, this study develops a deep neural network to evaluate the risk of credit card delinquency based on the client's personal characteristics and the spending behaviours. Compared with machine-learning algorithms of logistic regression, naive Bayes, traditional artificial neural networks, and decision trees, deep neural networks have a better overall predictive performance with the highest F scores and area under the receiver operating characteristic curve. The successful application of deep learning implies that artificial intelligence has great potential to support and automate credit risk assessment for financial institutions and credit bureaus.     

Mobile Commerce Website Success: Antecedents of Consumer Satisfaction and Purchase Intention

With the rapid development of apparel mobile commerce in the United States, more companies view mobile commerce as a new source of competitive advantage. Despite the importance of apparel mobile website quality and its effect on consumer satisfaction and future purchase stimulus, extant research has paid little attention to these topics. This study proposes a website quality–consumer satisfaction–purchase intention research model based on the self-regulatory process theory. Six dimensions of apparel mobile website quality—website visual appeal, apparel visual appeal, brand trust, website information quality, website response time, and website security—were investigated. In all, 293 eligible responses were collected via an online survey. Multiple regression analysis was utilized to test the proposed relationships. Results reveal that website information quality, website visual appeal, apparel visual appeal, and website security positively affect consumer satisfaction toward apparel mobile commerce websites, while website response time and brand trust show insignificant impacts on consumer satisfaction. With higher satisfaction on an apparel mobile commerce website, consumers are more likely to purchase apparel through the website.     

长三角港口腹地动态演变及耦合协调度研究

明确港口腹地演变趋势、分析港腹耦合协调度,对长三角地区港腹联动发展具有重要意义。量化港口系统—腹地系统综合指标评价体系,运用高斯烟羽模型在2006、2011、2016年3个时间节点上测算长三角地区主要港口影响强度,并对腹地范围进行划分,利用耦合协调模型定量分析港口系统和直接腹地经济系统耦合协调度。研究结果表明:长三角地区港口呈现"一超多强"的格局,以上海港为中心,各港口多极发展;港口腹地范围整体稳定,局部波动,上海港的边缘腹地不断缩减;港口与腹地的耦合协调度处于上升态势,宁波—舟山港腹耦合协调度最高。     

城市综合管理服务平台的框架探究

信息化平台能够串联起城市管理的各个环节,是提高城市综合管理效率与质量的关键工具。近年来,城市管理信息化平台建设进展迅猛,但平台内资源共享不足、部门间信息沟通不畅、各层级平台各自为政、主动监管监测不强等问题仍然突出,形成一个个信息孤岛,难以适应城市管理系统性整体性要求。从城市综合管理需求角度出发,提出理想的平台应由负责必需行政许可的城市管理子平台、负责信息处理的城市营运子平台以及服务市民的城市服务子平台组成,具备信息全面、权责明确、职能联动、实时主动监测等特点,服务城市基础功能维护和公共空间管理。提出应理顺保障机制,明确各部门之间的权责,在空间数据底图基础上,纳入各类资源数据,激活整合并形成城市管理、营运、服务三个子平台,支持城市管理决策,为市民及游客提供周到服务。     

Friends along supply chain and relationship-specific investments

Review of Quantitative Finance and Accounting - This study investigates whether the presence of school ties between supply chain partners encourages relationship-specific investments. Using Chinese...     

基于BIM技术的医院建筑设备运维管理研究

近年来,随着科学技术的不断发展,我国医疗水平也在逐步提升,医院建设标准越来越高,医院建筑设备越来越复杂,建筑设备运维管理的问题逐渐突显出来.本文通过分析研究现阶段医院建筑设备运维管理存在的问题,结合BIM技术的优势,提出在医院建筑设备运维管理中引入BIM技术,对医院建筑设备的运维管理方式、管理手段进行优化和改进,从而提高其运维效率、降低运维成本,为医院的长远可持续发展提供保障.     

小芯样法及其他混凝土抗压强度试验对比研究

混凝土的质量是决定建筑工程质量的重要因素,而混凝土抗压强度是反应混凝土质量最基本、最重要的指标.混凝土抗压强度的现场检测方法目前常用的有回弹法、超声回弹综合法和钻芯法.而随着混凝土强度的提高,钢筋间距的加密,小芯样法也逐渐被越来越多的人重视和研究.本文针对回弹法、超声回弹综合法和小芯样法展开试验研究,分析回弹法、超声回弹综合法和钻芯法检测混凝土抗压强度与标准试块强度之间的关系.     

决策可靠性分析及在SAR图像目标识别中的应用

决策融合是提高合成孔径雷达（Synthetic Aperture Radar,SAR）目标识别性能的重要手段，然而，可靠性较弱的决策往往会导致最终决策融合的效果变差。将可靠性分析引入基于决策融合的SAR目标识别方法中，分别计算各个决策的可靠性系数并选取可靠性的决策参与最终的决策融合。为了验证方法的有效性，分别将提出的可靠性分析应用于多特征决策融合以及多分类器决策融合并基于MSTAR（Moving and Stationary Target Acquisition and Recognition）数据集进行了目标识别实验。在基于主成分分析、线性鉴别分析和非负矩阵分解三种特征进行多特征决策融合的条件下，所提方法和直接进行决策融合的方法的识别率分别为97.47%和96.50%。在基于K近邻、支持向量机和稀疏表示分类器的多分类器决策融合中，所提方法和直接进行决策融合的方法的识别率分别为97.10%和96.28%。实验结果充分证明了所提方法的有效性。     

Do ongoing networks block out new friends? Reconciling the embeddedness constraint dilemma on new alliance partner addition

Research Summary: This study addresses a theoretical dilemma regarding how alliance network constraint (reflected by network cohesion) affects a firm’s alliance formation with new partners. Using a network pluralism approach, we separate a firm’s ego alliance network into two activity‐based networks—an exploratory network and an exploitative network—based on the primary value chain activity involved in each alliance. We argue that the cohesion of exploratory or exploitative networks has an inverted U‐shaped effect on the addition of new partners in the same activity‐based network, and a positive effect on the addition of new partners in the other network. Results based on data from the biotechnology industry largely support our predictions with one exception. Our study contributes to both scholarly understanding of network embeddedness and alliance practice. Managerial Summary: The structure of firms’ ongoing alliance networks may have paradoxical implications for their efforts to search for and form alliance with new partners. That is, when a firm’s alliance partners are tightly connected with each other, the cohesive network tends to both encourage and impede the focal firm to add new partners. We resolve this dilemma by showing that when a firm is deeply entrenched in a cohesive alliance network conducting a certain type of activities (e.g., R&D activities), it may not easily add new R&D alliance partners. However, it may still be able to escape from the cohesive R&D alliance network by seeking new partners conducting other activities (e.g., manufacturing activities).