Audit Analytics in the Financial Industry:
Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to ass...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Bingley :
Emerald Publishing Limited,
2019.
|
| Schriftenreihe: | Rutgers Studies in Accounting Analytics Ser.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes. |
| Beschreibung: | 4.1. Data Pre-Processing and Partitioning Phase |
| Beschreibung: | 1 online resource (247 pages) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 1787431738 9781787431737 9781787430853 1787430855 |
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| 245 | 1 | 0 | |a Audit Analytics in the Financial Industry |
| 260 | |a Bingley : |b Emerald Publishing Limited, |c 2019. | ||
| 300 | |a 1 online resource (247 pages) | ||
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| 505 | 0 | |a Intro; Audit Analytics in the Financial Industry; Contents; Introduction: What is Audit Analytics?; References; Part I: Audit Analytics Procedures; Chapter 1: An Application of Exploratory Data Analysis in Auditing -- Credit Card Retention Case*; 1. Introduction; 2. The Audit Problem; 2.1. Scenario; 2.2. Audit Objectives; 3. Methodology; 3.1. Data; 3.2. Data Preprocessing; 3.3. Applied EDA Techniques; 4. Results and Discussion; 4.1. Policy-violating Bank Representatives and Negative Discounts; 4.2. Lazy and Inactive Bank Representatives | |
| 505 | 8 | |a 4.3. Non-Negotiating Bank Representatives and Short Calls5. Conclusion; References; Chapter 2: Audit Analytics: A Field Study of Credit Card After-sale Service Problem Detection at a Major Bank; 1. Introduction; 2. Related Work; 3. Audit Analytics Protocol; 3.1. Scope of Internal Auditing Issues for Audit Analytics; 3.2. A General Protocol for Audit Analytics; 4. Field Study Description; 5. Implementing the Audit Analytics Protocol; 5.1. Identifying Business Scenarios; 5.2. Defining Audit Concern; 5.3. Understanding the Auditing Data; 5.4. Preparing the Data; 5.5. Selecting Methods | |
| 505 | 8 | |a 5.6. Analyzing the Data6. Presenting and Explaining Results; 6.1. Negative Discount Detection; 6.2. High Discount Analysis; 6.3. Optimal Discount Estimation; 6.4. Inactive Agents; 6.5. Short Call Analysis; 6.6. Graphic Analysis of the Relationship between Call Duration and Discounts Offered by Call Centers; 6.7. Regression Analysis; 6.8. Unsuccessful Retention Analysis; 6.9. Recommendations; 7. Conclusion; References; Part II: Analytics in Credit Card Audits; Chapter 3: Automated Clustering: From Concept to Reality; 1. Introduction; 2. Background; 3. Data | |
| 505 | 8 | |a 4. Discretization, Feature Selection/Creation, and Normalization5. Analysis and Results; 6. Conclusion; References; Ch apter 4: A Multi-faceted Outlier Detection Scheme for Use in Clustering*; 1. Introduction; 2. Preliminary Issues in Outlier Detection; 3. Distance Measures for Outlier Detection; 4. Similarity Measures for Outlier Detection; 5. Outlier Detection Method -- Final Considerations; 6. Analysis and Results; 7. Outlier Detection -- Auditing Context Example; 8. Conclusion; References | |
| 505 | 8 | |a Chapter 5: Are Customers Offered Appropriate Discounts? An Exploratory Study of Using Clustering Techniques in Internal Auditing1. Introduction; 2. Related Work; 3. Audit Problem; 4. Method; 4.1. Data Set Analysis; 4.2. Data Set Preprocessing; 4.3. Clustering Model Selection; 5. Experiment; 5.1. Evaluation Metric; 5.2. Parameterization; 5.3. Modeling; 5.4. Results; 6. Conclusion; References; Chapter 6: Predicting Credit Card Delinquency: An Application of the Decision Tree Technique; 1. Introduction; 2. Related Research; 3. Methodology; 4. Experiment and Results | |
| 500 | |a 4.1. Data Pre-Processing and Partitioning Phase | ||
| 520 | |a Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes. | ||
| 504 | |a Includes bibliographical references. | ||
| 650 | 0 | |a Financial services industry |x Auditing. | |
| 650 | 7 | |a Financial services industry |x Auditing |2 fast | |
| 700 | 1 | |a Vasarhelyi, Miklos A. | |
| 700 | 1 | |a Medinets, Ann F. | |
| 776 | 0 | 8 | |i Print version: |a Dai, Jun. |t Audit Analytics in the Financial Industry. |d Bingley : Emerald Publishing Limited, ©2019 |z 9781787430860 |
| 830 | 0 | |a Rutgers Studies in Accounting Analytics Ser. | |
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| contents | Intro; Audit Analytics in the Financial Industry; Contents; Introduction: What is Audit Analytics?; References; Part I: Audit Analytics Procedures; Chapter 1: An Application of Exploratory Data Analysis in Auditing -- Credit Card Retention Case*; 1. Introduction; 2. The Audit Problem; 2.1. Scenario; 2.2. Audit Objectives; 3. Methodology; 3.1. Data; 3.2. Data Preprocessing; 3.3. Applied EDA Techniques; 4. Results and Discussion; 4.1. Policy-violating Bank Representatives and Negative Discounts; 4.2. Lazy and Inactive Bank Representatives 4.3. Non-Negotiating Bank Representatives and Short Calls5. Conclusion; References; Chapter 2: Audit Analytics: A Field Study of Credit Card After-sale Service Problem Detection at a Major Bank; 1. Introduction; 2. Related Work; 3. Audit Analytics Protocol; 3.1. Scope of Internal Auditing Issues for Audit Analytics; 3.2. A General Protocol for Audit Analytics; 4. Field Study Description; 5. Implementing the Audit Analytics Protocol; 5.1. Identifying Business Scenarios; 5.2. Defining Audit Concern; 5.3. Understanding the Auditing Data; 5.4. Preparing the Data; 5.5. Selecting Methods 5.6. Analyzing the Data6. Presenting and Explaining Results; 6.1. Negative Discount Detection; 6.2. High Discount Analysis; 6.3. Optimal Discount Estimation; 6.4. Inactive Agents; 6.5. Short Call Analysis; 6.6. Graphic Analysis of the Relationship between Call Duration and Discounts Offered by Call Centers; 6.7. Regression Analysis; 6.8. Unsuccessful Retention Analysis; 6.9. Recommendations; 7. Conclusion; References; Part II: Analytics in Credit Card Audits; Chapter 3: Automated Clustering: From Concept to Reality; 1. Introduction; 2. Background; 3. Data 4. Discretization, Feature Selection/Creation, and Normalization5. Analysis and Results; 6. Conclusion; References; Ch apter 4: A Multi-faceted Outlier Detection Scheme for Use in Clustering*; 1. Introduction; 2. Preliminary Issues in Outlier Detection; 3. Distance Measures for Outlier Detection; 4. Similarity Measures for Outlier Detection; 5. Outlier Detection Method -- Final Considerations; 6. Analysis and Results; 7. Outlier Detection -- Auditing Context Example; 8. Conclusion; References Chapter 5: Are Customers Offered Appropriate Discounts? An Exploratory Study of Using Clustering Techniques in Internal Auditing1. Introduction; 2. Related Work; 3. Audit Problem; 4. Method; 4.1. Data Set Analysis; 4.2. Data Set Preprocessing; 4.3. Clustering Model Selection; 5. Experiment; 5.1. Evaluation Metric; 5.2. Parameterization; 5.3. Modeling; 5.4. Results; 6. Conclusion; References; Chapter 6: Predicting Credit Card Delinquency: An Application of the Decision Tree Technique; 1. Introduction; 2. Related Research; 3. Methodology; 4. Experiment and Results |
| ctrlnum | (OCoLC)1124605784 |
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| discipline | Wirtschaftswissenschaften |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-10-25T15:50:27Z |
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| language | English |
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| series | Rutgers Studies in Accounting Analytics Ser. |
| series2 | Rutgers Studies in Accounting Analytics Ser. |
| spelling | Dai, Jun. Audit Analytics in the Financial Industry Bingley : Emerald Publishing Limited, 2019. 1 online resource (247 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Rutgers Studies in Accounting Analytics Ser. Print version record. Intro; Audit Analytics in the Financial Industry; Contents; Introduction: What is Audit Analytics?; References; Part I: Audit Analytics Procedures; Chapter 1: An Application of Exploratory Data Analysis in Auditing -- Credit Card Retention Case*; 1. Introduction; 2. The Audit Problem; 2.1. Scenario; 2.2. Audit Objectives; 3. Methodology; 3.1. Data; 3.2. Data Preprocessing; 3.3. Applied EDA Techniques; 4. Results and Discussion; 4.1. Policy-violating Bank Representatives and Negative Discounts; 4.2. Lazy and Inactive Bank Representatives 4.3. Non-Negotiating Bank Representatives and Short Calls5. Conclusion; References; Chapter 2: Audit Analytics: A Field Study of Credit Card After-sale Service Problem Detection at a Major Bank; 1. Introduction; 2. Related Work; 3. Audit Analytics Protocol; 3.1. Scope of Internal Auditing Issues for Audit Analytics; 3.2. A General Protocol for Audit Analytics; 4. Field Study Description; 5. Implementing the Audit Analytics Protocol; 5.1. Identifying Business Scenarios; 5.2. Defining Audit Concern; 5.3. Understanding the Auditing Data; 5.4. Preparing the Data; 5.5. Selecting Methods 5.6. Analyzing the Data6. Presenting and Explaining Results; 6.1. Negative Discount Detection; 6.2. High Discount Analysis; 6.3. Optimal Discount Estimation; 6.4. Inactive Agents; 6.5. Short Call Analysis; 6.6. Graphic Analysis of the Relationship between Call Duration and Discounts Offered by Call Centers; 6.7. Regression Analysis; 6.8. Unsuccessful Retention Analysis; 6.9. Recommendations; 7. Conclusion; References; Part II: Analytics in Credit Card Audits; Chapter 3: Automated Clustering: From Concept to Reality; 1. Introduction; 2. Background; 3. Data 4. Discretization, Feature Selection/Creation, and Normalization5. Analysis and Results; 6. Conclusion; References; Ch apter 4: A Multi-faceted Outlier Detection Scheme for Use in Clustering*; 1. Introduction; 2. Preliminary Issues in Outlier Detection; 3. Distance Measures for Outlier Detection; 4. Similarity Measures for Outlier Detection; 5. Outlier Detection Method -- Final Considerations; 6. Analysis and Results; 7. Outlier Detection -- Auditing Context Example; 8. Conclusion; References Chapter 5: Are Customers Offered Appropriate Discounts? An Exploratory Study of Using Clustering Techniques in Internal Auditing1. Introduction; 2. Related Work; 3. Audit Problem; 4. Method; 4.1. Data Set Analysis; 4.2. Data Set Preprocessing; 4.3. Clustering Model Selection; 5. Experiment; 5.1. Evaluation Metric; 5.2. Parameterization; 5.3. Modeling; 5.4. Results; 6. Conclusion; References; Chapter 6: Predicting Credit Card Delinquency: An Application of the Decision Tree Technique; 1. Introduction; 2. Related Research; 3. Methodology; 4. Experiment and Results 4.1. Data Pre-Processing and Partitioning Phase Split into six parts, contributors explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry. Chapters include topics such as fraud risks in the credit card sector, clustering techniques, fraud and anomaly detection, and using Audit Analytics to assess risk in the lawsuit and payment processes. Includes bibliographical references. Financial services industry Auditing. Financial services industry Auditing fast Vasarhelyi, Miklos A. Medinets, Ann F. Print version: Dai, Jun. Audit Analytics in the Financial Industry. Bingley : Emerald Publishing Limited, ©2019 9781787430860 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2198488 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2198488 Volltext |
| spellingShingle | Dai, Jun Audit Analytics in the Financial Industry Rutgers Studies in Accounting Analytics Ser. Intro; Audit Analytics in the Financial Industry; Contents; Introduction: What is Audit Analytics?; References; Part I: Audit Analytics Procedures; Chapter 1: An Application of Exploratory Data Analysis in Auditing -- Credit Card Retention Case*; 1. Introduction; 2. The Audit Problem; 2.1. Scenario; 2.2. Audit Objectives; 3. Methodology; 3.1. Data; 3.2. Data Preprocessing; 3.3. Applied EDA Techniques; 4. Results and Discussion; 4.1. Policy-violating Bank Representatives and Negative Discounts; 4.2. Lazy and Inactive Bank Representatives 4.3. Non-Negotiating Bank Representatives and Short Calls5. Conclusion; References; Chapter 2: Audit Analytics: A Field Study of Credit Card After-sale Service Problem Detection at a Major Bank; 1. Introduction; 2. Related Work; 3. Audit Analytics Protocol; 3.1. Scope of Internal Auditing Issues for Audit Analytics; 3.2. A General Protocol for Audit Analytics; 4. Field Study Description; 5. Implementing the Audit Analytics Protocol; 5.1. Identifying Business Scenarios; 5.2. Defining Audit Concern; 5.3. Understanding the Auditing Data; 5.4. Preparing the Data; 5.5. Selecting Methods 5.6. Analyzing the Data6. Presenting and Explaining Results; 6.1. Negative Discount Detection; 6.2. High Discount Analysis; 6.3. Optimal Discount Estimation; 6.4. Inactive Agents; 6.5. Short Call Analysis; 6.6. Graphic Analysis of the Relationship between Call Duration and Discounts Offered by Call Centers; 6.7. Regression Analysis; 6.8. Unsuccessful Retention Analysis; 6.9. Recommendations; 7. Conclusion; References; Part II: Analytics in Credit Card Audits; Chapter 3: Automated Clustering: From Concept to Reality; 1. Introduction; 2. Background; 3. Data 4. Discretization, Feature Selection/Creation, and Normalization5. Analysis and Results; 6. Conclusion; References; Ch apter 4: A Multi-faceted Outlier Detection Scheme for Use in Clustering*; 1. Introduction; 2. Preliminary Issues in Outlier Detection; 3. Distance Measures for Outlier Detection; 4. Similarity Measures for Outlier Detection; 5. Outlier Detection Method -- Final Considerations; 6. Analysis and Results; 7. Outlier Detection -- Auditing Context Example; 8. Conclusion; References Chapter 5: Are Customers Offered Appropriate Discounts? An Exploratory Study of Using Clustering Techniques in Internal Auditing1. Introduction; 2. Related Work; 3. Audit Problem; 4. Method; 4.1. Data Set Analysis; 4.2. Data Set Preprocessing; 4.3. Clustering Model Selection; 5. Experiment; 5.1. Evaluation Metric; 5.2. Parameterization; 5.3. Modeling; 5.4. Results; 6. Conclusion; References; Chapter 6: Predicting Credit Card Delinquency: An Application of the Decision Tree Technique; 1. Introduction; 2. Related Research; 3. Methodology; 4. Experiment and Results Financial services industry Auditing. Financial services industry Auditing fast |
| title | Audit Analytics in the Financial Industry |
| title_auth | Audit Analytics in the Financial Industry |
| title_exact_search | Audit Analytics in the Financial Industry |
| title_full | Audit Analytics in the Financial Industry |
| title_fullStr | Audit Analytics in the Financial Industry |
| title_full_unstemmed | Audit Analytics in the Financial Industry |
| title_short | Audit Analytics in the Financial Industry |
| title_sort | audit analytics in the financial industry |
| topic | Financial services industry Auditing. Financial services industry Auditing fast |
| topic_facet | Financial services industry Auditing. Financial services industry Auditing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2198488 |
| work_keys_str_mv | AT daijun auditanalyticsinthefinancialindustry AT vasarhelyimiklosa auditanalyticsinthefinancialindustry AT medinetsannf auditanalyticsinthefinancialindustry |