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Data analytics in the audit March 18, 2011 Keith Barger, Principal, Advisory Services Forensic Technology Service

Overview. Speaker backgroundIntroductionWhat is fraud?Data analytics: DefinedData analytics: Practical useCase studiesWrap up / Q

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Data analytics in the audit March 18, 2011 Keith Barger, Principal, Advisory Services Forensic Technology Service

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    1. Data analytics in the audit March 18, 2011 Keith Barger, Principal, Advisory Services & Forensic Technology Services Practice Leader Keith.Barger@us.gt.com

    2. Overview Speaker background Introduction What is fraud? Data analytics: Defined Data analytics: Practical use Case studies Wrap up / Q & A

    3. Keith Barger ATF – 18+ years of special agent Technical operation Big 4 – Director Forensic Technology and e-Discovery Grant Thornton – Principal, Practice Leader Forensic & Litigation Services Forensic Technology Services

    4. Introduction Fraud examiners and internal/external auditors utilize data analytics to aid in revealing potential concerns, enabling the detection of fraudulent circumstances as early as possible

    5. What is fraud? A general concept that refers generally to any intentional act committed to secure an unfair or unlawful gain. Financial fraud typically falls into the following categories: Fraudulent financial transactions and reporting Misappropriation of assets Revenue of assets gained by fraudulent or illegal acts Expenditures or liabilities avoided for inappropriate purpose Improperly obtained assets and costs / expenses avoided Other misconduct (e.g., conflicts of interest, insider trading, theft of trade secrets, etc.)

    6. What is fraud? (continued) Public reports related to fraud occurrences Association of Certified Fraud Examiners 2008 Report to the Nation Occupational fraud schemes tend to be extremely costly The median loss caused by occupational frauds $175,000 More than 25% of the fraud involved losses of more than $1M Critical Perspectives on Accounting, 2010 90% of the frauds occur at the senior executive level PCAOB proposed Auditing Standard indicates Controls related to the preventions, identification, and detection of fraud often have a pervasive effect on the risk of fraud

    7. What is fraud? (continued) Goals of fraud risk management Understand fraud and misconduct risks that can undermine their business objectives Reduce exposure to corporate liability, sanctions, and litigation Achieve the highest levels of business integrity through sound corporate governance and intelligence, and internal policies and controls

    8. Data analytics: Defined Data analytics is the science of examining raw data with the purpose of drawing conclusions about that information

    9. Data analytics: Defined (continued) A data analytic aided program Information technology and use of computer based audit techniques such as data analytics can significantly improve the effectiveness of a corporate fraud risk management program and corporation investigations The data analytics program can be generally outlined as: Consideration of potential fraud schemes and scenarios Assessment at various levels: globally (corporate-wide), significant business units, substantial account levels Testing of the effectiveness of the internal policies and controls On-going monitoring and evaluations on a periodic and random frequency to access performance and effectiveness

    10. Data analytics: Defined (continued) Key benefits of data analytics Rapidly evaluate large amounts of data which could mitigate fraud risks and/or detect fraud Capable of analyzing large data set and oftentimes, 100% of the relevant data Abilities to apply similar analysis routines to various data sets without excess development time

    11. Data analytics: Defined (continued) How good is your data? Data quality is essential to interoperability and should be evaluated based on: How do you verify the completeness or data? Accuracy Consistency on data formats, naming conventions and precision Do data sources triangulate? Exportability and portability How easy can the data be exported? Audit trail How much effort is required to uncover the change in data values and accountability of the changes?

    12. Data analytics: Defined (continued) Data integrity Data normalization and standardization is often required before computerize tools start analyzing corporate financial and transactional data

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