银行转账诈骗信息泛滥引发的思考

近年来,通过银行转账诈骗信息实施诈骗、损害金融消费者利益的事件频繁发生,破坏了金融环境。通过阐述银行转账诈骗行为的特征,从公众保护意识、群发短信审核、银行账户管理、行业立法监管等方面探讨该诈骗行为泛滥的原因,分析包括事前风险提示、银行协助冻结及紧急止付、不良信息举报等现行应对措施及存在的不足,提出从加强部门监管、加大责任追究等角度来有效打击该诈骗行为的建议。     

金融诈骗犯罪初探

随着我国改革开放的不断深入,社会主义市场经济体制的逐步确立,我国金融业得到了极大的发展,金融场所的设立和扩充,金融流通形式的增加都可谓空前。与此同时高智能、多方式的金融诈骗犯罪骤然增多,已成为当前中国经济犯罪活动中危害最大的犯罪类型之一。金融诈骗犯罪不仅严重地扰乱了我国金融市场和金融管理秩序而且阻碍了市场经济的健康发展。防范和打击金融诈骗犯罪活动己显得刻不容缓。一、金融诈骗犯罪的现状建国后至SO年代初金融诈骗犯罪在同类犯罪中最得并不突出。诈骗的方式较为简单,诈骗数额也较小。SO年代以后,尤其是gO年…     

对信用卡诈骗案件频发的成因分析及对策研究——以大庆市为例

近年来信用卡诈骗犯罪呈逐年上升趋势,已经成为最突出的金融犯罪类型之一,严重威胁金融秩序和社会稳定。本文结合大庆市地方法院近两年审理的信用卡诈骗犯罪案例特点,分别从持卡人、发卡银行和管理者角度分析信用卡诈骗犯罪高发的深层次原因,并提出相应政策建议。     

如何防范银行卡犯罪？

一、问题的提出随着银行计算机应用技术不断深入,利用高科技进行金融犯罪日趋严重,特别是银行卡业务的安全问题已经引起了人们重视。近年来,有关银行卡诈骗的案件急剧上升,占金融案件的比例越来越大,其主要原因有以下方面:第一,银行卡交易系     

金融诈骗犯罪若干问题探讨

当前我国金融市场有点混乱和无序,金融违法、违规现象频发,金融诈骗犯罪突出,严重扰乱了金融秩序,阻碍了国民经济的正常发展。金融诈骗案件具有复杂性、多样性、金额巨大等特点,其产生是社会法律制度、监控和管理不足等多方因素综合作用的结果。必须重视金融诈骗犯罪的预防,完善法律和规章制度,特别要进一步完善我国刑事金融立法,及时有效地打击日益猖獗的金融诈骗犯罪。     

上海信用卡诈骗犯罪的现状、原因与防治对策

近年来,随着信用卡产业的快速发展,信用卡诈骗犯罪呈现高发、多发态势,成为当前金融犯罪中最突出、最严重的问题之一。信用卡诈骗犯罪不仅严重侵犯了银行信用卡管理秩序和公私财产所有权,而且还是产生其他违法犯罪活动的重要诱因。本文对2010年度上海法院审结信用卡诈骗犯罪的基本情况进行了梳理，并对信用卡诈骗犯罪高发、多发的原因进行剖析，有针对性地提出对策，以期为惩治信用卡诈骗犯罪提供一定的帮助。     

金融诈骗犯罪的风险防范

随着金融体制改革的深入,在金融领域内的犯罪活动也急剧增加,尤其是诈骗犯罪较为突出。据不完全统计,2004年1—10月份全国共发生金融票据诈骗案件2120多起,2004年金融票据涉案金额高达42．63亿元,占金融犯罪金额的一半。金融诈骗犯罪已成为我囤经济领域危害最大的犯罪类型之一。     

积极协防电信诈骗 打造最安全的银行

近年来．随着通信,金融．网络技术的迅猛发展,各种类型的电信诈骗犯罪在我国快速蔓延．数以亿计的民众曾接收过诈骗信息．数以百万人上当受骗,涉案资金达数十亿之巨．在不少地区电信诈骗案件数量甚至已超越盗窃案件成为珊事犯罪中发案率最高的种类．给人民群众财产安全和社会和谐稳定构成了巨大威胁,本文通过对电信诈骗犯罪进行深入分析,介绍了工商银行在协防电信诈骗方面做出的努力,并就全社会如何有效防范和打击此类犯罪提出了一些建议。     

银行卡欺诈与防范对策探析

随着银行卡业务迅速扩张和竞争日趋激烈,各类银行卡犯罪也相伴而生,信用卡套现、伪卡欺诈、ATM资金诈骗及短信和电话转账诈骗等案件日益增加。银行卡犯罪手段不断向高科技化、集团化、专业化和规模化发展,案件实施过程更为隐蔽,手法不断翻新,对银行和持卡人的资金安全构成了严重威胁。     

构建线上反欺诈智慧风控体系筑牢数字金融风险防线

近年来,电信网络诈骗犯罪形势严峻复杂,电信网络诈骗活动日益猖獗。工商银行作为国有大型银行深入践行金融工作政治性和人民性,统筹安全和发展,聚焦线上交易,积极应用大数据、人工智能、生物识别等新技术和支付限额管理等工具,构建线上反欺诈智慧风控体系,保护客户资金安全,以数字化转型助力网络金融业务反欺诈水平全面提升,为数字金融业务发展保驾护航。     

证券期货市场人工智能交易的刑法规制

证券期货市场人工智能交易具有强大的预测分析能力、投资策略制定能力以及敏锐的市场反应能力。与之相适应,刑法对证券期货犯罪的规制重点也应当从交易的行为方式向交易技术转移。鉴于利用人工智能交易可以实施滥用技术优势型市场操纵犯罪行为,刑法有必要对人工智能交易予以规制,并且这种规制利大于弊。规制应当明确区分人工智能交易的正当使用和滥用,并进一步完善操纵证券、期货市场罪的规定。     

Do they do it for the money?

Using a sample of all top management who were indicted for illegal insider trading in the United States for trades during the period 1989-2002, we explore the economic rationality of this white-collar crime. If this crime is an economically rational activity in the sense of Becker (1968), where a crime is committed if its expected benefits exceed its expected costs, “poorer” top management should be doing the most illegal insider trading. This is because the “poor” have less to lose (present value of foregone future compensation if caught is lower for them). We find in the data, however, that indictments are concentrated in the “richer” strata after we control for firm size, industry, firm growth opportunities, executive age, the opportunity to commit illegal insider trading, and the possibility that regulators target the “richer” strata. We thus rule out the economic motive for this white-collar crime, and leave open the possibility of other motives.     

The Effectiveness of White‐Collar Crime Enforcement: Evidence from the War on Terror

This paper analyzes the impact of changes in regulatory priorities and resource allocation on criminal enforcement of white‐collar criminal activities. Using the 9/11 terrorist attacks as a shock to the FBI's priorities and allocation of investigative resources, as well as variation in the Muslim population in the United States, I examine whether prioritization of counterterrorism investigations after 9/11 is associated with weaker enforcement of laws targeting white‐collar crime. I then use a difference‐in‐differences estimation to study the magnitude of any increase in white‐collar crime resulting from reduced oversight. I find a significantly greater reduction in white‐collar criminal cases referred by FBI field offices that shifted more of their investigative focus away from white‐collar crime to counterterrorism. Further, geographic areas in the jurisdictions of FBI field offices with greater shifts in attention from white‐collar crime to counterterrorism experienced greater increases in wire fraud, illegal insider‐trading activities, and fraud within financial institutions.     

Location Matters: An Examination of Trading Profits

The electronic trading system Xetra of the German Security Exchange provides a unique data source on the equity trades of 756 professional traders located in 23 different cities and eight European countries. We explore informational asymmetries across the trader population: Traders located outside Germany in non-German-speaking cities show lower proprietary trading profit. Their underperformance is not only statistically significant, it is also of economically significant magnitude and occurs for the 11 largest German blue-chip stocks. We also examine whether a trader location in Frankfurt as the financial center, or local proximity of the trader to the corporate headquarters of the traded stock, or affiliation with a large financial institution results in superior trading performance. The data provide no evidence for a financial center advantage or of increasing institutional scale economies in proprietary trading. However, we find evidence for an information advantage due to corporate headquarters proximity for high-frequency (intraday) trading.     

金融诈骗罪刑法单独设节的理性思考

现行刑法将金融诈骗罪单独设节作为刑法分则第三章第五节从而与分则第三章第四节破坏金融管理秩序罪形成独立、并列的关系。这种被视为突破刑法立法体例传统的做法引起理论上的争议。肯定金融诈骗罪单独设节的观点从社会危害性、罪刑法定原则、金融诈骗罪罪群以及便利实务适用等四个方面论证了这种做法的合理性,否定说的学者认为金融诈骗罪单独设节具有与立法体例不合、立法思路不一、无单独设节之必要以及与境外刑法立法趋势相悖等缺陷从而不应单独设节。本文从金融诈骗罪主要客体、金融犯罪逻辑体系以及刑事立法传统等方面论证了金融诈骗罪单独设节不具有合理性且一一回应了肯定说所提出的观点。本文认为,金融诈骗罪的主要客体是金融交易秩序,其是金融管理秩序的下位概念,金融诈骗罪应当划入破坏金融管理秩序罪分节之下。     

Non-technical skills for managing risk and performance in financial trading

Recent large-scale failures in financial institutions have been found to be caused, in-part, by human factors-related issues in financial trading. In other environments where risk management and performance are intertwined, a human factors approach is often adopted to understand how the ‘non-technical skills (NTS)’ (leadership (LD), decision-making (DM), situation awareness (SA), teamwork) of organisational actors influence outcomes. Yet, to date, there has been minimal application of human factors research in financial trading. This study (i) identifies ‘real-world’ (i.e. non-laboratory) research studies investigating the NTS important for performance in financial trading, (ii) examines and synthesises data on the NTS found to underpin good or poor performance and (iii) considers the quality and coverage of research investigating NTS in financial trading, and identifies potential areas for future research. Nineteen studies were identified through a systematic literature search and then content-analysed for associations between NTS and performance in financial trading. The review found a range of decision-making (e.g. heuristics and biases, intuitive DM, emotional regulation) and LD skills (e.g. setting standards, monitoring behaviour, encouraging speaking-up) to have been identified as important for managing risk and performance in financial trading environments. Furthermore, SA (e.g. information search and assessment strategies, vigilance, identifying ‘noise’ data) and teamwork (e.g. avoiding ‘role’ conflict, communication between traders) were found to be important, yet remain less explored within the literature, and should be the focus of future research. NTS appear essential for effective risk management within the financial sector, yet further field research is required to examine the context-relevant behaviours that underpin safe activity. This will facilitate the development of evidence-based systems for assessing and training NTS competencies.     

The role of attribute selection in Deep ANNs learning framework for high‐frequency financial trading

In financial trading, technical and quantitative analysis tools are used for the development of decision support systems. Although these traditional tools are useful, new techniques in the field of machine learning have been developed for time‐series forecasting. This paper analyses the role of attribute selection on the development of a simple deep‐learning ANN (D‐ANN) multi‐agent framework to accomplish a profitable trading strategy in the course of a series of trading simulations in the foreign exchange market. The paper evaluates the performance of the D‐ANN multi‐agent framework over different time spans of high‐frequency (HF) intraday asset time‐series data and determines how a set of the framework attributes produces effective forecasting for profitable trading. The paper shows the existence of predictable short‐term price trends in the market time series, and an understanding of the probability of price movements may be useful to HF traders. The results of this paper can be used to further develop financial decision‐support systems and autonomous trading strategies for the financial market.     

期货内幕交易罪立法之国际比较

期货内幕交易行为是期货市场的病毒,严重扰乱了期货市场秩序,其危害是导致了不公平竞争和非诚实信用.分析内幕交易行为的犯罪构成,比较各国(地区)禁止期货内幕交易行为的立法例.对于内幕交易行为的惩处方式,可增设剥夺从业资格.     

An analysis of commodity markets: What gain for investors?

In this paper we study whether the commodity futures market predicts the commodity spot market. Using historical daily data on four commodities—oil, gold, platinum, and silver—we find that they do. We then show how investors can use this information on the futures market to devise trading strategies and make profits. In particular, dynamic trading strategies based on a mean–variance investor framework produce somewhat different results compared with those based on technical trading rules. Dynamic trading strategies suggest that all commodities are profitable and profits are dependent on structural breaks. The most recent global financial crisis marked a period in which commodity profits were the weakest.     

Asymmetric trading by insiders – comparing abnormal returns and earnings prediction in Spain and Australia

This paper examines whether the ‘external governance’ imposed by comparative financial accounting standards reduces the trading advantage of insiders. We do this by directly comparing insider trading returns and insider’s ability to predict future earnings from accruals in Spain and Australia. Results show higher excess returns and greater prediction of future earnings from conditioned insider trading in Australia that is then utilized by financial analysts to lower forecast errors – particularly in contrarian‐based accruals trading. Possible explanations include: (i) a high asymmetric quality for market‐based accruals, (ii) information transfer from informed insiders to uninformed insiders and financial analysts and (iii) a more timely dissemination of financial information in Spain through different ownership and governance structures.