Audit Analytics in the Financial Industry

Description

In Audit Analytics in the Financial Industry, editors Jun Dai, Miklos A. Vasarhelyi and Ann F. Medinets bring together a cast of expert contributors to explore ways to integrate Audit Analytics techniques into existing audit programs for the financial industry.  Separated into six parts, the contributors take a variety of approaches to this exploration. In Part One, the contributors present two articles illustrating the process of applying Audit Analytics to solving audit problems. Part Two contains four studies that use various Audit Analytics techniques to discover fraud risks and potential frauds in the credit card sector. In Part Three, the chapter focus on the insurance sector and show the application of clustering techniques in auditing. Part Four includes two chapters on how to employ Audit Analytics in the transitory system for fraud/anomaly detection. Finally, Parts Five and Six illustrate the use of Audit Analytics to assess risk in the lawsuit and payment processes.   For students, researchers, and professionals in the accounting sector, this is an unmissable read exploring the latest research in Audit Analytics. 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 Calls 5. 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 Data 6. 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 Normalization 5. 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 Auditing 1. 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 4.2. Delinquency Modeling Phase 4.3. Performance Evaluation Phase 5. Conclusion References Part III: Analytics in Insurance Audits Chapter 7: Cluster Analysis for Anomaly Detection in Accounting* 1. Introduction 2. Exploratory Data Analysis 3. Cluster Analysis for Data Exploratory Purposes 4. The Audit Problem 5. Data 5.2. Attributes 5.3. Transitory Account for Debit Reclassification of Checking Account 6. Parsing Procedure 7. Banks Processing System 7.1. System Identification 7.2. Clustering Procedure 7.3. Results 8. Conclusions References Chapter 8: Multi-dimensional Approaches to Anomaly Detection: A Study of Insurance Claims* 1. Introduction 2. Related Literature 2.1. Insurance Outlier Detection 2.2. Belief Function 3. Data 4. Continuous Auditing Framework for Life Insurance 5. Claims Anomalies Detection 5.1. Is the Claim Settlement Reasonable? 5.2. Is the Claim Itself Legitimate? 6. Risk Scoring 7. Premium Outliers Detection 7.1. Factors Affecting Premium Calculations 7.2. Robust Regression 7.3. The Model 7.4. SAS ROBUSTREG 7.5. Results 8. Conclusion and Limitations References Part IV: Audit Analytics in Transitory Systems Chapter 9: Development of an Anomaly Detection Model for a Bank’s Transitory Account System 1. Introduction 2. Objectives 3. Methodology 3.1. Phase I 3.2. Phase II 3.3. Phase III 4. Conclusion, Limitations, and Future Research References Chapter 10: Development of an Anomaly Detection Model for an Insurance Company’s Wire Transfer System* 1. Introduction 2. Objectives 3. Methodology and Results 3.1. Overview 3.2. Phase I (September 2008) 3.3. Phase II 3.4. Phase III 3.5. Phase IV 4. Conclusion, Limitations, and Future Research References Part V: Audit Analytics for Lawsuit Risk Detection Chapter 11: A Legal Risk Prediction Model for Credit Cards 1. Introduction 2. Literature Review 2.1. Operational and Legal Risk 2.2. Predictive Analysis 3. Methodology 3.1. Data Description 3.2. Methods 3.3. Performance Measure 4. Result and Discussion 4.1. Preliminary Prediction Model 4.2. Final Prediction Model 4.3. Discussion 5. Conclusions References Part VI: Audit Analytics in the Payment Process Chapter 12: Analyzing Payment Data and Its Process: A Bank Case 1. Introduction 2. Literature Review 2.1. Fuzzy Logic 2.2. Detecting Anomalies in Accounting Data 3. Methodology and Results 3.1. Fuzzy Logic 3.2. Application of Fuzzy Logic 3.3. Case I: Random Assignment 3.4. Case II: Effectiveness of the Model 3.5. Detecting Anomalies in Accounting Data 3.6. Authorization Limit Problem 4. Conclusion References Appendix: Questionnaire Case 1: Accidental Error Case 2: Deliberate Act