Isbn: 9783659822049 - data quality assessment in credit risk management in banks: design, application and evaluation (10 risultati)

Condizione: New.

Taschenbuch. Condizione: Neu. Data Quality Assessment in Credit Risk Management in Banks | Design, Application and Evaluation | Muhammed ¿lyas Güne¿ | Taschenbuch | 152 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783659822049 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Paperback. Condizione: Brand New. 152 pages. 8.66x5.91x0.35 inches. In Stock.

paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -As the size and complexity of banks grow, the amount of data that their information systems need to handle also increases. This leads to the emergence of a variety of data quality (DQ) problems. Due to the possible economic losses due to such DQ issues, banks need to assure quality of their data via data quality assessment (DQA) techniques. This study presents a distinctive approach for data quality assessment in credit risk management. This approach grounds the selection of DQ dimensions on identification of data taxonomies for credit risk. Identification of data taxonomies with determination of data entities and attributes, followed by the development of DQ metrics based on the DQ dimension. DQ metrics are transformed into quality performance indicators in order to assess quality of credit risk data by means of DQA methods. Analysis of the results of DQA reveals the underlying causes of poor DQ performance. Identification of DQ problems and their major causes is followed by suggestion of appropriate improvement techniques based on the size, complexity and criticality of the problems in the context of credit risk management. 152 pp. Englisch.…

Condizione: New. Print on Demand.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Guenes Muhammed IlyasThe writer graduated from Industrial Engineering Department in Middle East Technical University (METU). He achieved his M.Sc. Degree at Information Systems program in METU. He is currently working as a banking spe.…

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - As the size and complexity of banks grow, the amount of data that their information systems need to handle also increases. This leads to the emergence of a variety of data quality (DQ) problems. Due to the possible economic losses due to such DQ issues, banks need to assure quality of their data via data quality assessment (DQA) techniques. This study presents a distinctive approach for data quality assessment in credit risk management. This approach grounds the selection of DQ dimensions on identification of data taxonomies for credit risk. Identification of data taxonomies with determination of data entities and attributes, followed by the development of DQ metrics based on the DQ dimension. DQ metrics are transformed into quality performance indicators in order to assess quality of credit risk data by means of DQA methods. Analysis of the results of DQA reveals the underlying causes of poor DQ performance. Identification of DQ problems and their major causes is followed by suggestion of appropriate improvement techniques based on the size, complexity and criticality of the problems in the context of credit risk management.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -As the size and complexity of banks grow, the amount of data that their information systems need to handle also increases. This leads to the emergence of a variety of data quality (DQ) problems. Due to the possible economic losses due to such DQ issues, banks need to assure quality of their data via data quality assessment (DQA) techniques. This study presents a distinctive approach for data quality assessment in credit risk management. This approach grounds the selection of DQ dimensions on identification of data taxonomies for credit risk. Identification of data taxonomies with determination of data entities and attributes, followed by the development of DQ metrics based on the DQ dimension. DQ metrics are transformed into quality performance indicators in order to assess quality of credit risk data by means of DQA methods. Analysis of the results of DQA reveals the underlying causes of poor DQ performance. Identification of DQ problems and their major causes is followed by suggestion of appropriate improvement techniques based on the size, complexity and criticality of the problems in the context of credit risk management.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 152 pp. Englisch.…