Data Quality Assessment in Credit Risk Management in Banks | Design, Application and Evaluation

Muhammed ¿lyas Güne¿

ISBN 10: 3659822043 / ISBN 13: 9783659822049

Published by LAP LAMBERT Academic Publishing, 2017

About this Item

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.

Seller Inventory # 108496930

Bibliographic Details

• Title: Data Quality Assessment in Credit Risk Management in Banks | Design, Application and Evaluation
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 3659822043
• ISBN 13: 9783659822049
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 10D millimeters 220 millimeters width by 150 millimeters height by 10 millimeters depth
• Weight: 244 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.

About the Author

The 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 specialist in Banking Regulation and Supervision Agency in Turkey. His study subjects are risk management, finance and information systems.