09-11-2012, 11:21 AM
Data Mining.
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Abstract:
Developments in technology have made new proactive fraud detection techniques possible. One approach using technology that appears to be effective in detecting fraud against organizations is a combination of deductive reasoning and technology---a method we call strategic fraud detection. This paper presents a model formalizing and describing the strategic fraud detection approach and shows how the use of information systems and technology provide effective ways to detect fraud. The model includes the following six stages: (1) understanding the business, (2) identifying all possible frauds that could occur, (3) cataloging possible symptoms for each type of fraud, (4) using technology to gather data about symptoms, (5) analyzing and refining results, and (6) investigating identified symptoms. Two optional steps of (7) following up on suspected frauds and (8) automating fraud detection procedures are also discussed. Two case studies---one of known fraud and one of unknown fraud---are used to build and test the approach. The case studies provide evidence that, while needing additional testing, the strategic fraud detection method described in this paper is effective in the early detection of fraud.
Introduction
Fraud detection is becoming increasingly important to managers of organizations, to internal and external auditors, and to regulators. Recent events, such as revelations of fraud-related problems at HealthSouth, Enron, and WorldCom, and the Sarbanes-Oxley Act stress the importance of early detection of fraud. Financial statement frauds have weakened investor confidence in corporate financial statements, led to a decrease in market capitalization, and have contributed to four of the 10 largest bankruptcies in history .
Because a $1 fraud against an organization reduces net income by $1 and because organizations usually have profit margins (net income / revenues) of 10 to 20 percent, additional revenue of 5 to 10 times the amount of the fraud must usually be generated to restore net income to its pre-fraud level. For example, a major automobile manufacturing company had a $436 million fraud a few years ago. At the time, the company's profit margin was just under 10 percent, meaning that additional revenues of approximately $4.36 billion had to be generated to bring net income to what it would have been without the fraud. Assuming automobiles sell for an average price of $20,000 each, the company had to make and sell 218,000 additional automobiles to restore net income to its pre-fraud amount. If this fraud had been proactively detected earlier, the fraud loss would have been much smaller and the effect on the firm much less severe. It is because frauds are so costly that statement on Auditing Standards No. 99 (AICPA 2002)—recently issued by the American Institute of Certified Fraud Examiners—requires auditors to assess the risk of material misstatement in financial statements due to fraud.
Case of Known Fraud
Several years ago, a senior vice president of a bank embezzled nearly $14 million over a 16-year period . When the fraud was discovered through a customer complaint, the bank sued its external auditors for negligence in not detecting the fraud. The fraud had been committed by manipulating, looting and abusing customer accounts and maintaining several slush accounts with sufficient funds to handle problems when customers complained.
To determine whose responsibility it was to detect the fraud, a strategic approach was used. For this fraud, the various kinds of symptoms that could have been present were identified and catalogued. Once the possible symptoms were known, 16 years of bank records (from microfiche and corporate databases) were combined into a searchable database. Using the searchable database, queries for possible symptoms previously identified were made. The actual symptoms that were found are listed in Appendix A.
All of the listed symptoms pointed to one member of management---the guilty senior vice president---as the perpetrator . With this case, it was already known that fraud had been committed. The search of bank records for fraud symptoms was for the purpose of determining who should have detected the fraud. Using technology to find symptoms revealed evidence so strong that there remained little doubt that a fraud had been committed, who the perpetrator was and who should have detected the fraud. This case indicates that appropriate use of technology can provide help in analyzing fraud that has already occurred. The important
question, however, is whether a similar, technology-based, statistical approach can be used to detect fraud against organizations that hasn't yet been discovered and where no knowledge or suspicion (predication) that fraud is being committed exists.
Literature Review and Purpose of Paper
A search of the academic literature found few studies describing the use of technology to proactively detect fraud or the articulation of a model describing proactive, technology-based fraud detection. In 2000, Nieschwietz, et al., published a comprehensive literature review of empirical fraud-related audit research. This paper cites 35 empirical studies on fraud detection in the audit, accounting,
Fraud-Fighting Activities
Fraud-fighting activities can be grouped into three primary categories: prevention, detection, and investigation.
Fraud prevention includes such activities as designing corporate fraud policies, creating internal audit departments, implementing internal controls, whistle-blower systems, and publicizing fraud occurrences. Investigation involves steps taken to answer the questions of who, how, when, and why once fraud is suspected or “fraud predication” is present. Fraud detection---the subject of this paper---includes both proactive and reactive activities targeted at finding the first indication that fraud might be occurring or undertaken to develop a “predication of fraud”. Most traditional fraud detection methods are reactive in nature---that is, they are initiated by tips or complaints, control overrides, or other indicators that someone observes or hears.
Proactive fraud detection involves aggressively targeting specific types of fraud and searching for their indicators, symptoms, or red flags. Early fraud detection is critical because the sizes of most frauds increase geometrically over time as perpetuators gain confidence that their schemes are not being detected.
The Strategic Method of Fraud Detection
Traditional fraud detection typically begins with an indication or anomaly that something isn't right, such as anonymous tips, unusual financial statement relationships, or control overrides. These indicators, often called red flags, provide predication that fraud may exist. Management, auditors or fraud examiners investigate these indicators with additional research, computer queries, or interviews to determine whether red flags represent real fraud or are being caused by other factors. This approach can be viewed as an inductive method: it begins with anomalies brought to someone's attention and continues by researching additional events and data until it is determined that fraud may be causing the indicators. It is followed by investigations to determine what the actual nature of the anomalies are.
As was illustrated in the known bank fraud example at the beginning of the paper, and as was validated using the fraud detection case described briefly at the end of this paper, current technology and widespread use of electronic databases to record transactions have made it possible to reverse traditional methods--starting with specific fraud types and moving forward to determine whether indicators or red flags of those specific frauds exist. It is now possible to specifically target different types of frauds, analyze entire populations, and zero in on fraud before traditional indicators become egregious enough to be observed. This method is called the strategic method of fraud detection. This method is a proactive approach that targets industry- and company-specific fraud anomalies and patterns and mines data for indicators of specific fraud types. Figure 2 describes the six steps involved in the Strategic Fraud Detection Model.