Small and medium-sized enterprises (SMEs) loans play an essential role in many aspects: including technological innovation, economic development, employment, and peoples livelihood, etc. In order to meet the loan evaluation criteria of commercial banks, many SMEs choose to guarantee each other to obtain loans, thus forming a complex guarantee network. If the borrower defaults on the loan, the risk will be diffused to its guarantors along with the contagion path, which may lead to systemic risk across the loan networks. This has brought severe challenges to the nations financial security and regulation. Thus, accurately rating the contagion path is an urgent task for systematic risk management in the loan network. Therefore, we present a deep learning-based approach to the risk rating of contagion paths in the bank industry. We leverage the graph neural network and attention mechanism on graph-structured loan behavior to learn high-order representations, which do not require handcraft feature engineering. We demonstrate that our approach outperforms the existing baselines with 2%$\\sim$15% improvements in risk rating and 3.5% in the newly constructed path rating problem. The result demonstrates the effectiveness of our proposed approach, which provides an effective method and theory basis for regulatory commissions and financial institutions to monitor systematic risks in networked-guarantee loans.

介绍目前国内信用评分的现状和存在的问题,认为国内信用评分的研发从征信机构到信贷部门都有很大的提升空间,并分析了消费信贷在数字金融场景下的特点及其对未来信用评分研发带来的挑战.对于面向新金融的信用评分的研发,建议从可替代数据源和信用模型等角度创新,要充分利用央行征信系统的数据,做好基础性指标体系搭建工作,同时也不能忽视个人隐私的挑战.进而对国内新兴征信机构百行征信开发信用评分提出建议,认为既要面向前沿应用,又要提高基础数据质量,相信央行征信系统的信用评分可以发挥更大的价值.

The peer-to-peer lending industry has experienced recent turmoil, posing risks to fintech companies and banks. Based on a random sample of 33,669 borrowers who had downloaded peer-to-peer lending platforms prior to submitting loan applications to a well-known fintech company, Du Xiaoman Financial (formerly Baidu Finance), this article evaluates the predictive power of borrowers' internet behaviours on credit default risk. After controlling for borrowers' basic characteristics that are widely used in academic research and enterprise practices, the coefficients of key factors selected from 3,100 variables are economically and statistically significant. The average Kolmogorov-Smirnov value of the prediction model calculated using the hold-out method is approximately 37.09%. The results remain robust in several additional analyses. This study indicates the importance of non-credit information, particularly borrowers' internet behaviours, in supplementing borrowers' credit records for both fintech companies and banks.

欧洲政策研究中心下属欧洲信用研究院和爱丁堡大学商学院联合发布了《欧洲全面征信信息共享对信贷市场发展作用实证研究》报告,通过欧盟23个国家的征信机构数据采集情况对信贷市场发展的影响进行实证分析,论证了全面征信信息共享对促进信贷市场发展的积极作用.对该报告的研究背景、研究方法、研究结论等进行解读与分析,能为我国信贷市场运行过程中全面的征信信息采集与共享提供借鉴与参考.

区块链是未来信息技术的一个重要方向,区块链技术和征信系统的结合是全球的研发热点.阐述征信的概念、信息技术在征信业发展过程中的作用以及征信市场的痛点,提出新技术背景下面临的挑战.为使区块链技术与征信更有效地结合,回顾区块链的产生、发展背景,对“初级版本”区块链和普通意义上的区块链进行详细解析,逐步发现区块链对构建一种新的征信视角的重要性.区块链也可以为传统征信体系提供技术架构.基于对新技术的包容态度,提出区块链监管的政策建议,并对区块链技术应用前景进行展望.

此次新冠肺炎疫情伴随着全球经济下行,作为金融基础设施的个人征信体系受到的冲击将是深远的,从消费者、消费金融到消费经济,都面临前所未有的风险和不确定性.全球的征信监管部门、行业协会和征信机构纷纷对这些问题进行讨论并采取措施以应对.

介绍美国受监管的专业征信机构及其服务于不同的消费生活场景,分析代表性的专业征信机构的基本业务,阐述专业征信机构和全面性(全国性)征信机构的关系.分析认为,美国专业征信机构为中国征信业的发展提供了借鉴和启示,2018年我国成立的百行征信是一个面向互联网信贷场景的专业征信机构,未来的专业征信机构是中国征信体系和信用机构服务培育的方向.

今年以来,在金融去杠杆的背景下,小微企业融资难的问题愈加凸显.2018年中,央行和全国工商联联合召开民营企业和小微企业金融服务座谈会探讨小微企业融资难问题,包括抵押物不足、发债难、政府和社会资本合作(PPP)“一刀切”、信用担保不到位四大难题.会议认为,信用体系建设是缓解小微企业融资难、融资贵问题的重要措施.

个人征信是消费金融和数字经济的重要基础设施,作为市场经济的产物,征信系统对于经济健康发展和金融市场稳定运行的重要性不言而喻. 中国的个人征信起步较晚,在本世纪初才开始筹建,但随着中国作为全球第二大经济体的崛起,国内消费金融日趋火爆、信贷市场日趋庞大,个人征信机构也逐渐受到越来越多的关注.

作为市场经济的产物,征信系统对于经济健康发展和金融市场稳定的重要性不言而喻.可以简单做一个类比,征信系统可以说是金融信息的高速公路,各种金融信贷平台可以理解成各种交通工具,风控就是驾驶技术,如果没有一个好的征信系统做基础,再好的驾驶技术也是没有意义的. 2017年4月21日,央行征信管理局举行了个人信息保护的国际学术研讨会,相关监管层领导表示全国八家个人征信机构中没有一家合格.这给征信业带来很大的冲击,震撼至今.时隔一年半,中国征信领域发生了一些变化,引起了社会的关注.本文对国内征信业发展进行梳理,并对2018年初成立的百行征信和未来征信业进行展望.

企业之间互保联保的担保圈模式是我国特有的一种信贷增信形式,这种模式在增进企业信用、提高信贷效率的同时,也将企业风险与行业风险紧紧地绑在一起.一个企业资金链断裂,一群企业乃至一个行业都会受到牵连.从担保圈风险的概念入手,分析担保圈风险的特点和现状,提出利用大数据技术实现担保圈风险控制的模型构想.

本文利用央行金融信用信息基础库的企业征信大数据,基于信贷视角对交通运输、仓储和邮政业进行分析.研究发现,十年间该行业的信贷规模不断扩大,尤其小微企业的增长速度更快.行业信贷资金主要流向北京、广东和上海等发达地区,但西部地区交通行业的企业融资规模增速更快.行业信贷质量较好,不良贷款率保持低位.

作为连接生产者和消费者桥梁和纽带的批发零售业,处于市场经济中最活跃的环节,对各种影响因素的反映敏感度要高于其他行业,其发展状况更是与宏观经济环境以及内部结构变化有着密切的关系.从批发零售业内部来看,批发业的主要销售对象是生产经营单位,且批发业销售额中的一半以上是生产资料商品,因而通过批发业的销售状况可以反映出国民经济中生产制造部门的状况,而通过批发零售业可以反映出消费领域的基本趋势.目前,从宏观经济走势来看,居民收入水平整体上处于较快上升阶段.从长远来看,中国居民消费无论是从总量上,还是从结构上,都有相当大的发展空间,这为中国批发零售行业发展提供了良好的中长期宏观环境.

Guaranteed loans are a common way for enterprises to raise money from banks without any collateral in China. The enterprises are highly intertwined with each other, and hence form a densely connected guarantee network. As the economy is down in recent years, the default risk spreads along with the guarantee relations, and has caused great financial risk in many regions of China. Thus it puts forward a new challenge for financial regulators to monitor the enterprises involved in the guarantee network and control the system risk. However, the traditional financial risk management are based on vector space models, and could not handle the relations among enterprises. In this paper, based on the k-shell decomposition method, we propose a novel risk evaluation strategy, NetRating, to assess the risk level of each enterprise involved in the guaranteed loans. Besides, to deal with the direct guarantee networks, we propose the directed k-shell decomposition method, and extend NetRating strategy to the directed NetRating strategy. The application of our strategy in the real data verifies its effectiveness in credit assessment. It indicates that our strategy can provide a novel perspective for financial regulators to monitor the guarantee networks and control potential system risk.

Science is a dynamic system. Its cognitive structure is subject to permanent changes. Any particular structural change, such as new research trends and emerging topics, can be detected in cognitive maps. In this paper, we attempt to identify new research trends and topic transition by detecting both changing structure and anomalous events in the course of mapping the structure of journal-based clustering. We form one community for each journal cluster, with nodes representing journals and edges representing the citation links among journals. To measure the significance of changes in the dynamic journal clusters, a community similarity measurement is required. However, the existing similarity algorithms neglect the shift of the internal structure in networks and graphs, which determines that these algorithms cannot be directly applied to journal clusters. We propose a novel community-similarity algorithm, which considers both the shifts of vertices and the shifts of communities' layered structure. Communities' layered structure categorizes nodes into different groups, depending on their influence on the community. We apply the novel algorithm on the temporal journal data set, and identify two types of anomalous events. Both the visualizations of the journal clusters and the text annotations demonstrate that the identified events correspond to new emerging trends in scientific fields.

Telecommunication big data is good alternative data source for credit evaluation of consumers.Meanwhile,credit risk management is a crucial task for telecommunication operators.In this work,the application of telecommunication big data in credit risk management was introduced.The application example of telecommunication big data in financial credit scenario was analyzed.Some suggestions were given about how to employ telecommunication big data to credit risk management in China.

The huge multisource data of enterprise credit system was applied to study strategies of industrial restructuring from the credit perspective of the past decade,and the interactive relationship between industrial restructuring and credit supply was studied.Furthermore,empirical analysis was took to study inter-industry credit resource of every economic area by establishing credit industrial change index.The studies show that the synchronization is existed between credited structure adjustment and industrial structure adjustment,however some industries and regions are obtained less credit support during the adjustment process.The research shows that credit data could provide decision support for the implementation of industrial restructuring and credit resources allocation.

在全球范围内,年轻消费者信贷市场具有很大的业务空间,但由于年轻消费者信用记录欠缺而发展缓慢.一些新兴P2P企业瞄准该市场,大胆尝试基于大数据的信用评估方法,其中美国的Upstart公司就是非常典型的一家.Upstart公司以其强大的智能数据管理和分析技术为基础,秉承风险投资理念,着眼于年轻消费者的未来潜力,这对刚刚起步的中国征信业和互联网金融具有一定的借鉴意义.

征信体系作为金融行业的基础设施,在现代经济活动中发挥着越来越重要的作用.美国作为信用管理行业高度发达的国家,在征信数据应用方面有许多值得借鉴的经验.对美国企业和个人征信数据的基本内容进行了介绍、分类、解释和说明,并通过应用案例具体论述了征信数据在金融保险业中的典型应用.通过借鉴分析不仅可以帮助国内征信机构加深对征信数据的理解,而且还能促进金融保险业更加广泛地使用征信数据进行风险监控、信贷决策、定价等,对提高国内征信数据在实践中的应用提供了参考.

2015年12月28日,经过五个月的征求意见,中国人民银行正式发布了《非银行支付机构网络支付业务管理办法》.该办法规定,III类支付账户(交易额年累计不超过20万元)如果使用非面对面方式,即线上开户,要通过五个以上合法安全的外部渠道,对身份基本信息多重交叉验证;对II类支付账户(交易额年累计不超过10万元)的开户,若仅通过线上方式核实身份,也要通过三个以上合法安全的外部渠道,对身份信息多重交叉验证.一石激起千层浪,高安全级别的身份验证办法(数字证书或电子签名)和相关的技术创新引起了争议和关注.