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This book is about identifying fraud with the aid of data analytic techniques. It includes some data analytical tests that you probably have not considered. It may also expose you to some useful features of IDEA and how to apply procedures to make you a more effective IDEA user. This is a list of the chapters in Fraud and Fraud Detection:

- Chapter 1, "Introduction." This chapter provides a simple definition of fraud to distinguish it from abuse. Different types of frauds are outlined. The chapter includes a discussion of why a certain amount of fraud risk is acceptable to organizations and how risk assessments enable us to evaluate and focus on areas with a higher potential risk of fraud.

- Chapter 2, "Fraud Detection." Occupational fraud is hard to detect as employees know their systems inside out. There are both fraudulent inclusions and fraudulent exclusions to be evaluated. This chapter discusses recognizing the red flags of fraud and different types of anomalies. Accounting and analytic anomalies are distinguished, as well as whether procedures are considered to be data mining or data analytics.

- Chapter 3, "The Data Analysis Cycle." The data analysis cycle steps include evaluation, technology, and auditing the results from the analysis. Before you can do any analysis, you must have good data. This chapter dei nes the steps in obtaining the data, such as necessary files, fields, file formats, the download, and techniques to verify data integrity. The next steps of preparing for data analysis include examples of data-familiarization techniques along with arranging and organizing the data.

- Chapter 4, "Statistics and Sampling." Knowledge of statistics can help to better understand and evaluate anomalies, of which some are deviations from the normal. Results of sampling methods can also be more effectively analyzed and interpreted. This chapter explains basic statistics that can easily be understood by auditors, accountants, and investigators. The chapter includes both descriptive and inferential statistics, the measure of center points, and variability from the center. Standard deviation and its usefulness in comparing data is discussed. Both statistical and nonstatistical sampling methods that include attribute, monetary unit, classical variable, discovery, and random sampling are demonstrated along with explanations of why sampling is not enough.

- Chapter 5, "Data Analytical Tests." Data analytical tests allow for the detection of anomalies over entire data sets. The tests can evaluate 100 percent of the transactions or records to reduce potentially millions of records to a reasonable amount of high-risk transactions for review. General data analytical tests can be applied in most situations and are ideal as a starting point for an audit when there is no specific fraud or target identified. Benford's Law for detecting abnormal duplications in data is explained for the primary, advanced, and associated tests. Examples of these are demonstrated using IDEA's Benford's Law built-in tests to provide understanding, application, and evaluations of the results. Z-score, relative size factor, number duplication, same-same-same, same-same-different, and even amounts tests are shown with step-by-step instructions of how they can be manually applied using IDEA.

- Chapter 6, "Advanced Data Analytical Tests." Correlation, trend analysis, and time-series analysis are explained in this chapter and demonstrated using IDEA's built-in features. Relationship tests known as GEL-1 and GEL-2 are shown with step-by-step instructions of how they can be manually applied using IDEA. The reader will be able to analyze and evaluate the results from these advanced tests.

- Chapter 7, "Skimming and Cash Larceny." In this chapter, we will look at the differences and similarities of skimming and cash larceny. This type of fraud has fewer data analytical tests that can be performed as the fraud is not recorded in the business system's databases or is well concealed. Less attention is paid to this area, as the losses are generally not as significant as in other types of fraud. Methods of skimming and larceny are discussed to provide an understanding of these types of frauds. A short case study using two years of sample sales data highlights possible data analytical tests that use charts and pivot tables to provide views for different analytical perspectives.

- Chapter 8, "Billing Schemes." Billing schemes occur through accounts payable and are centered on trade and expenses payable. The objective for reviewing accounts payable does not stop at looking for fraud by corrupt employees, but it is also useful for detecting errors and ineficiencies in the system. Most of the case study involves real-life payment data of over 2 million records. Data analytical tests from Chapter 5 are applied in practice and demonstrated using the IDEA software. Other specific tests include isolating payments without purchase orders, comparing invoice dates to payment dates, searching the database for post office box numbers, matching employees' addresses to the suppliers' addresses, duplicate addresses in the supplier master file, payments made to suppliers not in the master file, and the usage of gap detection on check numbers.

- Chapter 9, "Check Tampering Schemes." This chapter includes discussions of electronic payment-systems fraud along with traditional check-tampering schemes to better understand both new and old schemes. Methods of obtaining checks and authorized signatures, along with concealment methods, are outlined. Data analytical tests and a short case study that uses sample data are used as examples.

- Chapter 10, "Payroll Fraud." Some form of payroll fraud happens in almost every organization; whether they are the simple claiming of excess overtime and hours worked or taking complex steps to set up nonexistent employees on the payroll. This chapter outlines data analytical tests to identify anomalies that can be evaluated to determine payroll fraud. The case study uses real payroll payment data to illustrate step-by-step payroll analytical tests. Tests for the payroll register, payroll master, and commission files are discussed.

- Chapter 11, "Expense Reimbursement Schemes." Travel and entertainment expenses are open to abuse by employees, as improperly reimbursed amounts puts extra money in their pockets. This chapter uses actual travel expense data to highlight analytical tests to identify anomalies that require additional analysis and review. The case study includes methodologies to make the inconsistent data usable in a field that contains the travel destinations. Tests for purchase cards are also discussed.

- Chapter 12, "Register Disbursement Schemes." This chapter distinguishes between register disbursement schemes, skimming, and cash larceny. It provides an understanding of false returns, adjustments, and voids used to perpetrate this type of fraud. Concealment methods are discussed. The case study uses data analytics to detect anomalies in voids and coupon redemptions. Tests, such as identifying transactions where the same person both registers the sale and authorizes the void, and matching all sales with all voids are illustrated.

- Chapter 13, "Noncash Misappropriations." Noncash misappropriations frequently involve assets such as inventory, materials, and supplies. This type of fraud is easy to commit, difficult to conceal, and requires conversion of the assets to cash. Noncash schemes, such as misuse, unconcealed misappropriations, and transfer of assets are discussed. Concealment strategies by falsifying inventory records, sales, and purchases records are noted. The types of data file and data analytical tests to detect potential are suggested.

- Chapter 14, "Corruption." Corruption is insidious as it involves an employee of the organization. Corruption schemes are difficult to detect as the gain to the employee is paid by an outside party and not recorded in the organization's records. Corruption schemes include bribery, tender or bid rigging, kickbacks, illegal gratuities, extortion, and conflict of interest. Data analytics can expose transactions that may be associated with corruptions schemes. One of the most powerful tests that can be applied to data sets is a relationship-linking test, as demonstrated in this chapter.

- Chapter 15, "Money Laundering." This chapter introduces the reader to money-laundering schemes and how to detect them. Anti-money-laundering organizations are listed to provide resource information on this subject matter. Techniques used to convert illicit funds to what appear to be legitimate funds in the placement, layering, and integration stages are discussed. Nontraditional banking systems and new payment methods are included in this introduction. Data analytical tests to detect money laundering are outlined.

- Chapter 16, "Zapper Fraud." Zappers, or electronic suppression-of-sale devices, are being used to delete sales transactions from point-of-sales systems. This is a worldwide problem that is of great concern to taxation authorities and commercial landlords that base rental revenue on a percentage of sales. This chapter provides some background information on POS systems and zappers. Step-by-step instructions are shown on how to prepare POS files for analysis and on using various analytic techniques to analyze, detect, evaluate, and quantify deleted sales in IDEA.

- Chapter 17, "Automation and IDEAScript." This chapter introduces the automation tools of IDEAScript, Visual Script, and custom functions. Automating procedures, especially complex ones, can save much time. Considerations for automation are discussed. The benefits of IDEAScript are compared with those of Visual Script. Creating Visual Script and IDEAScript from the recording of procedures manually done in IDEA are shown. In addition, a look into how IDEAScript can create a script from the history log file is included in this chapter. IDEAScript resources are outlined, including useful completed scripts that are available from the companion website.

- Chapter 18, "Conclusion." This chapter explains why using data analytics to initially detect financial-statement fraud is not appropriate. Features, equations, and functions demonstrated throughout the book are summarized here. The project overview feature within IDEA is briely discussed, showing how it brings together all the databases used in the project and how they relate to one another. There are some final words regarding data analytic challenges and future needs.


WOULD LIKE TO THANK Athena Mailloux, who developed my interest in occupational fraud and fraud detection. Without her insistence that I join her in teaching a financial-fraud program that she developed for a local college, I would not have found the enthusiasm for fraud detection.

Alain Soubliere and all the wonderful people at CaseWare IDEA Inc. deserve much credit for their assistance, support, and professionalism. CaseWare IDEA allowed me to combine fraud detection with data analytics.

A special thanks to Brian Element, an IDEAScript master. Without Brian, the automated IDEAScripts offered in the companion website would not exist. I met Brian, a chartered professional accountant, at an advanced IDEA course that he presented in 2011. We started chatting and it came out the Brian wanted to learn IDEAScripting. Brian said that he would have to come up with a scenario for his self-learning IDEAScript project. At the time, I had given much thought to the detection of anomalies through the use of data analytics, so I said to him, "Have I got a deal for you!" Since that time we have been working together on ideas for scripts for fraud detection that Brian programs. From self-learning, Brian has become the world's leading expert in IDEAScripts and he has been conferred the certified IDEAScript expert (CISE) designation by CaseWare IDEA. At the time of writing, Brian is the sole holder of this designation.

Most important, I would like to thank my family, friends, and associates for all their support and encouragement to be able to complete this book.

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