Enterprise Information Risk von Edu Pristine

Dozent des Vortrages Enterprise Information Risk

Edu Pristine

Trusted by Fortune 500 Companies and 10,000 Students from 40+ countries across the globe, EduPristine is one of the leading International Training providers for Finance Certifications like FRM®, CFA®, PRM®, Business Analytics, HR Analytics, Financial Modeling, Operational Risk Modeling etc. It was founded by industry professionals who have worked in the area of investment banking and private equity in organizations such as Goldman Sachs, Crisil - A Standard & Poors Company, Standard Chartered and Accenture.

EduPristine has conducted corporate training for various leading corporations and colleges like JP Morgan, Bank of America, Ernst & Young, Accenture, HSBC, IIM C, NUS Singapore etc. EduPristine has conducted more than 500,000 man-hours of quality training in finance.
http://www.edupristine.com

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Auszüge aus dem Begleitmaterial

... system of processing and distributing critical risk information within and outside the firm. And firms that fared well in the recent crisis had a comprehensive approach to viewing firm-wide exposures ...

... system must have a process to monitor the firm’s risk continuously. Risk profile changes due to change in the portfolio of the firm or change in the market environment. The frequency of portfolio monitoring is the function of the firms portfolio of business. A firm engaged in large trading operations will require much ...

... Lack of Historical Data: Information about the structured finance market is not available to the market as these products were invented recently. This problem doesn’t have an easy solution to it, and the risk measures derived through this can’t be relied on with confidence. Lack of Data Completeness: In managed CDOs details of the portfolio were not available in timely manner ...

... of data can be because of the lack of data integrity, ambiguous meaning of data and lack ...

... solve the problem of data quality must recognize the problem as organizational problem rather than a mere technical problem. This comprehensive approach to solve the problem of data quality has following steps: Assessing Current State: The current state of the data quality is to be understood first, which ...

... Accessibility of the data. Flexibility of data. Extensibility of the data to new environments ...

... figure below shows all the seven components required for holistic Risk Information Management System. The innermost circle contains the data which makes it easy for all the users of the data to access ...

... security of the data. Reference or Master Data: Reference data is the static data, problems in which can impact information quality. Data Quality: This ensures the completeness, accuracy and reliability of the data. Metadata: It helps in defining the data semantics. Data Governance: Data governance ensures ...

... early benefits which in turn will ensure a growing base of advocates as well as provide program managers with success stories to use in giving the idea to potential new participants. It proved beneficial to implement the program in small ...

... Designing a future-state data environment i.e. setting the goal for the current data environment. Developing the roadmap to the ...