Access to this document was granted through an Emerald subscription provided by University of South Australia For Authors:If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service. Information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comWith over forty years' experience, Emerald Group Publishing is a leading independent publisher of global research with impact in business, society, public policy and education. In total, Emerald publishes over 275 journals and more than 130 book series, as well as an extensive range of online products and services. Emerald is both COUNTER 3 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to develop a model for upstream supply chain risk management linking risk identification, risk assessment and risk mitigation to risk performance and validate the model empirically. The effect of a continuous improvement process on identification, assessment, and mitigation is also included in the model. Design/methodology/approach -A literature review is undertaken to derive the hypotheses and operationalize the included constructs. The paper then tests the path analytical model using partial least squares analyses on survey data from 162 large and mid-sized manufacturing companies located in Germany. Findings -All items load high on their respective constructs and the data provides robust support to all hypothesized relationships. Superior risk identification supports the subsequent risk assessment and this in turn leads to better risk mitigation. The model explains 46 percent of the variance observed in risk performance. Research limitations/implications -This study empirically validates the sequential effect of the three risk management steps on risk performance as well as the influence of continuous improvement activities. Limitations of this study can be seen in the use of perceptional data from single informants and the focus on manufacturing firms in a single country. Practical implications -The detailed operationalization of the constructs sheds further light on the problem of measuring risk management efforts. Clear evidence of the performance effect of risk management provides managers with a business case to invest in such initiatives. Originality/value -This is one of the first large-scale, empirical studies on the process dimensions of upstream supply chain risk management.

Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations in discovering those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships among keywords and accounts over time. Furthermore, we introduce a search-by-example technique which extracts accounts that show similar transaction patterns. In collaboration with the Anti-Money Laundering division at Bank of America, we demonstrate that using our tool, investigators are able to detect accounts and transactions that exhibit suspicious behaviors.

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Financial crimes affect millions of people every year and financial institutions must employ methods to protect themselves and their customers. The use of statistical methods to address these problems faces many challenges. Financial crimes are rare events that lead to extreme class imbalances. Criminals deliberately attempt to conceal the nature of their actions and quickly change their strategies over time, resulting in class overlap and concept drift. In some cases, legal constraints and investigation delays make it impossible to actually verify suspected crimes in a timely manner, resulting in class mislabeling or unknown labels. In addition, the volume and complexity of financial data require algorithms to be not only effective, but also efficiently trained and executed. This article focuses on two important types of financial crimes: fraud and money laundering. It discusses some of the traditional statistical techniques that have been applied as well as more recent machine learning and data mining algorithms. The goal of the article is to introduce the subject and to provide a survey of broad classes of methodologies accompanied by selected illustrative examples.

A specific research stream within the purchasing and supply management literature focuses on the development of purchasing competence frameworks. We apply stakeholder theory and multiple methods of data collection to develop and confirm a hierarchy‐specific purchasing competence management framework for Chief Purchasing Officers and validate it using confirmatory factor analysis on empirical data from 124 multinational companies. The results reveal a significant relationship between Chief Purchasing Officers purchasing management competence and different purchasing performance measures confirming the appropriateness of stakeholder theory for such a competence framework.