Developing a quality score methodology for financial reporting

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2006.Includes bibliographical references (p. 85).Money laundering is a huge problem that is faced by financial institutions and banks are mandated by the government to have strict policies and procedures towards anti-money laundering reporting. The outcome of a complex investigation process is a Suspicious Activity Report (SAR) which is used by Financial Crimes Enforcement Network (FINCEN) to trace back criminals and to curb the money laundering activities. This thesis work involves developing a quality rating methodology for SAR reporting at the bank and rates the critical elements in determining the quality of a SAR. The results of the quality scoring mechanism have been validated by using Multivariate tools and techniques. Mahalanobis- Taguchi Strategy (MTS) and Principal Component Analysis (PCA) were used to come up with the measurement scale to distinguish the good and bad SARs and PCA was used to list the critical elements that were listed by the MTS approach. The results of the analysis were presented towards the end of the thesis and this methodology can be used to determine the quality rating of SARs.by Srinivasan Nallasivan.S.M

Do Metadata Models meet IQ Requirements?

Research has recognized the importance of analyzing information quality (IQ) for many different applications: The success of data integration greatly depends on the quality of the individual data. In statistical applications poor data quality often leads to wrong conclusions. High information quality is literally a vital property of hospital information systems. Poor data quality of stock price information services can lead to economically wrong decisions. Several projects have analyzed this need for IQ metadata and have proposed a set of IQ criteria or attributes which can be used to properly assess information quality. In this paper we survey and compare these approaches. In a second step we take a look at existing prominent proposals of metadata models, especially those on the Internet. Then, we match these models to the requirements of information quality modeling. Finally, we propose a quality assurance procedure for the assurance of metadata models