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Forensic Valuation: Faster, Better, Higher Return© The Combat CPA© Series… Darrell D. Dorrell, CPA/ABV, MBA, ASA, CVA, CMA, DABFA financialforensics® www.financialforensics.com.

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Presentation on theme: "Forensic Valuation: Faster, Better, Higher Return© The Combat CPA© Series… Darrell D. Dorrell, CPA/ABV, MBA, ASA, CVA, CMA, DABFA financialforensics® www.financialforensics.com."— Presentation transcript:

1 Forensic Valuation: Faster, Better, Higher Return© The Combat CPA© Series… Darrell D. Dorrell, CPA/ABV, MBA, ASA, CVA, CMA, DABFA financialforensics® Foundation for Accounting Education – FAE 2009 Business Valuation Conference New York, New York – May 21, 2007

2 Today – Session Description… 3 categories:3 categories: 1.Accelerate the valuation process 2.Applied forensic techniques, e.g. guidelines 3.Court cases re forensic accounting reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

3 Our offices…

4 Who worked on this material?

5 financialforensics ® Our mission statement: To spearhead forensic accounting innovation in civil, criminal and combat matters. © (financialforensics® October 2002) The definition of forensic accounting is: The art & science of investigating people & money. © (financialforensics® Newsletter, September 1993) 5

6 Some of our recent work using these techniques… US Department of Justice (USDOJ) USA Bulletin: counter-terrorism – USA Bulletin: Forensic accounting - counter-terrorismBulletincounter-terrorismBulletincounter-terrorism Federal Bureau of Investigation (FBI) –Forensic accounting - money laundering/white-collar crime Oregon Department of Justice (ORDOJ) HMC/Znetix, Inc. for SEC/Receiver - $106 million –2 nd largest Washington securities fraud –More than 12 executives; serving 910 months in aggregate –Forensic Accountants Report: $400 mm for-profit: alter-ego/fraudulent conveyance - $20 million $100 mm for-profit: purchasing agent embezzlement million$14 mm for-profit: controller embezzlement - $5.5 million $10 mm city: Finance Director embezzlement - $1.4 million $7 mm for-profit: CFO embezzlement $2 mm non-profit: controller embezzlement - $140,000 $1 mm for-profit: office manager embezzlement Dental practice: wife embezzlement - $70,000 Auto repair shop: owner embezzlement - $1,500 Law & Order TV series – technical advice Large Police Bureau – Financial Crimes

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8 Selected Valuation/Forensic Accounting Cases: alter ego: Forest products, services businesses, merger & acquisition Anti-trust: Forest products Bankruptcy dismissal (Chapter 13): Health care Beach of contract: Manufacturing, distribution, health care, banking, transportation Copyright: Art work, publications ESOPs, 401(k) share redemption: Dealerships, manufacturing Estate 706: manufacturing, distribution, construction, raw land, electronics, apartments Forensic accounting: Civil and criminal; private sector, public sector Fraud: Civil and criminal; private sector, public sector Fraudulent conveyance/transfer: Health clubs, finish carpentry Gifting 709: Securities, real property (residential/commercial) Information technology: Software, hardware Internet companies: B2B, B2C ISOs/ESOs: Black-Scholes, lattice/binomial Lending/Financing: Banks, borrowers Lost profits: Construction, manufacturing, distribution Marital Dissolution: Equitable distribution, community property Merger/Acquisition: Pre-transaction and post-transaction analysis Patent: Reasonable royalty, et al Public Sector: Jail Costing/Pricing Study, Public Funds Mismanagement Solvency/Insolvency: Wholesale distribution, agricultural Trademark; Trade Dress: Specialized tools Trade Secrets: Cell phone software 8

9 Forensic accounting defined… The art & science of investigating people & money.© –Forensic Accounting Academy© - April 2007 –(financialforensics® Newsletter, September 1993) 9

10 AICPA Latest Definition generally involving the application of specialized knowledge and investigative skills possessed by CPAs to collect, analyze, and evaluate evidential matter and to interpret and communicate findings in the courtroom, boardroom, or other legal or administrative venues. »CPA Expert, Winter 2009, page 2

11 Foundational Discipline Forensic Accounting – Foundational Discipline Fraud Valuation Economic Damages Audit/Review/Comp Forensic Accounting Tax PI, WD Internal Audit Performance Auditing

12 EverythingEverything that you see/hear today is: –Public record –And/or –Disguised 12

13 13 forensic accounting How does forensic accounting affect valuation?

14 14 forensic accounting valuation, attest, litigation, tax, fraud, et al ALL How does forensic accounting affect valuation, attest, litigation, tax, fraud, et al? i.e. virtually ALL of your practice areas

15 15 et al… Forensic Accounting & Valuation, et al… forensic accounting Your clients expectations reach beyond your core expertise whether auditing, tax, valuation or litigation. Such expectations are no surprise since forensic accounting is now a household term. thinksall Thanks to extensive media coverage of several high profile corporate collapses and showcasing of forensic accounting specialists the public thinks that all CPAs have such expertise. Consequently, the public, i.e. your clients think that you deliver professional services from a foundation of forensic accounting. The accounting profession has reinforced such perceptions despite failing to provide guidance. Specifically, virtually every accounting periodical devotes space to forensic accounting and related subjects. Further, all CPE providers offer various courses on the subject or some derivative. Finally, accounting graduates are increasingly attracted to firms offering such services. forensic accounting methodology comprising the forensic tools Paradoxically, the accounting profession has yet to embrace (or even offer) a cogent, comprehensive, forensic accounting methodology comprising the forensic tools by which accountants can guide and refine their forensic accounting craft. Likewise, defending ones core expertise continues to be more challenging without independently codified benchmarks.

16 forensic accounting How does forensic accounting affect valuation? Does valuation require: –Veracityreliability –Veracity or reliability of the financial statements –Financial analysis, e.g. ratios, trending? –Designed by lenders; lack cash & forensic tests; unfamiliar to valuators –Benchmarking against subject, peers? & –Normalizations; (do you analyze before & after?) journalentries –Do you normalize with journal entries? feasible –Earnings projections/estimations - feasible? cashgenerated –Tested against cash generated? reliability –Economic benefit stream reliability? –Discount/capitalization rate development? –Management assessment/performance? –Facilities and operations walkthrough? comparison/selection –Guideline company comparison/selection? –Market transactions statistical fit –Secondary –Secondary adjustments, e.g. DLOM, DLOC, Key Customer? –Etc.?

17 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

18 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

19 Example Court Cases – Forensic Accounting In re Coram Healthcare Corp., 2004 Bankr. LEXIS 1516 (Oct. 3, 2004) –Valuation issue was Debtors value which would determine whether Trustees plan was fair and equitable. –Both parties experts used same methodologies, guideline public company analysis, guideline transaction analysis, and DCF – but conclusions disparate –Court noted: Big 4 firm [for equity committee] and investment banker team [for trustee] included different assets in reaching their valuation conclusions, attached different weights to the three valuation methodologies, and took different positions regarding managements projections. –Equity Committee argued: investment banking team deflated Debtors value by relying on conservative projections because of flawed assumption and errors investment banking team did not conduct independent review of projections consistency or actual performance –Big 4 valuation used upside projections including EBITDA adjustments deemed to be irregular, including growth, cash flow and managements established reserves; total adjustments increased EBITDA by 40% –Court: Although valuations are subjective, (sic) there are proper and improper methods of performing a valuation. [Big 4 firm] took aggressive and optimistic views regarding the valuation and strength of the Debtors. Therefore, we do not find that the [Big 4 firm] valuation is an accurate reflection of the Debtors value. 19

20 Example Court Cases – Forensic Accounting Susan Fixel, Inc. v. Rosenthal & Rosenthal, Inc., 2005 Fla. App. LEXIS 1101 (Feb. 1, 2006) –Claims for breach of fiduciary duty and negligent misrepresentation – total loss –Expert calculated damages with client-prepared revenue and cash flow projections –Court: [expert] never verified those projections nor prepared his own. –Court: …too speculative… –Court: expert made critical error in date of valuation; 1 year prior to damages date –Court: excluded his testimony at trial –Company argued while lost profits may require more evidence, a market valuation could rely on forecasts –Court: disagreed – It is as inappropriate to use purely speculative forecasts of future revenue to determine the market value of a business as it is to use such speculative forecasts in determining future lost profits. –Appeals Court: Affirmed expert exclusion as unreliable when expert used improper date and relied on speculative income projections 20

21 Example Court Cases – Forensic Accounting In re Nellson Nutraceutical, 2007 Bankr. LEXIS 99 (January 18, 2007) –Chapter 11 case; private manufacturer of nutrition bars and supplements –Expert learned after-the-fact that managements long-range financial plans did not represent their best and most honest thinking. –Principal equity holders needed a value exceeding $365,000,000 –Principal equity holders screened valuation analysts to pre-determine their methodologies –Investors & attorneys sent selected expert values to be attributed to growth plans –Privately and via discussed how to figure out a way expert could assist investors –Investors pushed through a puffed up business plan for debtors –Plan inflated revenues/EBITDA projections, ignored price compressions… –… ignored increased market competition, eliminated millions of CapEx –Court: In sum, [the investors] utilized [their] control over [the debtors] to manipulate both the business planning and valuation process to come up with an artificially inflated enterprise value… to claim some residual value for their existing equity position. There is no other credible interpretation of the evidence before the Court. –Court specifically exonerated three experts –All three testified that there results would require reduction to reflect flawed projections One unable to recite the Gordon Growth model on cross examination –Debtors expert: Expert sent draft DCF analysis that did not reach desired equity During telecon Board convinced appraiser to use CapEx methodology One week later, appraiser sent Board new report using CapEx methodology –Court reached its own conclusion after adjusting experts results per manipulation 21

22 Example Court Cases – Forensic Accounting Aukeman v. Aukeman, 2007 Mich. App. LEXIS 1524 (June 12, 2007) –Husband/owner testified weekly business sales averaged $58,000 with slight growth –His expert used the numbers to value the three grocery stores at $1.53 million –Wifes expert used sales projections husband/owner used to obtain financing –Her expert used weekly sales of $122,000 to value stores at $3 million+ –Court: arrived at $2,225,000 22

23 Example Court Cases – Forensic Accounting Imaging International v. Hell Graphic Systems, Inc., 2007 N.Y. Misc. LEXIS 7368 (October 29, 2007) –Business owner supplied most/all of financial information – previously convicted of tax fraud –Expert initially calculates damages at $11 million, but revises to $4 million –Printer purchased printing equipment that never worked properly, but kept using it –3 years later filed bankruptcy –Jury awarded liability to printer who had sued for fraud –Printer used expert for damages: Prepared two reports, i.e and 2005 – widely disparate results First used ex post approach, projecting revenue for 15 years from 1990, 6% annual growth: $11 million Second used ex ante with DCF and 12% growth rate + 4% premium: $4 million Expert assumed Hell Graphics fraud was sole cause of business failure For both reports, expert relied on information provided by the owner, without any independent review or access to underlying financial documentation –Rebuttal expert found: Printer lost two major customers for reasons unrelated to allegation Owner refused to make staffing changes recommended by turnaround firm Owner found guilty of tax fraud two months before bankruptcy Industry shifted from analog to digital technology during relevant period Printers expert relied entirely on unverified data provided by owner who destroyed documents prior to trial –Court found: Owner lacked credibility, therefore experts damage analysis lacked credibility Expert failed to explain variance in growth rates and failed to account for other possible causes of decline Failed to provide a preponderance of evidence 23

24 Example Court Cases – Forensic Accounting Mood v. Kronos Products, Inc., 2007 Tex. App. LEXIS 9243 (November 27, 2007) –After 11 years exclusive agreement, both parties breached Kronos terminated without adhering to 60-day notice Mood counterclaimed for unauthorized sales to a Mood customer & breach damages –At trial Mood presented no direct expert testimony on first claim, using instead: Kronos invoices for about 2 years Moods average gross annual sales to customer Kronos began selling to A general assertion that Moods usual profit margin was 20% Expert witness assertion that about 80% of Moods sales were from Kronos products –Mood presented expert to calculate breach damages Attempted to predict lost profits over 10 years, i.e Discrete revenue forecast per recent years net income and applied 17% discount rate –Jury awarded $1.1 million; judge vacated for a take nothing verdict –Mood appealed; Kronos defended and Appeals Court agreed: Kronos gross sales to one customer insufficient evidence of lost profits; did not show same product/volume/price Gross sales estimate was based on hypothetical statement by counsel at trial, i.e. no evidence of actual sales Moods claim of 20% gross margin (and 80% of overall sales from one product) lacked sufficient factual basis Moods expert relied on history for 10 year projection & did not differentiate between direct and consequential Expert failed to address loss of goodwill and/or going concern value resulting from the breach –Court commented: Put another way, [the experts] analysis did not specifically address the economic impact of the summary termination of the distributorship agreement. 24

25 Example Court Cases – Forensic Accounting Derby v. Commr, 2008 WL (U.S. Tax Ct.) (Feb. 28, 2008) –Northern California physicians sold their practice to a non-profit medical foundation in 1994 –To maintain professional autonomy physicians refused to sign non-compete –Foundation did not want to pay for goodwill, and wanted to avoid kick-back laws –Attorney suggested donating intangible value to foundation as charitable donation –National valuation firm derived intangible result and certification of appraisal to each –Allocation formula derived by one physician used for each personal tax return –After IRS audit 3 rd appraiser enlisted who used physicians allocation formula –Appraiser adopted physicians allocation formula –Tax Court cited various appraiser deficiencies: Failed to distinguish between personal and professional goodwill Failed to account for non-compete Adopted physician formula without any independent analysis Ignored physician access bonus 25

26 Example Court Cases – Forensic Accounting Structural Polymer Group, Ltd. V. Zoltek Corp., 2008 WL (8 th Cir.) (Oct. 8, 2008) –Plaintiff contracted with defendant for all their carbon fiber needs for 10 years, subject to certain limitations –Alleged breach in years 5 & 6 –Plaintiff sought damages years 5 & 6 and remaining 4 years –Plaintiffs expert relied only on Discussions with management Management summaries Internal budgets Projected sales compared to actual purchases Defendants annual report Defendants depositions & CEO statement But for case subtracting variable costs & then-market price Applied to plaintiffs 18-month gross profit margin –Jurys findings for plaintiff upheld in appeal 26

27 Example Court Cases – Forensic Accounting Fluor Enterprises, Inc. v. Conex International Corp WL (Tex. App.) (Dec. 18, 2008) –Petrochemical company hired mechanical contractor & Fluor Enterprises, Inc. –At completion company failed to pay about $2 million to contractor –Contractor sued Fluor for business disparagement and interference Jury awarded contractor $98 million; Fluor appealed –Contractor used economics professor for damages Focused only on lost profits, not causation –Professor apparently did not consider actual contracts company awarded contractor 5-year post assignment period –Professors other problems included: Downturn in refinery business for the relevant period Not familiar with normal profit margin for the industry Did not analyze job cost analysis Did not include overhead Incorporated a contract that expired 3 years prior to initial project Failed to apply his methodology consistently and with objectivity Averaged profit margins but not expenses and costs –Court commented: …based his opinion of lost future profits on past performance only when it benefitted [the contractor. In other words, [the expert] provides no evidence of specific lost sales.., Thus, he did not supply one complete calculation, but provided computations based on different methods of calculation. 27

28 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

29 Cookies on the bottom shelf… 29

30 Covert Forensic Tools Deposition Matrix© Valuation Report Card© 30

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36 Statistics hacks 36

37 37

38 Deposition Matrix ©

39 Valuation Report Card©

40 40 Gap Detection (cont)

41 How/Where do you start/stay?

42 Pictures… 42

43

44 Pictures… 44

45 Pictures… 45

46 Is thiscashlockbox secure? Is this cash lockbox secure?

47 Finding the bodies…

48 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

49 You May Want To Receive… Sarbanes-Oxley (SOX) Compliance Journal –Financial Hieroglyphics – The Numbers Speak to Me American Journal of Family Law –Is The Moneyed Spouse Lying About The Money?© Value Examiner –2008 Update: Marketability Discounts - A Comprehensive Analysis National Litigation Consultants Review –Valuation Forensics 49

50 25+ New forensic accounting/valuation techniques… Full-and-False Inclusion Genogram TATA/TARTA/TITA/TDTA/TAPTA AQI Behavior Detection FACS Styleometry ICE©/SCORE© Link Analysis Articulated Cash Flow Dechow-Dichev Techniques Timeline Analysis IRS Formal Indirect Methods A(5) Cash-T (Modified) Net Worth Bank Deposits & Cash Expenditures Markup Unit & Volume Expectations Attributes Gap Detection Proof-of-Cash Deposition Matrix Entity(s) Chart Lev-Thiagarajan Techniques Damages Report Card Digital Analysis CATA/CRO MSSP 50

51 The techniques require a methodology 51

52 Methodology What Is A Methodology? Basic Example – for the Cops A way of doing things, a process… Criminal Investigation –7-Step Method –Others Forensic Accounting Investigation Combined Criminal/Forensic Accounting 52

53 ICE © ICE © C C - Control E E - External I - Internal 53

54 ICE © Closely-Held Business Example C C – Control Bank Statements E E – External Tax Returns Attest Reports I – Internal PBC Financials Operating Reports 54

55 ICE © ICE © C C – Control Bank Statements E E – External Tax Returns Attest Reports I – Internal PBC Financials Operating Reports Proof-of-Cash Timing Non-Cash 55

56 Why IsntICE © Sufficient? Why Isnt ICE © Sufficient? You must be: ThinkingOutside the… Triangle© –Thinking Outside the… Triangle© That is where SCORE © comes inThat is where SCORE © comes in 56

57 SCORE © SCORE © Flow of $ and/or Units StakeholderInOut S S – SuppliersU$ C C – Customers$U O O – Owners Investors/Lenders $$ R R – Regulators n/a $ E E – EmployeesU$ 57

58 Methodology What Is A Methodology? Forensic Accounting Example A way of doing things, a process… Criminal Investigation –7-Step Method –Others Forensic Accounting Investigation Combined Criminal/Forensic Accounting 58

59 What Can You Expect This Week (Today)? Uncertainty… Vocabulary – a common language…Vocabulary – a common language… VERY hard work… possible growth…VERY hard work… possible growth… Leave your ego at the door…Leave your ego at the door… 59

60 Assignment Development INTERPERSONALDATA COLLECTION AND ANALYSIS TRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Identify parties to the case u Correlate the matters of law u Confirm technical capabilities u AICPA/BVFLS Practice Aids u Litigation Services Handbook, 4th & Cumulative Supplements u NACVA Resources u Clear conflict u Insure matching of expectations between counsel and facts and circumstances of matter u Determine whether engaged as consultant or expert u Prepare and secure engagement letter u Establish concrete timelines, e.g. discovery cutoff, report submittal, etc. u Establish counsel communications protocol, e.g. whether/how subject to discovery u Identification of all parties u Specification of key timelines u Privilege determination u Agreement on standards u Entity / Party Chart u Signed engagement letter u Retainer Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL

61 Contributing Authors and/or Instructors Thomas F. Burrage CPA/ABV, CVA, DABFA Burrage & Johnson, CPAs, LLC – Albuquerque, NM Darrell D. Dorrell, CPA/ABV, MBA, CVA, ASA, CMA, DABFA financialforensics® – Lake Oswego, OR Gregory A. Gadawski, CPA/ABV, CVA, CFE financialforensics® – Lake Oswego, OR Katherine Heekin – JD, CFE The Heekin Law Firm – Portland, OR Dr. Diane A. Matthews, CPA, CFE Carlow University – Pittsburgh, PA Patricia A. Perzel, CPA, CVA, CFFA, CFD Perzel & Lara Forensic CPAs, P.A. – Clearwater, FL Gabriel H. Shurek, Manager Gettry Marcus Stern & Lehrer, CPA, P.C. – Woodbury, NY Mark S. Warshavsky, CPA/ABV, MBA, CVA, CBA, CFE, DABFA, CFFA Gettry Marcus Stern & Lehrer, CPA, P.C. – Woodbury, NY Paul E. Zikmund – MBA, MA, CFE, CFD Solomon Edwards Group, LLC – Philadelphia, PA

62 Assignment Development INTERPERSONAL DATA COLLECTION AND ANALYSIS TRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Identify parties to the case u Correlate the matters of law u Confirm technical capabilities u AICPA/BVFLS Practice Aids u Litigation Services Handbook, 4th & Cumulative Supplements u NACVA Resources u Clear conflict - firm-wide database u Insure matching of expectations between counsel and facts and circumstances of matter u Determine whether engaged as consultant or expert u Prepare and secure engagement letter & retainer u Establish concrete timelines, e.g. discovery cutoff, report submittal, etc. u Establish counsel communications protocol, e.g. whether/how subject to discovery u Identification of all parties u Specification of key timelines u Privilege determination u Agreement on standards u Conflict Resolution Form u Entity / Party Chart u Signed engagement letter & retainer u Retainer Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection InterviewsInterviews& Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical AnalysisAnalysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL

63 Assignment Development INTERPERSONAL DATA COLLECTION AND ANALYSIS TRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Identify parties to the caseparties u Correlate the matters of lawmatters of law u Confirm technical capabilitiestechnical u AICPA/BVFLS Practice Aids u Litigation Services Handbook, 4th & Cumulative Supplements u NACVA Resources u Clear conflict - firm-wide database u Insure matching of expectations between counsel and facts and circumstances of matter u Determine whether engaged as consultant or expert u Prepare and secure engagement letter & retainer u Establish concrete timelines, e.g. discovery cutoff, report submittal, etc. u Establish counsel communications protocol, e.g. whether/how subject to discovery u Identification of all parties u Specification of key timelines u Privilege determination u Agreement on standards u Conflict Resolution Form u Entity / Party Chart u Signed engagement letter & retainer u Retainer Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL

64 Assignment Development INTERPERSONAL DATA COLLECTION AND ANALYSIS TRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Identify parties to the case u Correlate the matters of law u Confirm technical capabilities u AICPA/BVFLS Practice Aids AICPA/BVFLS u Litigation Services Handbook, 4th & Cumulative SupplementsLitigation Services Handbook u NACVA ResourcesNACVA Resources u Clear conflict - firm-wide database u Insure matching of expectations between counsel and facts and circumstances of matter u Determine whether engaged as consultant or expert u Prepare and secure engagement letter & retainer u Establish concrete timelines, e.g. discovery cutoff, report submittal, etc. u Establish counsel communications protocol, e.g. whether/how subject to discovery u Identification of all parties u Specification of key timelines u Privilege determination u Agreement on standards u Conflict Resolution Form u Entity / Party Chart u Signed engagement letter & retainer u Retainer Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL

65 Assignment Development INTERPERSONAL DATA COLLECTION AND ANALYSIS TRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Identify parties to the case u Correlate the matters of law u Confirm technical capabilities u AICPA/BVFLS Practice Aids u Litigation Services Handbook, 4th & Cumulative Supplements u NACVA Resources u Clear conflict - firm-wide database u Insure matching of expectations between counsel and facts and circumstances of matter u Determine whether engaged as consultant or expert u Prepare and secure engagement letter & retainer u Establish concrete timelines, e.g. discovery cutoff, report submittal, etc. u Establish counsel communications protocol, e.g. whether/how subject to discovery u Identification of all parties u Specification of key timelines u Privilege determination u Agreement on standards u Conflict Resolution Form u Entity / Party Chart u Signed engagement letter & retainer u Retainer Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL

66 Assignment Development INTERPERSONAL DATA COLLECTION AND ANALYSIS TRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Identify parties to the case u Correlate the matters of law u Confirm technical capabilities u AICPA/BVFLS Practice Aids u Litigation Services Handbook, 4th & Cumulative Supplements u NACVA Resources u Clear conflict - firm-wide database u Insure matching of expectations between counsel and facts and circumstances of matter u Determine whether engaged as consultant or expert u Prepare and secure engagement letter & retainer u Establish concrete timelines, e.g. discovery cutoff, report submittal, etc. u Establish counsel communications protocol, e.g. whether/how subject to discovery u Identification of all partiesall u Specification of key timelinestimelines u Privilege determinationPrivilege u Agreement on standardsstandards u Conflict Resolution Form u Entity / Party Chart u Signed engagement letter & retainer u Retainer Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL

67 Assignment Development INTERPERSONAL DATA COLLECTION AND ANALYSIS TRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Identify parties to the case u Correlate the matters of law u Confirm technical capabilities u AICPA/BVFLS Practice Aids u Litigation Services Handbook, 4th & Cumulative Supplements u NACVA Resources u Clear conflict - firm-wide database u Insure matching of expectations between counsel and facts and circumstances of matter u Determine whether engaged as consultant or expert u Prepare and secure engagement letter & retainer u Establish concrete timelines, e.g. discovery cutoff, report submittal, etc. u Establish counsel communications protocol, e.g. whether/how subject to discovery u Identification of all parties u Specification of key timelines u Privilege determination u Agreement on standards u Conflict Resolution FormConflict Resolution Form u Entity / Party Chart Entity / Party u Signed engagement letter & retainerengagement letter u Retainer Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL

68 Assignment Development INTERPERSONAL DATA COLLECTION AND ANALYSIS TRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Identify parties to the case u Correlate the matters of law u Confirm technical capabilities u AICPA/BVFLS Practice Aids u Litigation Services Handbook, 4th & Cumulative Supplements u NACVA Resources u Clear conflict - firm-wide database u Insure matching of expectations between counsel and facts and circumstances of matter u Determine whether engaged as consultant or expert u Prepare and secure engagement letter & retainer u Establish concrete timelines, e.g. discovery cutoff, report submittal, etc. u Establish counsel communications protocol, e.g. whether/how subject to discovery u Identification of all parties u Specification of key timelines u Privilege determination u Agreement on standards u Entity / Party Chart u Signed engagement letter & retainer u Retainer Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL 68

69 Assignment Development ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Obtain sufficient relevant data to provide credible evidence u Bragg, Steven M., Business Ratios and Formulas (Wiley) u Benfords – u IDEA software u Summarize and analyze the findings of all deliverables and observations u Identify any missing information or gaps u DRAFT the Forensic Accountants Report u TASKS Common-sizing Horizontal analysis Vertical analysis Statement analysis (written) Accept-reject testing Stratified mean-per-unit (MPU) Attributes sampling Link Analysis/Root Tracing Item Listing u Forensic Accountants Report does not support the indictment u Additional techniques do not substantiate missing gaps u Gap Analysis u Indictment Matrix u WPN (words/pictures/numbers) Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment Forensic Accounting/Investigation Methodology (FA/IM) © INTERPERSONALDATA COLLECTION AND ANALYSISTRIAL/REPORTSFOUNDATIONAL

70 Foundational Discipline Forensic Accounting – Foundational Discipline Fraud Valuation Economic Damages Audit/Review/Comp Forensic Accounting Tax PI, WD Internal Audit Performance Auditing

71 Full-and-False-Inclusion yellow … the yellow crime scene tape of forensic accounting … 71

72 Chain of Custody Gaps in the chain or mishandling of evidence can damage a case Evidence may still be admissible if it can be authenticated by an identifying feature, but a mistake in custody affects the weight of the evidence In fraud cases, maintaining custody is particularly significant for electronic evidence (concern regarding alteration) – hand-to-hand chain of custody detailing how it was stored and protected from alteration

73 Genogram 73

74 Consolidated Operations Fr P P P P P P Red font – Family Fr – Friends since high school P – Pajama party participant W – Worked together many years WW W W W W W 74

75 Entity Chart(s)

76 TimelineAnalysis Timeline Analysis 76

77 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

78 Preliminary Analysis – Surprises Shareholders Equity section –Reconciliation yielded discrepanciesReconciliation Quarter-to Prior Year Quarter Changes Year-to-Year ChangesChanges

79 XYZ Historical Balance Sheets Source: Audited Financial Statements Balance Sheet Conclusions: Large sums into soft assets Hard assets declined

80 XYZ Historical Income Statements Source: Audited Financial Statements Income Statement Conclusions: Solid gross profit Very heavy debt load Heavy losses in entity investments Heavy debt restructuring Heavy losses in store open/close

81 XYZ Historical Cash Flow Statements Source: Audited Financial Statements Cash Flow Statement Conclusions: Operating cash impacted 2003 Few fixed assets 2002 & 2003 Only financing outflow in 2002

82 Annual Financial Statement Indicators

83 Financial Ratios – Overall Assessment

84 Overall Scoring – Grade Balance Sheet C- –Intangible assets not productive –Inefficient use of long-term debt Income Statement B- –Strong gross profit –Heavy interest expense –Expenses not managed Cash Flow Statement D –Inefficient capital structure Overall Financial Condition C- –Long-term to fix

85 Baseline Financials 85

86 Traditional Ratios Financials Reliable? 86

87 Forensic Tests - Simple 87

88 Earnings Manipulation Tests - Annual Asset Quality Index Total Accruals to Total Assets Index Days Sales in Receivables Index – n/a Inventory IndexInventory Index Sales Growth Index Gross Margin Index

89 Earnings Manipulation Tests - Quarter Comparison of Revenue & Gross Margin Asset Quality Index Total Accruals to Total Assets Index Days Sales in Receivables Index – n/a Inventory Index Sales Growth Index Gross Margin Index

90 Surprises in Shareholders Equity

91 Year-to-Year Discrepancies

92 Equity Declined in 2002

93 Long-Term Debt Increases

94 Other Non-Current Assets Increased

95 Net Intangible Assets Increased

96 Soft Assets Grew Dramatically

97 Large Sums into Soft Assets

98 Revenue-Producing Assets Declined

99 Large Swings in Net Income

100 Impact of Heavy Debt Load

101 Solid Gross Profit

102 Operating Expenses Increased

103 Revenues Increased

104 Cash Earned from Operations

105 Largest Operating Cash Impact

106 Cash Used for Investing

107 Cash In/Out for Financing

108 Only Cash Out in 2002

109 Significant Increase in 2003

110 Significant Increase in 2002

111 Pattern Contrary to Reported

112 2005 Contrary to Reported

113 Contrary to Common-Size

114 2005 Gross Profit Spikes Then Declines

115 Normal Is 1.0

116 Dramatic Variations in 2005

117 Cash Realization Ratio (CRO) Operating Cash / Net Income. 117

118 Asset Quality Index (AQI) (1-Current Assets t + PPE t / Total Assets t divided by: (1-Current Assets t -1 + PPE t -1 / Total Assets-1) 118

119 Depreciation Index (DI) (Depreciation t-1+Net PPE t-1) / (Depreciation t +Net PPE t) 119

120 SGA Expenses Index (SGAEI) SGAEI t / Sales t SGAEI t-1 / Sales t-1 120

121 Good Co. or Bad Co.? - CRO 121

122 XYZ Quarterly 122

123 Good Co. or Bad Co.? - GMI 123

124 XYZ Quarterly 124

125 Good Co. or Bad Co.? - AQI 125

126 XYZ Quarterly 126

127 Good Co. or Bad Co.? - SGI 127

128 XYZ Quarterly 128

129 Good Co. or Bad Co.? - LI 129

130 XYZ Quarterly 130

131 Foundational Discipline Forensic Accounting – Foundational Discipline Fraud Valuation Economic Damages Audit/Review/Comp Forensic Accounting Tax PI, WD Internal Audit Performance Auditing

132 Decompose the Cash Flows 132

133 Is Correlated Cash Flow Improving? 133

134 Cash Flow Correlation IBE - Income before extraordinary items and discontinued operations. CFO - Cash flow from operations. CI - Comprehensive income defined as the change in owners' equity plus dividends net of capital contributions. FCF - Free cash flow is measured by cash flow from operations (CFO) minus net capital expenditures plus net interest payments. 134

135 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

136 Reconcile Equity Beginning Shareholders' Equity 641, , , , , ,574 Net Income/(Loss) 768, , , ,377 27,797 29,475 Dividends Paid Common Stock (500,500) Dividends Paid Common Non-Voting Stock Dividends Paid Preferred Stock Common Stock Issued Common Non Voting Stock Issued 500,500 Treasury Stock Purchased (402,300) (367,050) (205,050) (27,797) Distributions to Shareholder (100,000) Foreign currency translation adj 5,500 Min. pension liability adj. Unreal gains on marketable sec 400 Prior Period Adjustments Other Restatements, Net (298,632) Ending Shareholders' Equity 1,016, , , , ,049 Where are the holes? What do you see?

137 Journal Entries to Normalize 137 The resultant impact of the normalizations is summarized by year below.

138 Show Your Work 138

139 ICE © ICE © C C – Control Bank Statements E E – External Tax Returns Attest Reports I – Internal PBC Financials Operating Reports Proof-of-Cash Timing Non-Cash 139

140 Internal Revenue Service, Part 4 Chapter 10 FSAT – Financial Status Audit Techniques Financial Status Analysis Formal Indirect Methods –Not precluded by books and records. See Lipsitz v. Commissioner, 21 T.C. 917 (1954) 140

141 When to Use Formal Indirect Methods Financial Status Analysis – unbalanced Irregularities – books & records; weak internal controls Gross margin – significant changes; by period or peer group Unexplained deposits in bank accounts Cash deposits –See Inflation and Consumer Spendingwww.bls.gov Net worth increase – not supported 141

142 Financial Status Analysis

143 Formal Indirect Methods Source & application of funds (IRM ) Cash T –a.k.a. Cash T Analysis Bank Deposits & Cash Expenditures (IRM ) Markup Method (IRM ) Unit & Volume (IRM ) Net Worth (IRM )

144 Formal Indirect Methods Source & application of funds Cash T –a.k.a. Cash T Analysis Bank Deposits & Cash Expenditures Markup Method Unit & Volume Net Worth

145 Source & Applications of Funds United States v. Johnson, 319 U.S. 503 (1943) Uses subjects cash flows to compare: –All known expenditures Estimates personal living expenses –All known receipts Takes into account: –Net changes in assets & liabilities –Non-deductible expenditures (for tax matters) –Non-taxable receipts (for tax matters) unreportedIf expenditures > receipts: unreported

146 Source & Applications of Funds Typical uses –Deductions/expenditures out of proportion –Cash does not flow from a bank account –Common use of cash – in or out

147 Source & Applications of Funds Accrual impacts –Beginning A/R shown on debit side i.e., collected during period –Ending A/R shown on credit side i.e., effects a noncash income increase –Beginning A/P shown on credit side i.e., current period cash out –Ending A/P shown on debit side i.e., current period income reduction

148 Source & Application of Funds

149 Cash T Example

150 Formal Indirect Methods Source & application of funds –a.k.a. Cash T Analysis Bank Deposits & Cash Expenditures Markup Method Unit & Volume Net Worth

151 Bank Deposits & Cash Expenditures Gleckman v. United States, 80 F.2d 394 (8 th Cir. 1935) Differs from Bank Account Analysis: –Depth of analysis of ALL bank account transactions –Accounts for cash expenditures –Estimates actual personal living expenditures Theory: only 2 things can occur with $$$: deposit or spent (including hoard)

152 Bank Deposits & Cash Expenditures Assumptions: –Bank deposits, adjustment for non-applicable items reflect taxable receipts (for tax matters) –Outlays on tax return were real: Could only occur from check, cash or credit card If cash, presumes taxable source –Taxpayers burden to demonstrate nontaxable

153 Bank Deposits & Cash Expenditures Used for business & nonbusiness May lead to additional sources If method indicates understatement of income: –Underreported income AND/OR –Overstated expenses

154 Bank Deposits & Cash Expenditures Typical uses: –Books & records unreliable –Deposits suggest income sources –Most expenses paid by check –Account previously used as reporting base Comments: –Significant cash reliance may preclude –Cannot take shortcut –Method incomplete unless cash accounted for

155 Gross Receipts Defined Deposits into accounts Funds expended, but not deposited Funds accumulated, but not deposited

156 Bank Deposits & Cash Expenditures

157 Formal Indirect Methods Source & application of funds –a.k.a. Cash T Analysis Bank Deposits & Cash Expenditures Markup Method Unit & Volume Net Worth

158 Markup Method United States v. Fior DItalia, Inc., l536 U.S. 238 (2002) Reconstructs income: –Uses subject-specific percentages or ratios Obtained from Bureau of Labor statistics or industry sources May use subjects actual markups –May overcome weaknesses of other Formal Indirect Methods when cash is unknown –Cost of Goods Sold verified and used to derive Revenues

159 Markup Method Typical uses: –Inventories are principal income-producing asset and subject records unreliable –Cost of Goods Sold readily ascertained and reasonable certainty re sales prices –Cash-based business, e.g. gasoline retailers, liquor stores, taverns, restaurants, jewelry stores, et al

160 Markup Method Gross Profit Margin to Sales: –(Sales-Cost of Goods Sold)/Sales

161 Markup Method - Example

162 Formal Indirect Methods Source & application of funds –a.k.a. Cash T Analysis Bank Deposits & Cash Expenditures Markup Method Unit & Volume Net Worth

163 Unit & Volume Irby v. Commissioner TC Memo Apply sales price to volume of subject business –Carryout pizza, coin operated laundry, mortuaries

164 Unit & Volume Typical uses: –Units readily ascertained and pricing apparent –Few types of products/services with little variation and price

165 Unit & Volume – Example

166 Item Listing Method Logical starting point Very easy to modify Provides a trail of investigation Leads to other evidence Simple for the court, jury, judge, etc. 166

167 Item Listing - Beginning 167

168 Net Worth Method Very old method: –United States v. Frost 25 F. Cas (N.D. III. 1869) First criminal case involving method: –United States v. Beard, 222 F.2d 84 (4 th Cir. 1955) Approved by U.S. Supreme Court: –Holland v. United States, 348 U.S. 121 (1954) 168

169 Holland Requirements: Establish opening net worth, i.e. base year with reasonable certainty Negate reasonable taxpayer explanations inconsistent with guilt; e.g. non-taxable funds Establish net worth increases attributable to taxable income If no books and records willfulness may be inferred from that fact couple with income understatement If books & records ok on face willfulness might not be inferred from net worth increase alone Government must prove beyond reasonable doubt, but not mathematical certainty

170 (Modified) Net Worth Method Long-recognized by the courts Implied income based upon changes in cost-based net worth (equity) Intuitively understood Relatively simple to prepare Straightforward to explain in court aka Indirect Method 170

171 Net Worth Method 171

172 Example Net Worth Method 172

173 Cash Inflows/Outflows Analysis 173

174 (Modified) Net Worth - Applied Dual Integration of (Pat Perzel): Lifestyle Cash Flow Net Worth 174

175 Forensic Analysis: Unreported Income Non-Business Expenditures 175

176 176

177 MR. & MRS. IMACHEAT LIFESTYLE EXPENDITURES, CONT 177

178 178

179 179

180 180

181 181

182 Proof-of-Cash Traces reported receipts and disbursements to bank statement(s) Relatively simple to prepare Excellent validation tool Intuitively understood Start with annual, drill-down to monthly 182

183 Example Proof-of-Cash (Annual) 183

184 Actual Proof-of-Cash (Monthly) 184

185 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

186 186

187 187

188 188

189 189

190 190

191 191

192 192 Essential Concept of Statistics

193 Descriptive Statistics 1.1A meaningful way to summarize a collection of data: May be presented in tabular, graphic or numerical format Used to provide summaries of the information in a data set Makes the data easier to interpret

194 Inferential Statistics 2.1Inferential statistics relate to: The process of using data from a sample To make estimates and test hypotheses concerning the characteristics of a population 2.2Inferential statistics uses the following groups: Population – is a set of all elements in a particular study Sample – is a subset of the population

195 The Objective of Statistics 3.1Statistical inference is a logical method by which relative truth can be extracted from numerical data: Describes sets of numbers or objects Make reasonable inferences about groups based on incomplete and/or limited information Inferences about the characteristics of a parent group by studying a limited amount of data from a smaller group

196 The Objective of Statistics (cont) 3.2An inference is an educated statistical guess used to solve a particular problem: Relates to the degree of probability of a thing being true or that a particular event will occur Statistics are only mathematical estimates

197 The Objective of Statistics (cont) 3.3 Statistics are rarely presented, absent a statement of precision (confidence). These are related issues: Precision relates to the probability associated with the estimate and the degree upon which the user can rely on the inference Any statistical study that omits a statement of precision should be scrutinized for bias or prejudice Precision is dependent on: Properly selected sample or samples Correctly applied statistical methods

198 The Objective of Statistics (cont) 3.4 Therefore, to a statistician, precision is used in two contexts: the variability of an estimator (e.g. the mean) or in regards to the probability of estimating something to within a certain error

199 Common False Inferences 4.1Statistical analysis is subject to: Manipulation Errors in the application of formula, methods and procedures Erroneous or false inferences from incomplete or tainted data Random chance, unanticipated events and unintended consequences

200 Common False Inferences (cont) 4.2Arithmetic errors and false inferences are found frequently in published studies: The public is conditioned to accept published studies as authoritative, error free and absolute truth Random and systematic errors occur frequently in published surveys and studies Published studies are regularly challenged, recanted and redacted

201 Common False Inferences (cont) 4.2Arithmetic errors and false inferences are found frequently in published studies (cont): It is essential to understand the objective, procedures applied and inferences asserted in published studies and surveys The limiting conditions published with a study or survey must be reviewed Adjectives are frequently used to emphasis or de- emphasis small statistical margins

202 Common False Inferences (cont) 4.3False Percentages Addition of unrelated percentages to achieve a total percentage Large percentages based on small data samples Percentages presented absent actual numbers

203 Common False Inferences (cont) 4.4Fictitious Precision Precision of an estimate does not increase proportionally with the number of decimal places: Verify that the degree of precision claimed is warranted by the data Percentages should be rounded to significant decimal places rounded to.12 or 12.35% rounded to 1.23 Numbers should be rounded to significant digits 123,456 rounded to 123, ,456,789 rounded to 123,500,000

204 Common False Inferences (cont) 4.4Fictitious Precision (cont) However – from a pure statistical viewpoint, precision of an estimator does increase with sample size Remember! Precision is used in two different ways in statistics

205 Common False Inferences (cont) 4.5Faulty Comparisons Comparisons are relevant only when made between groups genuinely suitable for comparison Statistics of one group can not be used to infer characteristics of an untested and dissimilar group Both groups must be measurable to demonstrate a correlation Publicly traded companies compared with small closely-held companies

206 Common False Inferences (cont) 4.6Improper Sampling The most common cause of misleading and false inferences Biased and/or non-random sample Exclusive or skewed samples Arbitrary sampling is not random Systematic non-responses or anomalies in sample are not investigated

207 Descriptive Statistics Methods 5.1Frequency Distribution – Definition A tabular summary of data showing the number (frequency) of items in each of several non- overlapping classes is referred to as a frequency distribution.

208 Descriptive Statistics Methods 5.2Examples Soft DrinkRelative FrequencyPercent Frequency Coke Classic.3838 Diet Coke.1616 Dr. Pepper.1010 Pepsi-Cola.2626 Sprite.1010 Total Relative and Percent Frequency Distributions of Soft Drink Purchases Bar Graph of Soft Drink Purchases Pie Chart of Soft Drink Purchases

209 Measure of Central Tendency Also known as the measure of location 6.1Mean – definition and use: – The average value of the numbers selected; obtained by summing the numbers and dividing by n, where n is how many numbers there are in the collection – The only measure of central tendency that is sensitive to all values in the distribution

210 Measure of Central Tendency (cont) 6.2Median – by definition: The halfway point in the following sense: half of the numbers lie below the median, and half of them lie above it Not sensitive to any values in the distribution, only the number of elements

211 Median is preferable when: The distribution of quantitative data is extremely asymmetrical The precise location of the bisected halves of the distribution is material 6. Measure of Central Tendency (cont)

212 Median is calculated by: Sort the observations in ascending order Locate the middle observation For odd number distributions the median is the middle observation For even number distributions the median is the midpoint between the two middle observations Middle observation =.5(n+1); where n is the number of observations 6. Measure of Central Tendency (cont)

213 Measure of Central Tendency (cont) median Mean = Average = = 150 = Calculation of a mean and median

214 Mode – definition and use: The number that occurs most frequently in a collection of numbers The mode is not sensitive to any values only the frequency of specific elements Used in situations which preclude the use of the mean or median Data is qualitative and not ordered Identification of the most frequent responses or occurrences are material 6. Measure of Central Tendency (cont)

215 Measure of Central Tendency (cont) 6.5 Calculation of the Mode

216 Some examples of frequency diagrams: Normal distribution Bimodal Distribution Asymmetrical (with a tail) – positive and negative skew 6. Measure of Central Tendency (cont)

217 Measure of Central Tendency (cont) mean, median, and mode 6.7Normal Distribution The frequency is symmetric with a single mode

218 Measure of Central Tendency (cont) mean, median mode 6.8 Bimodal Distribution The distribution has two modes

219 Measure of Central Tendency (cont) median mean mode 6.9Distribution that is asymmetrical; in this case with a tail to the right – positive skew

220 Measure of Central Tendency (cont) 6.10Quartiles It is often desirable to divide data into four parts With each part containing approximately one fourth, or 25% of the observations

221 Measure of Central Tendency (cont) 6.11Quartiles (cont) The division points are referred to as the quartiles and are defined as: Q1 = first quartile, or 25 th percentile Q2 = second quartile, or 50 th percentile (also the median) Q3 = third quartile, or 75 th percentile.

222 Measure of Central Tendency (cont) 6.12Location of Quartiles 25% First Quartile (25 th percentile) Second Quartile (50 th percentile) Third Quartile (75 th percentile) Q1Q1 Q2Q2 Q3Q3

223 Measure of Central Tendency (cont) 6.13New Clients by Sales Representatives: First Quartile (2,865) Second Quartile (2,905) Third Quartile (3,000) Q1Q1 Q2Q2 Q3Q3

224 Range The simplest measure of variability The difference between the largest and smallest numbers in a group Seldom used as the only measure Based on only two of the observations Therefore, influenced by extreme values 7. Measure of Variability

225 Range (cont) Largest value – smallest value = range 5,000 – 3,000 = 2,000 10,000 – 3,000 = 7,000 Refer to 6.13 for new clients: Range is 3,325 – 2,710 = ,000 new clients instead of 3,325, the range would be: 10,000 – 2,710 = 7, of the 12 sales representatives within 2,170 and 3, Measure of Variability (cont)

226 Variance The variance is a widely used measure of dispersion A measure of variability that utilizes all the data Based upon the difference between the value of each observation and the mean. If the numbers in the list are all close to the mean, the variance will be small If they are far away, the variance will be large 7. Measure of Variability (cont)

227 Measure of Variability (cont) Variance 7.2 Variance (cont) Variance is the sum of squared deviations of observations around their mean: mean (-10) data point Measurement of distance from the mean

228 Measure of Variability (cont) 7.3Calculating the Variance For the number of students in a classroom, we first find the distance from the mean for each element: Students per classroomDistance from mean – 30 = (-5) – 30 = – 30 = – 30 = (-8) – 30 = (-2) Mean = 30 Total -0-

229 Measure of Variability (cont) 7.4As observed in the previous slide: We cannot add distances up and average them Negative numbers and positive numbers cancel out Resulting in zero We must square each of these numbers, making all positive numbers

230 Measure of Variability (cont) Therefore, we must square each of these numbers, making them all positive: Students per classroomDistance from meanDistance Squared – 30 = (- 5) (-5)² = – 30 = 10 10² = – 30 = 5 5² = – 30 = (- 8) (-8)² = – 30 = (- 2) (-2)² = 4 Total: 218

231 Measure of Variability (cont) The calculations would then be: Mean = 30 Number of classrooms (n) = 5 Variance = (x i – x)² = 218 = 54.5 n Variance = 54.5 Units² Standard Deviation = 54.5 Standard Deviation = Units

232 Standard Deviation The measure of dispersion in original units The positive square root of the variance Variance is in squared units Standard deviation is in original units 7. Measure of Variability (cont)

233 Coefficient of Variation (CV) » A descriptive statistic that indicates how large the standard deviation is relative to the mean » It is usually expressed as a percentage » The formula is CV = standard deviation x 100% mean 7. Measure of Variability (cont)

234 Coefficient of Variation (CV) (cont) » This ratio is useful when comparing the variability of variables that have different standard deviations and different means 7. Measure of Variability (cont) CV = % = 24.61% 30

235 Statistics in Valuation Dispersion

236

237 Statistics in Valuation Significance

238 Measure of Relative Location 8.1The measure of the shape of the distribution is also important Skewness is an important numerical measure of the shape of a distribution Compliment the measures of location and variability In a symmetric distribution – mean and median are equal When positively skewed – mean greater than median When negatively skewed – mean less than median

239 Measure of Relative Location (cont) 8.1 Skewness for Two Distributions

240 Measure of Relative Location (cont) 8.2Z-score Expresses a measure in terms of the number of standard deviations the measure is from the mean Often called standardized value Z 1 = 2.2 would indicate that X 1 is 2.2 standard deviations greater than the mean

241 Chebyshevs Theorem Allows us to make statements about the proportion of data values that must be within a certain number of standard deviations of the mean 8. Measure of Relative Location (cont)

242 Chebyshevs Theorem (cont) Some of the implications of this theorem, with z = 2, 3, and 4 standard deviations, follow. – At least.75, or 75%, of the data values must be within z = 2 standard deviations of the mean – At least.89, or 89%, of the data values must be within z = 3 standard deviations of the mean – At least.94, or 94%, of the data values must be within z = 4 standard deviations of the mean 8. Measure of Relative Location (cont)

243 Empirical Rule Based upon the normal probability distribution Distributions are symmetric mound-shape or bell shaped Determines the percentage of items that must be within a specific number of standard deviations of the mean Formula - When the distribution of data approximates a normal (symmetrical) curve, the percentage of items that must be within a specified number of standard deviations of the mean can be estimated 8. Measure of Distribution Shape

244 Empirical Rule (cont) Application - For normally distributed data: Approximately 68% of the items will be within one standard deviation of the mean Approximately 95% of the items will be within two standard deviation of the mean Approximately 100% of the items will be within three standard deviation of the mean 8. Measure of Distribution Shape (cont)

245 Outliers Represents a data set where one or more observations have unusually large or small values May represent a data value that has been incorrectly recorded May be from an observation that was incorrectly included in the data set May be an unusual data value that has been recorded incorrectly and belongs in the data set, and should remain 8. Measure of Distribution Shape (cont)

246 Relation Between Two Sets of Measures 9.1 Types of Measurement Charts – Scattergram – is used to display graphically the relationship between two different measures in a sample – Dot Plot – one of the simplest graphical summaries of data – Scatter Diagram – a graphical presentation of the relationship between two quantitative variables – Trendline – a line that provides an approximation of the relationship between two variables

247 Statistics in Valuation Correlation

248 Presentation of Data 10.1Descriptive statistics Methods of organizing and summarizing data to reveal patterns and facilitate interpretation Numeric statistics Pictorial statistics

249 Presentation of Data (cont) 10.2Numerical statistics Numeric values (measures) describing the data set Measurements include: Measures of central tendency Measures of variability

250 Presentation of Data (cont) 10.3Pictorial statistics Presents numerical statistics in the form of pictures and graphs Tabular procedures Graphical procedures

251 Presentation of Data (cont) 10.3Pictorial statistics (cont) Presentation depends on the type of data Qualitative data is numerical data about categories that vary significantly in kind – Best represented in Bar Charts and Pie Charts Quantitative data can be measured in amounts – Best represents in Dot Plot or Histograms

252 Presentation of Data (cont) 10.4Types of graphical presentations Bar chart A graphical device for depicting qualitative data summarized by frequency Can be arranged horizontally or vertically Ordering is ascending or descending Spaces are present between bars to define categories Bars are of equal width

253 Presentation of Data (cont) 10.4Types of graphical presentations (cont) Dot plot A graphical device for depicting quantitative data summarized by frequency Generally used for a small set of values or data Dots, representing a measure of value, are placed above a reference number on a horizontal axis

254 Presentation of Data (cont) 10.4Types of graphical presentations (cont) Pie chart A graphical device for depicting qualitative data summarized in a frequency distribution or a relative frequency distribution Sections are presented in ascending order Effective when the elements or classes are few in number Addresses the limitations of bar charts and dot plots by representing the proportion of each element or class

255 Presentation of Data (cont) 10.4Types of graphical presentations (cont) Histogram A graphical device for depicting quantitative data summarized in a frequency distribution or a relative frequency distribution Sections are presented in ascending order The range of values are divided into non-overlapping, equal length class intervals – The number of intervals should be more than 5 and less than 20 – Class intervals are not separated by spaces as with a bar chart

256 Conclusion 11.1Statistics are not truth Statistics are relative truth extracted from numerical data Statistics are estimates based on incomplete information Statistics are subject to manipulation and misinterpretation Statistical information must be sufficiently evaluated before use as a foundation for an opinion 11.2Statistical analysis is not a substitute for common sense and logical reasoning

257 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

258 Digital Analysis 258

259 259 Use Of Technology To Uncover Fraud Input Master Employee File Master Vendor File Forensic Software Database Match of similar characteristics such as: Telephone number Address Output

260 DigitalAnalysis Digital Analysis Procedures used to analyze the digit and number patterns of data sets, with the aim of finding anomalies and reporting on broad statistical trends Benfords Law, duplicate numbers, round numbers, etc. 260

261 Benfords Law 261

262 Benfords Law - Requirements Sizes of similar phenomena –e.g. Revenues for corporations on the NYSE No built-in minimum or maximum numbers –Zero is an acceptable as a minimum No assigned numbers –i.e. social security numbers or zip codes Follow geometric pattern when ranked smallest to largest –More small items than larger items –The numeric mean should exceed the numeric median 262

263 Benfords Law – Major Digit Tests 263

264 Benfords Law – Major Digit Tests Benfords Law tests results can provide a roadmap for the investigation as well as provide indirect evidence. –The 1 st and 2 nd digit test are high level and not used to select samples. –The 1 st two and 1 st three digits tests are designed to select audit samples. –The last two digits test detect excessive rounding or numeric invention. Existence of a pattern or benchmark –Not necessarily one consistent pattern, but some pattern which false or wrong data will deviate from. –Before and After Testing. 264

265 First Digit Test 265

266 Second Digit Test 266

267 First Two Digits Test 267

268 First Three Digits Test 268

269 Last Two Digits Test 269

270 Numeric Tests 270

271 Duplicate Numbers Test 271

272 RoundNumbers Round Numbers 272

273 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

274 Behavior Detection: Interviewing & Interrogation

275 Facts about Lying Individuals are trained liars People will defend themselves by lying until the pain of their conscience becomes unbearable or until outside influences prompt to reveal their guilt Completely voluntary confessions are a myth Lying is a stressor, which exhibits signs/symptoms. Lying is hard work, which is revealing Lying subjects will issue qualifiers

276 The person being lied to is personally acquainted with the liar The person being lied to is not easily deceived Facts about Lying The lie is not expected The lie is challenged The liar has little experience The consequences are high Lies are more likely to be detected when:

277 Communication Early landmark study found that communication is –38% vocal (pitch, stress, tone, pauses) –55% physical (expressions, gestures) –7% verbal (content) – Active listening required Subsequent studies have refined these numbers, but consistently find that at least of meaning is communicated nonverbally Nonverbal

278 Generally Truthful Behavior Direct answers – nothing to hide, facts are allies Spontaneous answers – nothing to think about Generally attentive and interested – not distracted Nonverbally engaged – oriented toward the interviewer Verbal and nonverbal consistency – words and behavior in agreement

279 Clustering Deceptive behaviors usually occur in clusters of two or more Clusters –Disregard transient (isolated or individual) behaviors –The first deceptive behavior must begin within 5 seconds of stimulus –The second behavior must follow shortly thereafter –The greater the number of behaviors in the cluster the greater the likelihood of deception

280 Deceptive Verbal Behaviors Failure to answer the question – influence Failure to deny – involves an act of wrongdoing Repeating the question (as opposed to seeking clarification) – buys time Overly specific answers –Narrows the question – selectively excludes negative information –Attempt to influence, mislead, buy time

281 Classic Overly Specific Answer I did not have a twelve-year affair with Ms. Flowers. William Jefferson Clinton1992 presidential campaign The fact is, there was no twelve-year affair. William Jefferson Clinton, My Life We are going to cut taxes for 95 percent of Americans. Barack Hussein Obama 2008 campaign promise

282 Deceptive Verbal Behaviors Inappropriate level of concern – influence Verbal attacks directed at the accuser – attempt to influence Detour statements –As I said before … –However, in this [other] instance … –That reminds me of something that happened last week …

283 Deceptive Verbal Behaviors Invoking religion –I swear to God –As God is my witness (AFI Top 100) Failure to understand a simple term or question – attempt to buy time Selective memory –Not that I can recall/remember –To the best of my knowledge Statements that fail to answer a question Thats a good question I knew you were going to ask me that

284 Deceptive Nonverbal Behaviors: Grooming Gestures Adjusting clothes, hair, jewelry, glasses Inspecting hands and nails Cleaning up surroundings Lint picking Scratching Smoothing

285 Deceptive Nonverbal Behaviors: The Telltale Face Touching the face Covering mouth or eyes Biting lips Clearing throat Labored swallowing Coughing Wiping sweat

286 Facial Mapping: Paul Ekman(born 1934) –psychologist studied emotions and their relation to facial expressions –One of the100 most eminent psychologist of the 20th century –Facial expressions are not culturally determined but universal …including anger, disgust, fear, joy, sadness, surprise, & contempt –Developed the Facial Action Coding System (FACS) to taxonomize every conceivable human facial expression –FACS: interpreting involuntary expressions to understand our real emotions, reactions, intentions; careful analysis can be used to gauge a subjects real reaction –Also contributed significantly to the study of lying –Jones, D. (2008, February 25). Its written all over their faces. USA Today. READ FOR HOMEWORK! ceo-faces_N.htm Deceptive Nonverbal Behaviors: The Telltale Face

287 Business Records Authentication – someone must be familiar with the content and record made in ordinary course of the business Witness has personal knowledge of record Witness removed a record from a file Witness recognizes the record as the one removed Witness can explain the document FRE 803 – Business Records

288 Exclusionary Rule Evidence illegally obtained or analyzed is inadmissible in criminal trials Constitutional basis –Fourth Amendment – protection from unreasonable search and seizure –Fifth Amendment – protection from compelled self-incrimination –Fourteenth Amendment – due process clause extended the exclusionary rule to state courts

289 Illegally-Seized Evidence Specifically, the Fourth Amendment excludes evidence obtained by authorities without –a warrant –or authorization from the owner This rule generally only applies in criminal cases or to government employers There is no prohibition against a private employer admitting evidence that was seized illegally However, the employer may be exposed to litigation for invasion of privacy or trespass

290 Chain of Custody Gaps in the chain or mishandling of evidence can damage a case Evidence may still be admissible if it can be authenticated by an identifying feature, but a mistake in custody affects the weight of the evidence In fraud cases, maintaining custody is particularly significant for electronic evidence (concern regarding alteration) – hand-to-hand chain of custody detailing how it was stored and protected from alteration

291 Tagged at Scene Chain of Custody Property Room Evidence and Case Building Court Property Returned to Owner Final Disposal Crime Lab Review by DA and Defense

292 Computer Evidence Protecting data in hardware seizures Insidious s Computer log files Electronic documents and files Internet log files Insure expertise is present to process electronic evidence

293 Computer Forensics Art and science of applying computer science to aid the legal process. Computer forensics = Crime scene investigations (apply the same principles). Establish parameters of the scene Physically secure the scene. Physically secure evidence.

294 Electronic Evidence What forms of electronic evidence do you come in contact with? Who processes this evidence? What potential hurdles do you face? How do you guarantee chain of custody requirements? Will your collection standards/procedures meet all legal challenges?

295 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

296 Example Balance Sheet 296

297 Visual Balance Sheet 297

298 Example Income Statement 298

299 Visual Income Statement 299

300 Side-by-Side Comparison 300

301 Cash Flow Statement 301

302 Decomposing the Income Statement 302

303 Translate data into pictures & Opinions into sound bites. Dominant, deliberate and declining… Family, failure and fate… 303

304 Key Indicators… 304

305 LSAT – Linguistic Style Analysis Technique

306 Today – Session Description… reverseWill preview in reverse order 1.Court cases re forensic accounting 2.Applied forensic techniques, e.g. guidelines 3.Accelerate (throughout)

307 Valuation Representation Letter

308 25+ New forensic accounting techniques… Full-and-False Inclusion Genogram TATA/TARTA/TITA/TDTA/TAPTA AQI Behavior Detection FACS Styleometry ICE©/SCORE© Link Analysis Articulated Cash Flow Dechow-Dichev Techniques Timeline Analysis IRS Formal Indirect Methods A(5) Cash-T (Modified) Net Worth Bank Deposits & Cash Expenditures Markup Unit & Volume Expectations Attributes Gap Detection Proof-of-Cash Deposition Matrix Entity(s) Chart Lev-Thiagarajan Techniques Damages Report Card Digital Analysis CATA/CRO MSSP 308

309 Foundational Discipline Forensic Accounting – Foundational Discipline Fraud Valuation Economic Damages Audit/Review/Comp Forensic Accounting Tax PI, WD Internal Audit Performance Auditing

310 Forensic Accounting Organizations ACFE – Association of Certified Fraud Examiners: ACFE – American College of Forensic Examiners: AICPA – American Institute of Certified Public Accountants: CICA – Canadian Institute of Chartered Accountants: IIA – Institute of Internal Auditors: IMA – Institute of Management Accounting: and NACVA – National Association of Certified Valuation Analysts: 310

311 Assignment Development INTERPERSONAL DATA COLLECTION AND ANALYSISTRIAL ReferencesPurpose of StagePotential IssuesDeliverables Tasks to be Performed u Obtain the results of the case u Benefit from the experience u Learn from the experience u Assess firms performance u TASKS Evaluate performance of each party Update cv Extract show and tell as feasible\ Follow-up with counsel; grading form u Continuous professional development u Verdict u Judges Ruling u Lessons Learned u Updated cv Forensic Accounting/Investigation Methodology (FA/IM)© Trial Preparation Data Collection Interviews & Interrogation Scoping Background Research Laboratory Analysis Undercover Confidential Informants Surveillance -Electronic, Physical Analysis of Transactions Testimony & Exhibits Post- Assignment FOUNDATIONAL u Your previous results FOUNDATIONAL INTERPERSONALDATA COLLECTION AND ANALYSISTRIAL 311

312 Forensic Valuation: Faster, Better, Higher Return© The Combat CPA© Series… Darrell D. Dorrell, CPA/ABV, MBA, ASA, CVA, CMA, DABFA financialforensics® Foundation for Accounting Education – FAE 2009 Business Valuation Conference New York, New York – May 21, 2007


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