Data Quality Assessment in Credit Risk Management in Banks: Design, Application and Evaluation
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Lieferung aus: Schweiz, Lieferzeit: 11 Tage, zzgl. Versandkosten.
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.

Lieferung aus: Deutschland, Versandkostenfrei.
Data Quality Assessment in Credit Risk Management in Banks: 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. Englisch, Taschenbuch.

Lieferung aus: Deutschland, Versandkostenfrei.
Data Quality Assessment in Credit Risk Management in Banks: 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. Englisch, Taschenbuch.

Lieferung aus: Deutschland, Versandkosten nach: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Syndikat Buchdienst, [4235284].
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. 2017, Taschenbuch / Paperback, Neuware, H: 220mm, B: 150mm, 152, Internationaler Versand, Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung.

Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, European-Media-Service Mannheim [1048135], Mannheim, Germany.
This item is printed on demand for shipment within 3 working days.

Data Quality Assessment in Credit Risk Management in Banks ab 55.9 EURO Design, Application and Evaluation.

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Von Händler/Antiquariat, M & L aus Deutschland.
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