C:\_Papers\_Uncooking

1-6-10-8pm_doc__tony.tinker-UNCOOKING_THE_BOOKS_FROM_TOXIC_PAPER_SUB-PRIMEMORTGAGES_CDS_AND_CSOS_MATERIAL_MISSTATEMENTS.doc

UNCOOKING THE BOOKS FROM TOXIC PAPER SUB-PRIME MORTGAGES CDS AND CSOS MATERIAL MISSTATEMENTS OF THE FINANCIAL SERVICES INDUSTRY:  A BARCLAYS BANK PLC CASE STUDY

                                    Avi Rushinek,Ph.D.

                                    University of Miami

                                    Coral Gables, Fl 33124

                                    arush@miami.edu

Send Correspondence to:

                                    Sara Rushinek

                                    Unversity of Miami

                                    CIS Department

                                    Coral Gables, Fl 33124

                                    305.666.7890

                                    s.rushinek@miami.edu

UNCOOKING THE BOOKS FROM TOXIC PAPER SUB-PRIME MORTGAGES CDS AND CSOS MATERIAL MISSTATEMENTS OF THE FINANCIAL SERVICES INDUSTRY:  A BARCLAYS BANK PLC CASE STUDY

Abstract

This is a study of the sub-prime mortgages, Credit Default Swaps (CDS), and Collateralized Synthetic Obligations (CSOs) cooking the books of the financial services industries around the world.  This case study develops a methodology of uncooking the books from material misstatements of the financial industry using Barclays Bank as a classic case study.   It shows how sub-prime mortgages, CDS, and CSOs overstated the revenues of the financial services industries leading to the stock markets melt-down of October 2008.  This research develops multiple regression models and software that simulates such frauds automatically.  The internet based software scans the World Wide Web and the financial statements to detect the frauds that it perpetrated.  It reveals the best fraud predictors.  It ranks and rates the financial ratios according to their predictive power, assess the fraud amounts and sources, and most importantly it correctly restate the financial statements of company discarding the phony sales, and marking to market the overstated assets.  It applies these methods and software to the financial services industries using Barclays Bank as a typical case study.

This study develops a method for fraud “finger print” definition. The regression parameters define parts of the fraud finger print. Some of these parameters include such variables as the upper and lower bounds of the coefficients of the best fraud predictor variables, and its statistical significance. These finger prints define Expert System (ES) rules for a Case Based Reasoning (CBR) Knowledge Base (KB). The CBRKB should eventually contain all the possible combinations of fraud finger prints for each Standard Industry Classification (SIC) code. We also define the fraud rate of over or under statements of financial accounts, and its time periods, as well as its amounts and their pattern, since they may affect the fraud finger print. Such finger prints together with other fraud characteristics should help an ES diagnose fraud patterns by benchmarking a suspicious company against its peers. The resulting anomalies will fire the ES rules that will help trace the source of the fraud.

Introduction & Literature Review

BARCLAYS BANK COOKING THE BOOKS MATERIAL MISSTATEMENTS GOOGLE SEARCH PHRASE

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A search of Google using the phrase "Barclays Bank cooking The Books material misstatements" reveals the following hyperlinks among others: Peter Viles, peter.viles@latimes.com, May 2007, writes on the http://latimesblogs.latimes.com/laland/mortgages/  about "Blame game: KPMG accused of lax auditing of New Century."  The internet sites report that "Driven by a 'brazen obsession' with generating sub-prime mortgages, Irvine's New Century Financial Corp. engaged in improper accounting that overstated its profit and allowed top executives to reap millions of dollars in inflated or undeserved bonuses, a U.S. Bankruptcy Court examiner said in a report released Wednesday."

"Michael J. Missal's report said senior managers 'turned a blind eye' to the 'ticking time bomb' created by the high-risk lending in 2005 and 2006. At the same time, Missal said, New Century's auditor, KPMG, contributed to the problems by failing to exercise due care in reviewing its books, leading to material misstatements in New Century's financial reports."

From The New York Times: "In a sweeping accusation against one of the country’s largest accounting firm, an investigator released a report on Wednesday that said 'improper and imprudent practices' by a once high-flying mortgage company were condoned and enabled by its auditors."

"KPMG denies the accusations in the report, which was commissioned by the United States Trustee overseeing the New Century bankruptcy."

BARCLAYS GROUP U.S. THE SAFEST LENDERS FOLLOWED BY THE TOXIC PAPER LOADED "COUNTRYWIDE"

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The same site reports about "Ranking lenders by risky loans." It asks: "Why are some lenders failing and others hanging in?" Ranking agencies like "SMR ... ranked 163 U.S. lenders according to credit risk. A score of 1,000 was average. ... Here are a few of the surviving lenders whose credit risk score ranked higher than the 1,000 average: "Barclays Group U.S. 2,220" is leading the list of some of the safest lenders followed by: "H & R Block Mortgage 1,770; Quick Loan Funding 1,662; Indymac Bank, 1,609; Metrocities Mortgage 1,466;" and the toxic paper loaded "Countrywide" with an "above average score of "1,016."

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CREDIT DEFAULT SWAP CDS

Matthew Monks, September 9, 2008 2:28 PM ET, on (Matthew Monks at mmonks@financialweek.com and the editors at fw_editor@financialweek.com) http://www.financialweek.com/apps/pbcs.dll/article?AID=/20080909/REG/809099979/1036  reports that "Mortgage bailout could trigger massive credit default swap settlement Payouts to be limited, though, as Fannie, Freddie bonds likely to be settled close to par." 

“The government takeover of Fannie Mae and Freddie Mac could trigger the largest credit default swap settlement ever. Actual payments could be limited, however, as a result of the relatively high value of the mortgage underwriters’ bonds. Investors may be forced to settle contracts covering the mortgage giants’ $1.6 trillion in outstanding debt because the government seizure constitutes a credit event that triggers the payment or delivery of their bonds. The International Swaps and Derivatives Association announced on Monday that it would establish a protocol to facilitate the settlement of CDS trades involving Fannie Mae and Freddie Mac.”

“This will likely be the largest CDS credit event in terms of the amount of CDS contracts outstanding,” J.P. Morgan analyst Eric Beinstein wrote in a report released on Monday "The settlement process will likely take 30 days, with investors cashing their CDS contracts at a price established through an auction process. Payouts could be limited, though, because most analysts believe CDS covering the mortgage companies’ senior debt will be settled between $98 and par, RBC Capital Markets analyst T.J. Marta wrote in a report to clients. In a cash CDS settlement, buyers are paid the difference between the par value and market value of the debt obligation. In related news, at least two analysts believe new Fannie Mae and Freddie Mac CDS contracts will likely be introduced under different terms.

Barclays Capital analysts Vince Breitenbach and Jeff Meli wrote in a report that Barclays

“believes the appointment of a conservator for the [firms] constitutes a credit event for

[their] senior and subordinated CDS. As such, new CDS contracts without the conservatorship trigger should begin to trade.”

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FANNIE, FREDDIE CREDIT-DEFAULT SWAPS MAY BE SETTLED

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Oliver Biggadike and Shannon D. Harrington, on Sept. 8 (Bloomberg) writes -- "Investors may be forced to settle contracts protecting more than $1.4 trillion of Fannie Mae and Freddie Mac bonds against default after the U.S. seized control of the companies in a bid to bolster the housing market (Oliver Biggadike in Sydney at obiggadike@bloomberg.net; Shannon D. Harrington in New York at sharrington6@bloomberg.net).

Thirteen ``major'' dealers of credit-default swaps agreed ``unanimously'' that the rescue

constitutes a credit event triggering payment or delivery of the companies' bonds, the

International Swaps and Derivatives Association said in a memo obtained by Bloomberg News today. Market makers for the privately traded contracts will discuss how to settle them in a conference call at 11 a.m. in New York." ``This is a big deal,'' said Sarah Percy-Dove, head of credit research at Colonial First State Global Asset Management in Sydney. ``The market is not experienced at settling a credit event for a name of this size, so it is a bit of an unknown.''

A settlement likely would be the largest in the market's decade-long history. Credit-default swaps on Fannie and Freddie have been among the most actively traded the past few months, according to reports from broker GFI Group Inc. Both companies also are among 125 companies in the benchmark Markit CDX North America Investment Grade Index, the most actively traded contract in credit markets, which investors use to speculate on corporate creditworthiness or to hedge against losses."

CONSERVATORSHIP IS A CREDIT EVENT BARCLAYS PLC ANALYSTS NOTE TO CLIENTS

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``We believe conservatorship is a credit event,'' Barclays Plc analysts Vince Breitenbach and Jeff Meli said in a note to clients yesterday. Barclays is a member of the ISDA. U.S. default protection costs as measured by the Markit CDX North America Investment Grade Index will also decline, they said. A basis point, or 0.01 percentage point, is worth $1,000 on a swap that protects $10 million of debt reported by Oliver Biggadike in Sydney at obiggadike@bloomberg.net and Shannon D. Harrington in New York at

sharrington6@bloomberg.net ;

http://www.bloomberg.com/apps/news?pid=20601087&sid=aa6nmsv7BakE&refer=home claims that "Lehman Credit-Swap Auction Sets Payout of 91.38 Cents" by Shannon D. Harrington and Neil Unmack on Oct. 10 (Bloomberg) -- "Sellers of credit-default protection on bankrupt Lehman Brothers Holdings Inc. will have to pay 91.375 cents on the dollar to settle the contracts, setting up the biggest-ever payout in the $55 trillion market.

An auction to determine the size of the settlement on Lehman credit-default swaps set a value of 8.625 cents on the dollar for the debt, according to Creditfixings.com, a Web site run by auction administrators Creditex Group Inc. and Markit Group Ltd. The auction may lead to payments of more than $270 billion.”

“No one knows exactly who has what at stake because there's no central exchange or system for reporting trades. Sellers are required to post collateral, or pledge assets, to the buyer of protection, known as the counterparty, on the other side of the trade if the value of their positions declines. Because Lehman's bonds had already fallen, the collateral has probably been posted, Yelvington said."

“Hedge funds, insurance companies and banks typically buy and sell credit protection, which is used either to insure a bond against default or as a bet against the company's ability to pay its debt. The payments ``are insignificant when put into the context of the trillions of dollars of payments that are made through settlement systems each and every day.''

“Some funds may be forced to dump assets to meet the payment demands if they haven't hedged, … Banks can go to the Federal Reserve, or use the commercial paper market where it is still functioning'' to meet protection payments, said Cicione, who said a 9.75 cent recovery rate would lead to payments of about $270 billion. But fund managers or hedge funds, once they've used their cash, have only one option: to sell assets.''

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OFFSHORE BANK, A BERMUDA-BASED COMPANY HAD MADE BETS WHICH THE ICELANDIC GOVERNMENT SEIZED ... STOCK WAS HALTED FROM TRADING ON THE NEW YORK STOCK EXCHANGE YESTERDAY AFTER FALLING DOWN 89 % PERCENT THIS YEAR ... THE ICELANDIC BANKS THAT FAILED THIS WEEK WERE ALSO OFTEN INCLUDED IN CDOS CREATED DURING 2006

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A unit of Primus Guaranty Ltd., a Bermuda-based company that has sold more than $24 billion in credit-default swaps, said last month it guaranteed $80 million of Lehman debt. The firm sold protection on $215 million of Fannie and Freddie debt and $16.1 million on WaMu. Yesterday, it said it also had made bets of $68.2 million on Kaupthing Bank hf, which the Icelandic government seized.

Primus said last week it had $820 million in cash and liquid investments to meet claims on the contracts. The stock was halted from trading on the New York Stock Exchange yesterday after falling to 99 cents. The shares, down 89 percent this year, slumped 15 cents, or 17 percent, to 75 cents.

The failures of Lehman, once the fourth-largest securities firm, and Seattle-based Washington Mutual Inc. as well as the government takeovers of Fannie Mae, Freddie Mac and Iceland's biggest banks have provided the 10-year-old credit-default swaps market with its biggest test to date.

The use of credit derivatives has grown more than 100-fold in the past seven years as investors began using the swaps to bet on companies' creditworthiness.  The Federal Reserve Bank of New York met with credit swap dealers and exchanges today to

expedite efforts for a market clearinghouse that would reduce risks and absorb counterparty losses resulting from the failure of market makers such as Lehman."

"``CDS contracts did not cause any firm to fail,'' Pickel said. ``The underlying cause of

problems that has affected firms is the risk that they chose to take on. ''Credit-default swaps are financial instruments that can be based on bonds and loans. They pay the buyer face value in exchange for the underlying securities or the cash equivalent should a borrower fail to adhere to its debt agreements.

Five-year credit-default swaps on Lehman rose as high as 790 basis points before the firm filed for bankruptcy, according to Phoenix Partners Group, a New York-based inter-dealer broker. A basis point on a credit-default swap contract protecting $10 million of debt from default for five years is equivalent to $1,000 a year.

Standard & Poor's has rankings on 1,889 CDOs that sold credit-default swap protection on Lehman, the New York-based ratings firm said last month. Pieces of 1,526 CDOs sold protection on Washington Mutual, S&P said. More than 1,200 made bets on both Fannie and Freddie. The Icelandic banks that failed this week were also often included in CDOs created during 2006 and 2007, according to Sivan Mahadevan, a New York-based Morgan Stanley strategist."

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AS LAW PROFESSOR WARNS THE INTERNET ABOUT TOXIC PAPER RISK THE AUDITORS FIND NO MATERIAL MISSTATEMENTS

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As Law Professor Warns The Internet About Toxic Paper Risk And Material Misstatements

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http://lawprofessors.typepad.com/securities/news_stories/index.html, reports on

August 23, 2007 "Rumors of a Sale of Bear Stearns."  It asks "Is Bear Stearns on the auction block?  Hit hard by the collapse of the subprime mortgage industry, the drop in its stock price could make it a bargain.  Possible suitors include ... Barclays ... which earlier this summer bought a 10% stake in The Blackstone Group."

On March 27, the site reports "New Century's Bankruptcy Expected,"  ... the poster child for the collapse of the subprime mortgage industry, is expected to file for bankruptcy soon, as both Barclays Bank ... took back loans that secured New Century's financing and plan to auction them off.

On September 22, 2008 the site writes about "Bankruptcy Court Approves Sales of Lehman Broker Dealer to Barclays." They proceed to report: "Questions raised about Lehman's transfer of assets among its businesses in the days before its bankruptcy filing did not interfere with swift action by the bankruptcy court to approve the sale of substantially all of the assets of Lehman Brothers, Inc., to Barclays Capital. The court’s decision followed a marathon eleven-hour hearing in a packed Manhattan courtroom where attorneys from the SEC and other government agencies successfully supported Lehman’s argument that swift approval of the deal was in the national interest. The national interest" apparently is the prompt transfer of the broker-dealer's customer accounts instead of a lengthy brokerage liquidation process. The transfer of most retail accounts, which hold over one hundred billion dollars in assets, is expected to be completed within days. In one of the last liquidations of a major securities firm, when Drexel collapsed in 1990, it was weeks before customer accounts were transferred to a

new firm. The expeditious transfer of Lehman’s assets also avoids disruption of capital markets because securities transactions will continue to be completed and Lehman’s counterparties can confidently continue to do business with the firm."

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THE AUDITOR REPORT THAT THE FINANCIAL STATEMENTS ARE FREE FROM MATERIAL MISSTATEMENTS

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http://www.secinfo.com/d14D5a.t2zzz.htm  reports about "CDO Credit Default Swap Barclays Bank settlement CBOE Misstatement."  Quoting from the "Report of Independent Registered Public Accounting Firm To the Board of Directors and Shareholders of Barclays Bank PLC," the accountant’s state:

In our opinion, financial statements are free of $$ material misstatement."  PricewaterhouseCoopers LLP, Chartered Accountants and Registered Auditors, London, United Kingdom, 10th March 2008, proceed in the "Internal control" section of Barclays report they repeat the assertion that they provide " reasonable assurance against $$ material misstatement or loss."

In page 148 of Barclays, Annual Report 2007, the auditors use the same boiler plate stating that: "Internal control systems obtain reasonable assurance about whether the financial statements are free of material $$ misstatement ..."

IMPLYING THAT CDOs ARE SAFE ASSETS, AND UNDERSTATING THE TOXIC INHERENT RISK.

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In the "Corporate bonds" section they report states that "Corporate bonds are generally valued using observable quoted prices or recently executed transactions. Where observable price quotations are not available, the fair value is determined based on cash flow models where significant inputs may include yield curves, bond or single name credit default ^^ swap spreads."  They never mention any of the reports on the internet concerning the toxic paper, and the debate in the press.

in the "Derivatives" section the report talks some more about the swaps explaining that

"Derivative contracts can be exchange traded or over the counter (OTC). OTC derivative contracts include forward, ^^ swap and option contracts related to interest rates, bonds, foreign currencies, credit standing of reference entities, equity prices, fund levels, commodity prices or indices on these assets.

For many pricing models there is no material subjectivity because the methodologies employed do not necessitate significant judgment and the pricing inputs are observed from actively quoted markets, as is the case for generic interest rate ^^ swaps and option markets. In the case of more established derivative products, the pricing models used are widely accepted and used by the other market participants," implying that they are safe assets, and understating the toxic inherent risk.

^^ swap spreads - indicate discussion about swap spreads and CDOS

## CDO Super Senior - indicate discussion about swap spreads and CDOS

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EXPOSURES RELATING TO US SUB-PRIME WERE ACTIVELY MANAGED AND DECLINED OVER THE PERIOD REPORT CLAIMS

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The report proceeds to understate the risk of sub-prime toxic paper, stating that "Undrawn contractually committed facilities and guarantees provided includes £360m (2006: £nil) provision against undrawn facilities on ABS ## CDO Super Senior positions.

The US sub-prime driven market dislocation affected performance in the second half of 2007.  Exposures relating to US sub-prime were actively managed and declined over the period. Barclays Capital’s 2007 results reflected net losses related to the credit market turbulence of £1,635m, of which £795m was included in income, net of £658m gains arising from the fair valuation of notes issued by Barclays Capital. Impairment charges included £840m against ABS ## CDO Super Senior exposures, other credit market exposures and drawn leveraged finance underwriting positions.

Impairment charges and other credit provisions of £846m included £722m against ABS ## CDO Super Senior exposures, £60m from other credit market exposures and £58m relating to drawn leveraged finance underwriting positions. Other impairment charges on loans and advances amounted to a release of £7m (2006: £44m release) before impairment charges on available for sale assets of £13m (2006: £86m)."

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MISREPRESENT REALITY BY APPLYING "MONTE CARLO SIMULATION IS USED RATHER THAN ANALYTIC APPROXIMATION" WHERE TOTALLY UNREALISTIC ASSUMPTIONS CAN OVERSTATE THE PERFORMANCE AND

UNDERSTATE THE TOXIC PAPER TRUE RISK EXPOSURE

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http://msnmoney.brand.edgar-online.com/EFX_dll/EDGARpro.dll?FetchFilingHTML1?ID=5823375&SessionID=5RgcWZDBP11rCl9   in the "– Structured credit derivatives" section of the report states that:

"Collateralised synthetic obligations (CSOs) are structured credit derivatives which reference the loss profile of a portfolio of loans, debts or synthetic underlyings. The reference asset can be a corporate credit or an asset backed credit. For CSOs that reference corporate credits an analytical model is used. For CSOs on asset backed underlyings, due to the path dependent nature of a CSO on an amortizing portfolio a Monte Carlo simulation is used rather than analytic approximation. The expected loss probability for each reference credit in the portfolio is derived from the single name credit default ^^ swap spread curve and in addition, for ABS references, a prepayment rate assumption.

A simulation is then used to compute survival time which allows us to calculate the marginal loss over each payment period by reference to estimated recovery rates. Significant inputs include prepayment rates, cumulative default rates, and recovery rates."  Again, they misrepresent reality by applying "Monte Carlo simulation is used rather than analytic approximation" where totally unrealistic assumptions can overstate the performance and understate the toxic paper true risk exposure In the "Derivatives" section the report continues to confuse the issues and understate the risk by saying that "Derivative contracts can be exchange traded or over the counter (OTC). OTC derivative contracts include forward, ^^ swap and option contracts related to interest rates, bonds, foreign currencies, credit standing of reference entities, equity prices, fund levels, commodity prices or indices on these assets."

NET SALES/TOTAL ASSETS ESTIMATING 10% INFLATED NET SALES & RECEIVABLE, TO ENLARGE GROSS PROFIT & STOCK MARKET VALUES: A FBARCLAYS BANK PLC AND THE COMMERCIAL BANKS, NEC (COB) SIMULATED FRAUD SOFTWARE CASE STUDY

Overview

This study finds the ratios NET SALES/TOTAL ASSETS, SG & A/SALES and NET SALES/EMPLOYEES as the Best Predictor Percent Differenced Financial Ratios (BP%DFR) and most effective in constructing a fraud cost model. These BP%DFR variables are estimating 10% INFLATED NET SALES & RECEIVABLE, TO ENLARGE GROSS PROFIT & STOCK MARKET VALUES fraud value. We use these variables to build a regression model. This is a case study of fBARCLAYS BANK PLC (f=fictitiously simulated fraud, contrary to r=real) and the Commercial Banks, nec (COB). This may also apply to other companies and other industries. We decompose fraud values into Fixed Fraud (FF), Variable Fraud (VF), and their sum Mixed Fraud (MF), building a regression model for each component. Thus, we develop a Cost Function (CF) for each component, FFCF, VFCF, & MFCF.

We Simulated Fraud (SF) of the NET SALES, which is the primary (1st) account. We balance this 1^st account against the RECEIVABLES account, the 2^nd account. The FF remains fixed starting from the 1st period, 12/31/85, to the last period, 12/31/94, at $1,318,800. We have calculated the FF as the higher of 10% of the 1st primary SF account or 1% of Net Sales.  Add the FF to the VF and you get the MF for the last year $2,637,600.  Add this MF to the initial balance of the 1st account, the real account balance. This  rNET SALES balances $13,188,000, for the last period of this study. This way, you get the phony (f=fraudulent) balance of 1st account, fNET SALES, $15,825,600. Likewise, for the 2nd balancing account, start with the real balance, (r prefixed account), $115,356,000, combine the SF, $2,637,600, and you get the phony value (f prefixed) of the fRECEIVABLES balancing the SF at $117,993,600.

We difference (deduct) the fNET SALES (Fraudulent, Post SF) from the, rNET SALES (real, Pre SF), calculating the SF Dollar values. Likewise, we difference all the Disclosure™ financial ratios (%DFR), to identify the fraud drivers. We regress the SF Dollar Values on the %DFR, to discover the Best (highest R Square) Predictor Percent Differenced Financial Ratios (BP%DFR). In this case, these (BP%DFR) include: NET SALES/TOTAL ASSETS, SG & A/SALES, and NET SALES/EMPLOYEES.

INTRODUCTION OF PERCENTAGE DIFFERENCED FINANCIAL RATIOS (%DFR)

According to new securities legislation, auditors will need to report more quickly than at present any suspicion of fraud. A tougher approach is also coming from the auditing standards board of the AICPA. The new standard will require external auditors to be more aggressive in not only reporting but unearthing fraud and faulty financial statements. Failure to comply with the institute’s measure will include civil penalties and possible loss of a CPA’s license. These proposed standards will have auditors spotting higher risk of fraud to develop specific plans to eliminate that risk. The board has been considering stiffening auditing standards for several years as research showed that current standards were not tough enough on discovering major frauds (Berton, 1996).  Congress has also  considering a  bill would create a Financial Services Oversight Council made up of the Treasury secretary, the Federal Reserve chairman and heads of regulatory agencies to monitor the financial markets for potential threats to U.S. system. It would identify firms and activities that should be subject to heightened standards, including requirements that they place more money in reserve. Companies would have to plan for their own demise, detailing how they would be dismantled if they failed (Laws, 2009).

This study defines the %DFR as a predictor variable for estimating the value of fraud. We are developing several theories and applying other theories to design this study. The Reversed Accounting Theory (RAT) contends that to discover fraud we have to go from the final financial statements, in reverse, to the original fraudulent transaction. To pick the proper methodology, we are applying Activity Based Costing (ABC) to RAT and fraud, developing a derivation of ABC, Activity Based Fraud (ABF).  ABF contents that like any other cost, fraud originates from some activities. Since we do not know what these activities are, we could use highly correlated surrogates to these activities to act as fraud drivers. These %DFR are such drivers.

We intend to supplement existing Expert Systems (ES) software that can discriminate between fraudulent and fraud free companies, but cannot pinpoint the amounts and the accounts.  Integrating such a model into these ES, will extend their abilities beyond current technology. Such extension will enable the ES to quantify the fraud and flag its sources (accounts). The supplementary nature of model we develop explains its limitations. This model is not for discovering fraudulent companies, since it will be redundant to the existing ES. Thus, we optimize it for a relevant range that outside the vicinity of the origin, where both the %DFR and the Fraud approach a value of zero. Therefore, we hypothesize that the intercept will be statistically insignificant about the origin, which it turns out to be. In contrast to the insignificance of the Intercept, we hypothesize that the entire model will be significant, as well as at least one PERCENT. Indeed, the ANOVA (Analysis Of Variance) confirms our expectations, rejecting the null hypothesis that the model is insignificant and that the regression coefficients are equal to zero.

Since we intend to use it ultimately on a fraud suspect (a company the E has identified) we refer to that company as the Focus Company. In contrast, the industry or competitors’ ratio averages constitute a fraud free Peer Review Group (PRG). We calculate the percentage as the ratios of the Focus less the Peers divided by the Peers. These assumptions that fraud is the single difference between the Focus and the Peers highlight the limitations of this approach. These limitations exclude heterogeneous industries where companies are not very similar, as well as countries where fraud (e.g., bribes) is part of doing business, and GAAP/S (Generally Accepted Accounting Principles and Auditing Standards) are rare.

We search for the ratios that will maximize the correlation between the %DFR, the predictor variable(s) and the SF, the predicted variable.  We find that these ratios can help in auditing, detecting and deterring fraud, as well as help develop rules for the ES software for Analytical Review (ARES). We apply it to one company and one industry, but design it to be more generic, so we can expand it to other companies and industries.  This study simulates fraud (SF) in the financial statements. This Simulated Fraud (SF) combines Variable Fraud (VF) and Fixed Fraud (FF) into a Mixed Fraud (MF). We regress, correlate, & decompose such a MF into its VF & FF components. We compare accounts, ratios, periods & companies. We regress a Focus company with Simulated Frauds (SF) on its Peer Review Group (PRG) average (assumed to be without the same MF). We apply RAT to Expert System (ES) software and then test the hypothesis that RAT can decompose MF & help in flagging the MF's riskiest financial ratios. Eventually, the riskiest ratios will be part of the riskiest accounts. 

Applying RAT, we decompose the MF into its VF, & FF components. To decompose the MF, we regress it on sales. We define the FF as regression’s intercept, and the VF as the regression’s slope. Using the Least Square Regression we construct the MF function. We forecast sales as a function of serial date, testing this date as a surrogate for sales, whenever it is a good predictor of sales. We construct a statistically significant fraud model. This study calculates risk for Analytical Review Diagnostics Controls for Automated Personal Computer (PC) Expert Systems (ARES) based RAT. We integrate RAT, and the decomposition of this MF into the design of ARES, demonstrating some rules, screen, and reports that will result from applying these ideas. By scanning financial statements and their ratios, and regressing them against the peers averages the ARES could fire some rules. These rules will quantify the risk of the likelihood of over or under stating balances of accounts, helping auditors plan their audit. We could use such risk measures to allocate audit time to accounts.

Likewise, such fraud coefficients help the ARES prioritize its output and rate its opinions (related to problems in different accounts: phony sales ore receivable) from the most to the least risky accounts. Thus, the ARES could minimize over loading the user with reports that they cannot process. This way the users can limit the ARES to report only on the top most risky accounts and/or ratios, or pages of reports. This will relieve the users from evaluating hundreds of accounts and ratios. This is especially true when the users are not knowledgeable, do not have the resources to conduct such an evaluation, and cannot set apriority the materiality level. This could facilitate the deployment of ARES for inexperienced managers and auditors. Next, we would like to define the Simulated Fraud (SF) and some of its assumptions.

Activity Based Costing (ABC) and Cost & Management Accounting Theory (CMAT)

Applying Activity Based Costing (ABC) theories to fraud, we develop the Activity Based Fraud (ABF) theory.  ABF Treats fraud as a cost that we want to trace back to the activities that generate it, much like any cost.  Except that for fraud, that unlike traditional costs, which we simply want to minimize, we want to completely eradicate and eliminate, approximating a zero level fraud. To calculate the cost effectiveness of fraud controls, we have to estimate it (since criminals never report frauds voluntarily -- unlike other costs), decompose it, leading to the activity sources. For that purpose we deploy the Reversed Accounting Theory (RAT) to trace the fraud back to its sources as a step to eliminate such fraud. Our unique contribution is going in ‘reverse’ to traditional auditing. Instead of going from the individual transactions to the financial statements, we go in reverse, from the financial statements trying to reconstruct fraudulent entries. Hence, we develop the concept of RAT. Much like reversed engineering, RAT goes in reverse to the common sequence of accounting work. We test the hypothesis that RAT can construct a statistically significant fraud function.

We deploy the Cost & Management Accounting Theory (CMAT), and define VF, FF, & MF, as well as develop software integrity controls. CMAT suggests that the slope of Mixed Cost (MC) regressed on the sales denotes Variable Cost Rate (VCR). Likewise, the intercept of MC regressed on sales estimates the Fixed Cost (FC). Similarly, our Dollar SF regressed on the sales has a slope and an intercept. This slope shows the Variable Fraud Rate (VFR), which is analogous to the VCR. Likewise, the intercept (of the SF on sales) shows the FF, analogous to the FC. RAT contends that whenever we are dealing with unknown fraud. Decomposing this fraud (into its fixed and variable components) will be helpful in classifying its behavior and eventually pinpointing its sources.

FRAUD SIMULATION (FS) DEFINITION

fBARCLAYS BANK PLC NET SALES -  FRAUD SIMULATION DEFINITION                                                                                                                         

Variable Fraud (VF), Fixed Fraud (FF), & Mixed Fraud (MF) Definition   

The 10% Simulated Fraud (SF) of the NET SALES  primary (1st) account is balanced against the SF of the secondary (2nd) balancing RECEIVABLES account. The FF remains fixed starting from the 1st period, 12/31/85, to the last period, 12/31/94, at $1,318,800 amount. We have calculated dollar fraud amount as the higher of 10%  of the 1st primary SF account or 1% of Net Sales. Add the FF to the VF and you get the MF for the last year $2,637,600. Add this MF to the initial balance of the 1st account, real, rNET SALES balance $3,188,000, for the last period. You get the phony, fraudulent, balance of 1st account, fNET SALES, $15,825,600. Likewise, for the 2nd balancing account, start with the real balance, rRECEIVABLES, $115,356,000. Then, combine the SF, $2,637,600, and you get the phony balance of the fRECEIVABLES balancing that SF at $117,993,600. Our question is which ratio is this fraud’s top predictor?[1]

fNET SALES Fraud, Why Would Criminals Create It & What Predicts It?

The objective and the result of such a fraud may be the INFLATED NET SALES & RECEIVABLE, TO ENLARGE GROSS PROFIT & STOCK MARKET VALUES. However, this may not explain why a criminal would do it in a certain way. We could understand the possible purpose of such a Fraud by looking at the financial results. A fBARCLAYS BANK PLC executive working in the Commercial Banks industry (COB) may want to raise net income (reduce loss), credit ratings, commissions, or  promote his or her reputation. For example, a fraud that overstates the Sales balance, will in turn overstate the Net Income. Overstating Net Income will make the company appear to be more profitable. Such an overstatement of the Sales and profitability of the company may benefit employees who own stock options, and/or commission employees. An overstatement of Sales may easily translate to a pay raise, for certain employees. Misclassifying Long Term (LT) assets as current assets, can improve the credit rating of a company. However, such a fraud may not affect the income statement at all. We expect to find a ratio that can forecast such fraud.

10%  fNET SALES fraud: Account And Amount? A fraud perpetrator may prefer to misstate an account with a balance greater than zero. It is possible, that overstating an account with a positive balance may appear a bit easier to conceal, compared to an account with a zero balance. In the case of fBARCLAYS BANK PLC, and the Commercial Banks industry, the rNET SALES (r=Real) account, is more likely to have a positive balance, compared to other companies & industries. An account such as Cost of Goods will have a positive (debit) balance. Unlike some other accounts, such as Investment Gains/Losses, this is equally likely to have either balance, positive (debit), negative (credit). Likewise, it is most likely to have a zero balance in a non banking or finance environment. Once we have picked up an account, the next question is concerning the amount. The Criminal (the perpetrator of the fraud) may not know in advance what the normal balance of the account should be at all times. Therefore, a fraud that is a percentage of the real balance may appear safer (easier to conceal, automate, and harder to detect) choice. Likewise, small percentages such as 10%, as used in the present case, may appear to be safer than larger percentages. If the “criminals” got away with the fraud for one period, their greed may lead them to try it again and again. Therefore, a multi-period fraud is likely.

The Real Peer Co. rNET SALES (Source Account) & Its Phony (f) Partner[2]

The common criminal would make a fraudulent entry, where the Debit and the Credit do balance. Some accounting software may prevent unbalanced entries. Such entries may also be too obvious, and too easy to detect. Therefore, to supplement the present fraudulent entry, the Criminal should make a balancing entry in other accounts that normally balance the source account. Such an account could be the (Focus Co.) fRECEIVABLES. Such an account normally couples the source account, in this environment.  Furthermore, it is not as easily verifiable as the Cash account, for example. Since it is much easier to verify the balance of the Cash account and to detect phony balances, the Criminal may prefer to use accounts other than Cash, for the phony entries. Likewise, auditors are usually less likely to audit it as compared to the Cash account.  The Peers (fraud free) less the Focus Co. balance is the Fraud.

Reversed Accounting Theory (RAT) will help us pinpoint the source of the fraud and estimate its damages. This should help us detect it, if we can possibly classify it correctly (variable versus fixed). For example, if the fraud positively correlates with sales, or varies proportionately with the sales account, that gives us a clue as to which account actually contains this VF. Such a fraud most likely originates from sales itself, or one of its derivatives, such as sales commissions. In contrast, we can more easily eliminate fraud sources that typically do not vary with sales, such as fixed assets and depreciation frauds of all kinds.

LITERATURE REVIEW

Activity Base Cost / Fraud (ABC/F), Short and Long Runs, & Regression Analysis

The developments in activity-based costing (ABC) since the late 1980s have improved Accounting Information Systems. However, no one has ever applied ABC to Fraud, which is the focus of the current study. Applying ABC to Fraud produces Activity-Based Fraud (ABF). The literature covers other extensions of ABC to management, or Activity Based Management (ABM), but not to ABF. Hartnett and Lowry (1994) predict total cost for change in product mix. We are also trying to predict costs, except that we predict the cost of fraud damages as a function of fraud mix (VF, FF, & MF). Holmen (1995) suggests that ABC has primarily a long-run horizon. Therefore, we apply it to long-run fraud estimation problems, frauds that continue for 1 year, and usually much longer. Macintosh (1994) suggests that in the “scientific  ABC” method the designer uses multivariate regression analysis. 

Hartnett, Lowry, and Luther (1994) confirm that ABC supplies better approximations of long-run variable costs. This is one of the reasons that we are focusing strictly on long term frauds. Likewise, we suspect that ABF may also lead to similar results; it may supply better results in long run, rather than short run analysis.  Even if we detect no material fraud, just by estimating the fraud we may reduce its damages by acting as a deterrent. There are, however, obstacles to using ABF, much like ABC in reporting. Such obstacles include applying standards rather than actual values, subjectivity and verifiability in the choice of cost-drivers, and   ABC or ABF integration into the nominal ledger. We use the pre-fraud as surrogates of fraud free standards, normally based on competitors' financial statements. The problem is that selecting proper competitors is subjective and may vary greatly depending on the industry, economy, and other variables.

Pattison, Arendt, and Gavan (1994) implement a modified ABC system. We have modified ABC so much that we have to rename it ABF. It is too different from ABC, to retain the same name. Sheu and Wacker (1994) integrate time series forecasting, and ABC analysis. Similarly, we are doing the same thing. We also integrate time series into ABF. Datar and Gupta (1994) suggest that an ABC generates more accurate product costs than other systems. For a similar reason, we hope that ABF will generate more accurate fraud cost estimates than other systems. Groth and Kinney (1994) suggest activity-based costing (ABC) and cost driver analysis may reduce business risk, promoting value creation in a firm. Similarly, we contend that ABF and its cost drivers may help reduce fraud risk. In a counter sense, fraud cost management not implemented properly result in an intensified eradication of value. These reasons attest to the importance of studying fraud costs in value creation. This may ultimately lead to a cost benefit analysis of fraud eradication investment. 

ABF as A Control for Long Term Fraud & As a Function of Sales

Mak and Roush (1994) argue that ABC controls costs. Likewise, we feel that ABF controls. Under one proposal, they formulate flexible budgets for each activity using the cost driver for that activity. We are using sales or some of the financial ratios as the surrogate cost drivers of the fraud in order to develop an estimation method. In the future, we may try other cost drivers. Smith (1994) extends ABC to a management context and calls it: activity-based management (ABM). Similarly we extend ABC to ABF.   Our extension is a bit less radical in the sense that we can view fraud as a cost category, while management is a much broader concept than cost alone. We all agree that most of the emphasis in the literature, thus far, has been on costs (activity based costing) and quality (total quality management). We also consider other areas and factors. A time-based focus has a number of positive implications for the management accountant in designing improved management information systems. This ensures that decision-making is linked to an appropriate time horizon by matching short-run and long-run costs with decisions. For these reasons we focus on the long-run in the current model, reserving the short-run for future studies.

Literature of Fraud, Damages, Detection Expert Systems (ES) & Disclosure

A major problem in fraud detection is the lack of education on the part of those who must detect it (Kerwin, 1995). Some early warning signs may be found by parsing financial statements (Donlan, 1994). We combine a Simulated Fraud (SF) educational approach with parsing financial statements to find the best fraud predictor as warning signs in our ABF theory. Redirected Cash flows the wrong receivables or the wrong disbursement location is a common early warning sign of a cash fraud (Marks; Arnette, 1994). Employees commit the overwhelming majority (90.8%). Although at a much lower rate, executive fraud is only 26% (Campbell and Lindsay, 1994). Therefore, we our SF deals with an employee fraud, such as a cash fraud. The Financial Fraud Detection and Disclosure Act, requires exception reporting when control systems fail, such as material financial frauds (Campbell and Lindsay, 1994). Neural networks can help to find patterns and relationships, even obscure and nonlinear relationships (Stewart, 1994; Basch, 1994; Mayor, 1994). In our SF the relationships are fairly linear; therefore, we use linear regression. One way to combat management fraud involves Analytical Procedures (AP). Quantitative APs alone will not detect fraud; they simply signal the likelihood of a problem (Calderon, & Green, 1994). This is where our model comes in. Our model actually estimates the dollar damage of the fraud, after the AP signaled a high likelihood of a problem.

OVERVIEW & DEFINITION OF PROBLEMS

The problem is that we do not know how to eradicate and prevent fraud. In addition, we do not know even how to estimate fraud and how to trace its sources, which may be a prerequisite to prevention. Estimating fraud and tracing it back to its initial transaction is the focus of this study. To find the initial fraudulent transaction we developed the Reversed Accounting Theory (RAT). RAT states that, unlike ordinary accounting, where we start from individual transactions and conclude with summarized financial reports, the reversal may be more effective for fraud estimation and definition. Thus, RAT starts from the summarized financial reports, which may be conceal fraudulent transactions, concluding with estimating the value of the fraud and defining the fraudulent transaction. 

To define the culprit transaction, we use the PERCENTAGE Differenced Financial Ratios (%DFR), such as the Dollar Sales per Employee or Quick Ratio, instead of the original accounts, such as Cash or Inventory.  There are some advantages to using %DFR instead of using the original accounts. There are fewer %DFRs (dozens) compared to the original accounts (hundreds). The process of eliminating the irrelevant suspects is easier. The %DFR are much more standardized and uniform across times-series, companies and industries, than the original accounts themselves. The relative (Percentage) unit of measurement is more comparable than the original unit of measurement (inter or intra company or industry or currency comparisons of $ Sales per Employee to the Quick Ratio) 

Our accuracy of spotting and forecasting the existence of frauds is “extremely good” (Fanning et. al., 1995, Coates et. al., 1991). Likewise, we know what the red flags that indicate perpetrated frauds are (see: Fraud Red Flags Questionnaire, Pincus, 1987). This sounds sufficient to scare auditors about being held liable for not reporting the fraud (AICPA, 1988). Yet, such indications are much too vague to enable auditors, prosecutors, or managers to pinpoint the fraud. We still do not know how to estimate that fraud, what activity generates such frauds, and which transaction initiates such fraud. We think that if we can figure out the activity that generates the fraud, we should be closer to tracking the fraudulent transaction, and eventually the culprits themselves. For that we propose an Activity Based Fraud (ABF) theory. 

We view fraud as a cost item. Fraud is certainly not a revenue item, nor is it a liability, assets or an equity item.  Therefore, it is most similar to cost items, even in the sense that we are undoubtedly trying to minimize fraud (at least in theory). In fact, fraud may be the oldest cost, from biblical times, yet we do not estimate and account for it like any other cost. Fraud is currently the leading cause and cost of auditing litigation (Palmrose, 1991; St Pierre and Anderson, 1994). Auditors have changed the way they operate due to the increase of litigation (O’Malley, 1993; Elgin, 1992; Fuerman, 1992). Auditors charge escalating fees to fund such litigation risk.  

Based on Activity Based Costing (ABC) theories, every cost originates from some activity. Such activity is the cost driver, which are among the best cost predictors. Therefore, if we can treat fraud like any other cost, we may apply ABC to fraud. Like any other cost, certain activities produce fraud. Therefore, it only follows that like any other cost, every fraud may have its own drivers, which are its best predictors. We try to develop a method of identifying these drivers for specific types of fraud, using ABC methods. 

Unlike any other costs that are legal, fraud is illegal. Therefore, we cannot simply use traditional transaction-to-financial reports accounting. Thus, we deploy the RAT approach and combine it with ABC. From this, we create the Activity Base Fraud (ABF) theory. ABF states that if we can find the Best Predictor %DFR (BP%DFR), we will be further along the way of estimating the fraud, its drivers, and eventually it original culprits. Furthermore, by just deploying ABF, it may act as a deterrent to fraud. Just as we are using standards to cost the unknown overheads, we may eventually also use standards to cost the unknown frauds. 

We have some red flags that fire up whenever the likelihood of multi-year fraud rises. Others have clearly defined such flags (discussed above), so that not only an auditor could use it, but also a computer base Expert System (ES) could use it, while it is emulating an auditor. Our current objective is one step further, to estimate the amount of the fraud, and its original transaction or activity that created that transaction. For example, such an amount could be a billion dollars of overstated marketable securities over several years. Such was the case of unrecorded investment losses from futures trading at Diawa bank. Another scenario is phony sales debited to accounts receivable. A fraud like this was committed in the Equity Funding case where sales were in the names of the deceased.

For this purpose we have simulated multiple frauds (SF) on the financial statements of this company. The simulated Frauds (SF) include increasing net income over the last few years. Thus for example, we could have overstated sales, and understated all the expenses by 10% of their values. At the same time we have also entered a balancing entry, so that the debit and the credit remain balanced, and do not obviate the frauds.

We have then benchmarked the SF company against its own financial ratios prior to the SF. We use that to emulate how a real fraud ridden company may compare to its peers, which are not involved in the same type of frauds. This way we hope to identify a methodology of spotting red flags and patterns to detect and deter fraud.

COMPANY AND INDUSTRY LITERATURE REVIEW

OVERVIEW OF BANKING INDUSTRY

At the nation's 11,970 FDIC-insured institutions, total assets grew $84 billion from September 30 to December 31, compared to growth of $90 billion during the fourth quarter of 1994. Fourth-quarter asset growth was funded to a greater extent by deposits in 1995 than in 1994. Total deposits increased $96 billion during the fourth quarter of 1995, with most of this growth occurring in domestic deposits at commercial banks, which were up $91 billion. In 1994's fourth quarter, by comparison, total deposits increased $70 billion, including just $42 billion in domestic deposit growth (Helfer, 1995).

The reserve ratio of the Bank Insurance Fund (BIF) was 1.30 percent of insured deposits on December 31, down nominally from 1.31 percent on September 30 but still above the statutory minimum of 1.25 percent. The reserve ratio of the Savings Association Insurance Fund (SAIF) rose from 0.43 percent to 0.47 percent during the fourth quarter but remains far below the target level of 1.25 percent. As a result of the BIF's full capitalization, the FDIC was able to reduce BIF assessment rates twice in the latter half of 1995. The average BIF assessment rate fell from 23.3 cents per $100 of assessable deposits to just 0.4 cents (effective January 1, 1996), improving the attractiveness of BIF-assessable deposits relative to other funding alternatives. Because SAIF assessment rates cannot be lowered significantly until the fund is fully capitalized, the average assessment rate for SAIF members remains at 23.7 cents per $100. (1) This premium disparity between the BIF and the SAIF may partially explain deposit growth patterns in 1995. Deposits assessable by the BIF grew $110 billion (4.6 percent) during 1995, with $83 billion of the increase coming in the fourth quarter. Deposits assessable by the SAIF grew $20 billion (2.8 percent) in 1995 but decreased, by less than $1 billion, in the fourth quarter. (2) Other factors, such as loan demand, also may affect deposit growth patterns (Helfer, 1995).

BARCLAYS BANK COMPANY PROFILE

Group profit before tax improved by 1,198m as a result of a decline in provisions for bad and doubtful debts. Of this reduction, 856m occurred in the United Kingdom, where new gross specific provisions were 791m, and 309m in the United States, where new gross specific provisions were 97m. Although falling significantly, non-performing lending remained at a high level, particularly in regard to the UK property, construction, hotel and leisure sectors.  As a consequence of work that is being undertaken to improve the assessment of credit and credit losses throughout the business, the general allowance to cover unidentified bad debts has been increased by 74m to 850m (Barclays (b), 1994).

Profit before tax showed a significant improvement over the two previous years as budget debt provisions fell, helped by releases in the United Kingdom and the rest of Europe. The reduction in provisions was achieved despite one large corporate provision of 65m in Europe and further provisions in Canada (Barclays (b), 1994).

FUTURE OF BARCLAYS BANK COMPANY

The Trust Company profit for the year was adversely affected by an ongoing re-organization process, the result of which will be to improve efficiency and customer service in the future. Most of the Group's trading activities are customer oriented. In anticipation of future customer demand, the Group maintains access to market liquidity by quoting bid and offer prices with other market makers and carries an inventory of capital market instruments including a variety of derivative and non-derivative (or 'cash') financial instruments. Positions are also taken in the interest rate, foreign exchange, debt, equity and commodity markets based on expectations of future market conditions. These activities constitute the proprietary trading business of the Group. Given the relationships between instruments and markets, trading strategies depend on both market-making and proprietary positions and are managed in concern in order to maximize trading related revenue. Trading positions and any offsetting hedges are established as appropriate to accommodate customer or Group requirements (Barclays (a), 1994).

FUTURE OF BANKING INDUSTRY

Five Paces, Inc. has developed the next step in banking technology: Virtual Bank Manager (VBM). VBM is a software solution that allows financial institutions to conduct secure on-line transactions over the Internet. It is the first module within Virtual Financial Manager.  We're developing additional modules that can be incorporated within Virtual Financial Manager or, like Virtual Bank Manager, can be used as a stand-alone solution. VBM will allow anyone anywhere with a PC easy access to full-service, on-line Internet banking. (Five).

METHODS AND PROCEDURES, DATA ANALYSIS & INTERPRETATION

DATA DEFINITION, INPUT, & OUTPUT OF REGRESSION ANALYSIS THEORIES

Differenced Financial Ratios (DFR) Measure Fraud Impact & Identify Fraud Insensitive Ratios

We down load the company Profile, Annual Report, & Financial ratios from the Disclosure database and then upload them into our Fraud Simulator spreadsheet Software Program (The Program). The Program recalculates all these Financial Ratios verifying their values and extending their decimal point. Then the program applies the Simulated Fraud (SF) to the recalculated and verified Annual Report, producing a second set of phony fraudulent fAnnual Reports. The Program inputs the fAnnual Reports to the financial ratio calculation and produces a second set of phony fraudulent financial ratios. The difference between the 1st real financial ratios and the second fraudulent financial ratio produces the Difference Financial Ratios (DFR). Such DFR measures the effects of a fraud on that particular ratio. If the DFR equals zero, then we can conclude that a particular fraud does not affect it. Likewise, if the DFR of another ratio also equals zero, then we can conclude that like the 1st ratio, the same fraud does not affect the second ratio.  Thus, both ratios are insensitive to this fraud. The DFR is only comparable when it is zero. When it is different than zero, we cannot use it effectively for comparisons.

DFR Inability To Compare Fraud Impact & Identify A Given Fraud Most Sensitive Ratios

These DFR denominate their original values and therefore are hardly comparable. For example, the Net Sales Per Employee (NSPE) DFR can describe the fraudulent increase in sales per employee due to the fraud. Likewise, the DFR Quick Ratio (QR) describes the change in liquidity due to the fraud. However, the absolute value of the NSPE DFR will always exceed its QR counterpart, since NSPE it measures Sales per employee. Thus, we cannot tell which one is more sensitive to this particular fraud, just by comparing their DFR values. Since one of our objectives is to find the ratio that is most sensitive to this kind of fraud, we have to develop a more relative measure. Such a measure will compare the sensitivity of different ratios to different fraud simply based on its magnitude.

Relative %DFR Comparison Fraud Impact & Identify Most Sensitive Ratios

We define the relative %DFR as the DFR divided by the real Financial Ratio. This way we can decide which ratio is more sensitive to a given fraud, even though these ratios denominate completely different units of measurement. While the NSPE DFR and the QR DFR are clearly not comparable, their counter parts, the NSPE %DFR and the QR %DFR are clearly comparable. Comparing their magnitudes, describe their sensitivity to the fraud. Typically, the ratio with the highest %DFR will also be the most sensitive to that particular type of fraud. This way we can construct a Financial Ratio Fraud Sensitivity Theory (FRFST) and use the %DFR to test our theory. 

Financial Ratio Fraud Sensitivity Theory (FRFST) Tested By DFR, %DFR, Or Both

We can hypothesize that a fraud that overstates (credits) Credit Sales, balancing it by overstating (debiting) fixed assets, should affect the NSPE ratio more than the QR. To test such theory, we can simply use the DFR value of both ratios. Since this fraud will not affect any current assets or any current liabilities, its QR DFR must be zero, while its NSPE DFR will be larger than zero, and therefore more sensitive. But, what if The Fraud balances the Credit Sales by overstating (debiting) Accounts Receivable, which is a much more likely fraud. This way one of the DFRs will no longer remain zero, and will no longer be as comparable as the former example. In this case, we can call %DFR to the rescue, using it to test our hypothesis and further construct while validating our FRFST. Can we therefore stop using DFR and simply replace it by %DFR? Not necessarily so, some time DFR may be necessary, since we cannot calculate %DFR.

Fraud Damage Cost Estimating Software Program (FDCESP) & DFR/%DFR 2 Stages

Due to the difficulty that computers have in calculating divisions by zero, it is sometimes necessary to initially use a measure like DFR. In developing a Fraud Damage Cost Estimating Software Program (FDCESP) we have to be considering such issues. Since we want to extended current ES technology with such a FDCESP system, we have to consider machine difficulties. Furthermore, since we typically want to minimize computer resources and maximize efficiency, we would use DFR as well as %DFR. We have to calculate DFR before we can calculate %DFR. Therefore, we will also use it in our FDCESP development as a 2 stage process. The Program will first calculate and use DFR, if it can choose the most sensitive ratio.  If it can decide at this stage, it can then store its decisions in a Case Based Reasoning Knowledge Base (CBEKB). The program needs to go no further. It does not need to calculate %DFR. This may be a case where the DFR will be zero, anyway. Therefore, we do not need to calculate %DFR for a decision.  Furthermore, the attempt to calculate it may result in a Division By Zero error, creating all kinds of problems.

Case Based Reasoning Knowledge Base (CBEKB), Expert System (ES) & FDCESP

In this context, a Case Based Reasoning Knowledge Base (CBEKB) is a data base of fraud cases and its related decisions. Such CBEKB can be a part of an Expert System (ES) that issues a Fraud Detection, Damage Estimation, Investigation, & Prevention opinions. Such opinions will rely on a Cost Benefit Analysis comparing the benefits of fraud damage reduction to its costs. This is where Fraud Damage Cost Estimating Software Program (FDCESP) integration fits.  For a system like this to work, identifying a few best predictors of fraud value could be very useful. These variables will be the Best Predictors PERCENTAGE % Differenced Financial Ratio (BP%DFR).

Best Predictors Percentage Differenced Financial Ratio (BP%DFR) Flags Fraud Patterns

Best Predictors Percentage Differenced Financial Ratio (BP%DFR) variables will estimate the dollar (or other currency) value of damages. Such analysis should be done when existing ES predict that the financial statements do contain fraud, and the main question is which kind of fraud is it? Is it a Credit Sales and Fixed Assets fraud? Is it a Credit Sales and Accounts Receivable fraud? This is where various BP%DFR can red flag the various types of frauds that may be present. As we can reasonably expect, different ratios will become BP%DFR for different types of fraud. For example, if the fraud pattern appears to have fixed values over time, then it may be a Fixed Fraud (FF). A FF is more likely to origin in fixed assets accounts, rather than in a Sales Commission account. In contrast to FF, a Variable Fraud (VF) is more likely to originate in a Sales Commission account or may be in a Direct Labor Cost type of account. Likewise, a Mixed Fraud (MF) may be split over both types of accounts, etc. Furthermore, this BP%DFR will estimate the total dollar amount of the fraud telling us how many resources will it justify, and most importantly which accounts are the culprits.

Regressing Fraud Values On Best Predictors % Differenced Financial Ratio (BP%DFR)

Regressing Fraud Values On Best Predictors Percentage % Differenced Financial Ratio (BP%DFR) will help build a model to estimating Fraud Values. To set up the data for such a regression analysis, we split the data into 2 parts. The first part constitutes the pre-fraud condition. Naturally, for these observations, the independent predictor variables, the BP%DFR, as well as the dependent predicted variable, the Fraud Values, are both zeros. In an experimental simulation, that is certainly true. This is saying that if no fraud what so ever exists, then the pre-fraud and the post-fraud ratios will be the same. Thus, the BP%DFR, as well as all other differenced ratios, must equal zero. In contrast, in the real world, if no fraud what so ever exists, then the Peer Review Company, or Companies and Industry Averages (like pre-fraud, free fraud) and the Focus Company (like the post-fraud, fraud ridden) ratios will not necessarily be the same. However, they may approximate identity, if the peers are almost identical to the focus company. Although, identity between a focus company and its peers is very unrealistic condition, there are ways to approximate such condition, by statistical and mathematical means. Such methods are beyond the scope of the present study, however, we will discuss them briefly later, and in future studies. The second part of these observations will typically be different than zero. These 2 parts will make up 2 parallel time series. One time series without fraud, and the second with fraud. Using these 2 sets of time series in parallel, makes this analysis a cross sectional and time series regression analysis.

DATA INPUT FOR THE REGRESSION ANALYSIS: ITS EMPIRICAL ASPECTS

NET SALES/TOTAL ASSETS top BARCLAYS BANK PLC PREDICTOR RATIO[3]

For the top predictor, X1 BP%DFR, X1=NET SALES/TOTAL ASSETS, we compute the 2nd part of the positive fraud observations, as follows. Following are steps that calculate some of the predictor variables' values, such as X1, X2, etc.  We start with the first variable, X1. The fraudulent, phony, fNET SALES/TOTAL ASSETS has a value of 0.13049. In contrast, the real, rNET SALES/TOTAL ASSETS, has a value of 0.10669. To difference, one needs to deduct the fraudulent from the real balance. The difference can be the gain in NET SALES/TOTAL ASSETS of 22.307620%.  This percentage Loss (-) or Gain (+) in the ratio due to the fraud is the value of X1, the 1ST BP%DFR independent predictor variable. Applying the same process to SG & A/SALES, & NET SALES/EMPLOYEES, you calculate X2=SG & A/SALES and X3=NET SALES/EMPLOYEES, the additional BP%DFRs. Calculate the dependent predicted variable, Y=NET SALES, as the Mixed Fraud (MF), sum of Fixed Fraud (FF) and Variable Fraud (VF), and you are ready to regress it on the BP%DFRs.

The MF Cost Function (MFCF) is calculated by regressing VF, FF, & MF on Net Sales. Start with VF.  We classify such a VF Cost Function (VFCF) as a case of Pure VC definition. Continue with FF. The FF regression has an Intercept of $1,318,800.00 and a Slope of 0.00%. We classify such a FF Cost Function (FFCF) as a Pure FF definition. Proceed with MF. The MF regression has an R-Square of 1, an Intercept of $1,318,800.00 and a Slope of 10.00%. We classify this MF Cost Function (MFCF) as a Pure MF definition. Now, instead of regressing the sum, MF=VF+FF, try adding the Intercepts and Slopes of the separate VF and FF regression, producing a 2nd redundant, but independently calculated Intercept and Slope. Deduct the independently calculated Intercepts and Slope.

Theoretically, for a Pure VF definition, the Intercept should be a perfect zero, but that may change due to rounding errors. A Gross VF definition may cause an error, if we define the VF as a percentage of a primary account (not perfectly correlated with Net Sales). Such an account may be Long Term Assets. The FF should be the exact complement (mirror image) of the VF, a slope of zero, and an Intercept that is positive. At the same time, the VF should have the opposite values, zero intercept and a positive Slope. Their sum, MF, should have both positive Intercept and Slope. Likewise, we made MFCF by combining the separately regressed VF & FF. These 2 functions (VF & FF) should have the same 2 regression parameters (intercept and slope).  Much like the single MFCS regression, it should also have an intercept and slope. These redundant calculation controls demonstrate how to promote software integrity. Classify and decomposing fraud into its parts will help detect, measure, and ultimately minimize fraud damages. A pure version of the definition may be more effective than the Gross version, but any one is better than none at all.

The R-Square shows the statistical quality of this model, which we will discuss later. Such MFCF can estimate fraud prevention benefits. It traps error by comparing actual and expected results. Thus, the growth rate & variance of FF should be zero. If it indeed is zero, then we may have avoided this error. In this case, these error values are equal to 0.00%. Even if it is not zero, it may be due to an immaterial error such as a rounding error, or an error in the Disclosure data base, that is beyond the scope of this study.[4]

Expert Systems (ES) exemplify regressions' rules. The ES can regress the estimated fraud values (estimated by our model) on the NET SALES account of the Focus company. If the regression is significant, then the ES may reach some tentative conclusions, about the possibility of fraud. This can be true, if it turns out to be a case of a Pure definition of VF, FF, & MF. The ES further reinforces its conclusion that fraud may exist, estimating its cost function structure, dollar estimates, and probabilities. More analysis needs to be used to further reinforce such suspicions, and eventually come up with an explanation to the fraud. ES show that explanations to the Variable Fraud (VF) can include: 10% INFLATED NET SALES & RECEIVABLE, TO INCREASE GROSS PROFIT & STOCK MARKET VALUES.  Our methods may help ES calculate risks and correctly interpret them. Are the ratios correct?  Is it Fraud or is it not fraud? That is the question. This ES tests itself, using a value and sign test as an Internal Controls Of Integrity & Reliability.[5]

SUMMARY OUTPUT FOR BARCLAYS BANK PLC[6]

The Regression Statistics sub-analysis contains the 3 parts. The 1st part is the Multiple R, 0.999845. The 2nd part is the lower R-Square, R**2, value of 0.99969. The 3rd part, following the R-Square is the even lower ARS (Adjusted R-Square) of 0.999632. Multiple R is the correlation between the predicted and the actual values of the focus company, BARCLAYS BANK PLC, the coefficient of correlation. These correlate the actual and the forecasted values of this Simulated Fraud (SM).

Bank Expert System Rules[7]

This regression analysis helps Expert Systems (ES) form patterns of rules for fraud classification. The ES will apply such rules to a Suspicious Bank, to classify its fraud. We will form the ES rule in a step by step fashion as we discuss the output. 0.999632. Following is the 1^st component of this rule, dealing with R-Square:

For the Regression of the Differenced Account On The Differenced Financial Ratios:

If the Industry is equal to This Industry and the Suspicious Company R-Square is Equal To

Less than the upper bound of R**2: 0.99969, And

Greater Than the Lower bound of ARS: 0.999632 , And è

In practice this will be one large rule, for simplicity we break it into its components. Arrow heads indicate è  that the rule continues later. A Knowledge Base System (KBS) will store these rules.

Multiple R (R) -- Internal Software Control, The Sum Of Square (SS) & Beta Values

If the Multiple R is equal to 1, then a change in the value of X, the independent predictor variable, the BP%DFR predicts the dependent predicted variable, the focus company, BARCLAYS BANK PLC BP%DFR, perfectly well. Otherwise, Multiple R is different from 1, and the prediction is less than perfect. The present Multiple R of 0.999845 tells us that there is relatively strong linear relationship. The independent variables (the BP%DFR of Barclays and the COB industry) estimate the Simulated Fraud (SF) very well. This means that if the BP%DFR increases, then the Simulated Fraud (SM) is also very likely to increase as well, in this sample of data. This value of R tells us two things. If this is statistically significant, it may apply to other samples.

R-Square: Variance In The Simulated Fraud Explained By The Financial Ratios

Unfortunately, we cannot interpret R in more precise terms, since it is not equal to 1. The R-Square, the Coefficient of Determination, may help us determine the meaning of the relationship of the population more precisely. Expert Systems (ES) would typically apply such relationship to the population of cases.

The Multiple R (R), 0.999845 squared is equal to, R-Square, 0.99969. The R-Square should be lower than R, because we square a fraction. If R-Square is equal or larger than R, we most likely have an error. The R-Square is the Coefficient of determination. The R-Square shows how the Independent variable determines the Dependent variable. The Independent (Predictor) Variables are some ratios, the BP%DFR. In contrast, the Simulate Fraud (SF) is the Dependent (Predicted) Variable. R shows that BP%DFR predicts the Simulated Fraud (in the focus company) very well, close enough to 100%. This way ES estimate fraud damage as a function of such BP%DFR, for a cost benefit analysis.

The Adjusted R Square: A Function Of The R And The Number Of Observations

The Adjusted R-Square (ARS), 0.999632  is lower than both the Multiple R and the R-Square.[8] We adjust the ARS for the degrees of freedom (df). The number of Observations, 20 , determines the df. This ARS tempers the strength of the R, which may be unrealistically high, especially if we have a small number of observations. To avoid overstating the strength of R-Square, we calculate the Adjusted R-Square. This difference between the R-Square (RS) and ARS will shrink as we increase the number of observations. We split these Observations into 2 parts. The 1st half (10 Observations) includes Zero values of both the Predictor Financial Ratios, BP%DFR, and the Predicted Simulated Frauds. These zero values represent no difference in financial ratios in the absence of fraud. Thus, the difference between the Fraudulent and the Real Financial Ratios, as well as the Simulated Fraud itself is all equal to zero. The second half shows the differences in the Financial Ratios that explain the Simulated Fraud. In contrast to the origin values of zero, these later 10 observations will usually differ from zero. Otherwise, we may not have simulated a fraud; we know this by definition and design. 

ANOVA: ANALYSIS OF VARIANCE: Testing The Utility of the Model  

The difference between the R-Square (RS) and ARS will shrink as we increase the number of observations.  If such differences exist, then, the ANOVA portion will explain them. Specifically, the SSE (Sum of Squared [SS, column] Error or Residual [row]), that is much greater than zero, explain some such differences. In our case, the value of SS Residual is 1.1E+10, which is very small and very close to zero, explaining the proximity of RS & ARS. If RS & ARS are much further apart, the regression model may not be as reliable (compared to close RS & ARS).

The ANOVA helps us evaluate the utility of the entire model. It tests individual coefficients allowing us to conclude that a linear relationship between the independent and the dependent variables exists. The ANOVA decomposes the variability of the dependent variable, The Emulate Fraud, measured by SS Total, 3.55E+13  into its components, the Regression SS, 3.55E+13, and the Residual part, 1.1E+10. If the SSR (Regression) is large relatively to the SSE (Residual), the RS is high -- signifying a good fit and a good model, as is our present case. A value of F, 17225.41, shows the significance of the model. This F value is statistically significant at the .05 level. This means that the BP%DFR, Xs independent variables, explain the variation in Y dependent variable, the Simulated Fraud  Dollar amount for BARCLAYS BANK PLC. Therefore, the model is useful for estimating the cost for this kind of fraud damages.[9]

ANOVA PREDICTORS-TEST OF THE INTERCEPT CONSTANT AND THE BETA SLOPE(S)

The test of the Intercept and/or individual Beta coefficients allows us to decide about the linearity assumption. We decide whether a linear relationship exists between the Independent Predictor Financial Ratios and their Predicted Dependent Variable, the Simulated Fraud in the focus company, BARCLAYS BANK PLC. Consequently, we perform the t-test for the Intercept constant, -13.1994, and the Predictor Beta value for  BARCLAYS BANK PLC is -1E+07. Our test criterion is the P-value.

To evaluate the statistical significance of the Intercept Coefficient, -13.1994, we compare it to its Standard Error. The Standard Err column, 8288.838, for the Intercept, along with the relatively small differences between the t-Stat and the P-value columns, -0.00159  and 0.998749, confirm our notion from the theory. We cannot reject the null hypothesis, since this P-value, of 0.998749, is greater or equal to .05. Thus, we cannot reject the null hypothesis, that the Intercept value is zero, concluding that it is zero, when the value of the other variables is also zero. Namely, in the absence of any differences among the financial ratios the model assumes no fraud. Therefore, the Focus and the Peer companies (Differenced Financial Ratios equal zero), at the Origin, the forecasted fraud (Intercept) must also equal zero. However, if we are farther away from the origin, the Intercept can be used together with the other variables, and then the entire model is statistically significant at the .05 level, as the ANOVA demonstrated. Thus, the origin and its vicinity are outside our relevant range. Because, when we apply this model, we know that the fraud is greater than zero.

Expert Systems (ES) Form Patterns Of Rules For ANOVA & Intercept’s Upper & Lower Bounds

Using the ANOVA bounds of the Intercept, the ES can continue forming the rules as follows:

èIf The ANOVA is statistically significant at the 5% level, & The Suspicious Company’s Intercept is:

Less Than Intercept Coefficient Upper 95% Bound Of: 17558.35, And

Greater Than Intercept Coefficient Lower 95% Bound Of:-17584.7, And ==>

Expert Systems (ES) Form Patterns Of Rules For Predictor (Independent) Variables: The

X1-3 Coefficients Or Slopes, & Their 95% Upper And Lower Confidence Interval (CI) Bounds

We will plug the X1-3 Coefficients Or Slopes Of The Variables in the final regression equation. To complete the regression equation we need also the residual. Therefore, we will do that in the after the residual output analysis. In the mean time we can complete the Fraud Pattern ES rule, using the 95% Upper And Lower Bounds of this variable together with the R-Square and the Intercept, as follows:

=> the 1^st Best Predictor is: X1=NET SALES/TOTAL ASSETS , And

its Coefficient (Slope) value is Greater Than 95% lower Bound of: -1.5E+07;

The 2^nd Best Predictor is: X2=SG & A/SALES . (same as above but for X2 Bounds), And

The 3^rd Best  Predictor is X3=NET SALES/EMPLOYEES

Then it is most likely due to a: 10% inflated net sales and receivables to increase gross profit and stock market value type of perpetrated fraud.

At this point, the ES will also fire an explanation as to support its decision.[10] To complete the calculation of the estimated fraud, we need to include the Residual value, in addition to the values of the financial ratios. Thus, we will replace the X symbols by their values, and add the residual to the sum, that should produce the Simulated Fraud in a given year. In contrast to the insignificant intercept, the slopes are all significant. Thus, we reject the null hypothesis that the slopes are all equal to zero. We can plug our parameters into the model after we figure out the residual value.

Conclusions

This study develops a “finger print” definition for a specific class of frauds, with a specific rate, for the banking industry. The regression parameters define parts of the fraud finger print. Some of these parameters include such variables as the upper and lower bounds of the coefficients of the best fraud predictor variables, and its statistical significance. This finger prints define Expert System (ES) rules for a Case Based Reasoning (CBR) Knowledge Base (KB). The CBRKB should eventually contain all the possible combinations of fraud finger prints for the banking industry. We also define the fraud rate of over or under statements of financial accounts, and its time periods, as well as its amounts and their pattern, since they may affect the fraud finger print. Such finger prints together with other fraud characteristics should help an ES diagnose fraud patterns by benchmarking a suspicious bank against its banking peers.  The resulting anomalies will fire the ES rules that will help trace the source of the bank fraud.

It is evident that past market meltdowns could not rely on financial reports.  The problem is the failure of financial reports to report a crucial fact: a company’s actual financial condition.  Ultimately, we intend to supplement existing Expert Systems (ES) software that can discriminate between fraudulent and fraud free banks.  Further research needs to create and test systems that will be able to flag and pinpoint the sources, accounts, and amounts of bank frauds.  This should be done in a preventive nature before financial firms are a threat to the system.

REFERENCES

REFERENCE WEB SITES AND QUOTES

Oliver Biggadike and Shannon D. Harrington, on Sept. 8 (Bloomberg) "Investors may be forced to settle contracts protecting more than $1.4 trillion of Fannie Mae and Freddie Mac bonds against default" and  Oct. 10 (Bloomberg) 

http://www.bloomberg.com/apps/news?pid=20601087&sid=aa6nmsv7BakE&refer=home  "Lehman Credit-Swap Auction Sets Payout of 91.38 Cents"

http://www.secinfo.com/d14D5a.t2zzz.htm  "CDO Credit Default Swap Barclays Bank settlement CBOE Misstatement." 

http://lawprofessors.typepad.com/securities/news_stories/index.html , August 23, 2007 “Rumors of a Sale of Bear Stearns"

http://msnmoney.brand.edgaronline.com/EFX_dll/EDGARpro.dll?FetchFilingHTML1?ID=5823375&SessionID=5RgcWZDBP11rCl9  "Structured credit derivatives"  

Matthew Monks, September 9, 2008, (mmonks@financialweek.com fw_editor@financialweek.com)

http://www.financialweek.com/apps/pbcs.dll/article?AID=/20080909/REG/809099979/1036  “Mortgage bailout could trigger massive credit default swap settlement Payouts"

PricewaterhouseCoopers LLP, Chartered Accountants and Registered Auditors, London "Report of Independent Registered Public Accounting Firm To the Board of Directors and Shareholders of Barclays Bank PLC

Peter Viles, peter.viles@latimes.com, May 2007, http://latimesblogs.latimes.com/laland/mortgages/  "Blame game: KPMG accused of lax auditing of New Century."

REFERENCES ON FRAUD, DAMAGES, DETECTION EXPERT SYSTEMS (EI) & DISCLOSURE

American Institute of Certified Public Accountants (AICPA), “The Auditor’s responsibility to detect and report errors and irregularities”, STATEMENT ON AUDITING STANDARDS NO. 53, auditing standard board, New York, 1988.

Berton, Lee, “Auditors Face Stiffer Rules for Finding, Reporting Fraud at Client Companies”,

WALL STREET JOURNAL, February 5, 1996, A2.

Coates, P., and Fant, L., “A neural network approach to forecasting financial distress”, THE JOURNAL OF BUSINESS FORECASTING, Winter 1991-2, 9-12.

Fanning, Kurt; Cogger, O. Kenneth;  Srivastave, Rajendra “Detection of Management Fraud: A Neural Network Approach” INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE AND MANAGEMENT  Vol. 4:113-126 (1995)

Fuerman, R.,” The accounting profession’s litigation crisis”, OHIO CPA JOURNAL, October 1992, 39-40.

Elgin, P., “Huge liability judgments pressure CPAs to raise prices”, CORPORATE CASHFLOW, 13, July 1992, 12-14.

O’Mally, S., “Legal liability is having a chilling effect on the auditor’s role”, ACCOUNTING HORIZONS, 72, June 1993, 82-7.

Palmrose, Z., “Trials of legal disputes involving independent auditors: some empirical evidence”, JOURNAL OF ACCOUNTING RESEARCH, 21, Supplement, 1991, 149-185.

Pincus, K., “The efficacy of red flag questionnaire for assessing the possibility of fraud”, ACCOUNTING ORGANIZATIONS AND SOCIETY, 14, 1987, 153-63

St Pierre, K. and Anderson, J., “An analysis of the factors associated with lawsuits against public accountants”, THE ACCOUNTING REVIEW, 59, 2, 1984, 242-63.

References on Activity Base Cost / Fraud (ABC/F), Short and Long Runs, & Regression

Datar, Srikant; Gupta, Mahendra, “Aggregation, specification and measurement errors in

 product costing”,   Accounting Review , Vol: 69  Iss: 4 ,  Oct 1994,   567-591.

Groth, John C; Kinney, Michael R, “Cost management and value creation”, Management Decision, Vol: 32, Iss: 4, 1994  p: 52-57.

Hartnett, Neil; Lowry, John, “From ABC to ABM”, Australian Accountant, Vol: 64  Iss: 2 , Mar 1994  p: 28-32

Hartnett, Neil; Lowry, John; Luther, Robert, “Is ABC feasible for external reporting?”  Accountancy, Vol: 113 Iss: 1209, May 1994,  p: 74

Holmen, Jay S, “ABC vs. TOC: It's a matter of time”, Management Accounting, Vol: 76  Iss: 7,  Jan 1995  p: 37-40

Macintosh, Norman B, “Management accounting's dark side: Part I”, CA Magazine, Vol: 127  Iss: 7  Sep 1994,  p: 40-45

Mak, Y T;  Roush, Melvin L, “Flexible budgeting and variance analysis in an activity-based costing environment”,  Accounting Horizons , Vol: 8  Iss: 2, Jun 1994,  p: 93-103

Pattison, Diane D; Arendt, Carrie Gavan, “Activity-based costing: It doesn't work all the time”,

Management Accounting, Vol: 75  Iss: 10, Apr 1994,  p: 55-61.

Sheu, Chwen; Wacker, John G, “A planning and control framework for non-profit humanitarian organizations”, International Journal of Operations & Production Management, Vol: 14 Iss: 4, 1994 p: 64-78

Smith, Malcolm, “Bottleneck management”, Management Accounting-London, Vol: 73  Iss: 3  Mar 1995  p: 26-28.

Industry & Company References (Historical Perspective)

REFERENCES OF BARCLAYS AND BANKING INDUSTRY

Barclays(a), “President’s letter”, 10-K Annual Report, 12/31/94

Barclays(b), “Management discussion”, 10-K Annual Report, 12/31/94

Five Paces Inc. Home Page

Helfer, Ricki, The FDIC Quarterly Banking Profile, 12/31/95

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[1] We simulate a 10% inflated based fraud

[2] We use a real peer compared to a simulated phony partner

[3] This is a simulation predictor ratio

[4] This is an estimate of potential fraud prevention benefits

[5] Further studies need to test the reliability of the Expert System

[6] The regression analysis is between the actual and simulated fraud data

[7] In this case the expert system uses real and simulated data

[8] Actual Financial Ratios and Simulated Ratios were used

[9] The model needs to be tested over extended time

[10] Experts will continually add their expertise to the system via the Web.