000			03856cam a2200361 a 4500
001			2008053433
003			DLC
005			20151013135758.0
008			081219s2009 njua b 001 0 eng
010			_a 2008053433
015			_aGBA928586 _2bnb
016	7		_a014932769 _2Uk
020			_a9780470392430 (cloth)
020			_a0470392436 (cloth)
035			_a(OCoLC)ocn246887310
040			_aDLC _cDLC _dBTCTA _dYDXCP _dC#P _dUKM _dCDX _dBWX _dKSU _dOMB _dBDX _dDLC
050	0	0	_aHV8079.F7 _bC62 2009
082	0	0	_a364.4 _222
100	1		_aCoderre, David G.
245	1	0	_aComputer-aided fraud prevention and detection : _ba step-by-step guide / _cDavid Coderre.
260			_aHoboken, N.J. : _bJohn Wiley & Sons, _cc2009.
300			_axviii, 280 p. : _bill. ; _c24 cm. + _e1 CD-ROM (4 3/4 in.)
504			_aIncludes bibliographical references (p. 275-276) and index.
505	0		_aWhat is fraud? : Fraud: a definition ; Why fraud happens ; Who is responsible for fraud detection? ; What is a fraud awareness program? ; What is a corporate fraud policy? -- Fraud prevention and detection : Detecting fraud ; Determining the exposure to fraud ; Assessing the risk that fraud is occurring (or will occur) ; External symptoms ; Identifying areas of high risk for fraud ; Looking at the exposures from the fraudster's perspective ; Approach 1: Control weaknesses ; Approach 2: Key fields ; Being alert to the symptoms of fraud ; Building programs to look for symptoms ; Investigating and reporting instances of fraud ; Implementing controls for fraud prevention -- Why use data analysis to detect fraud? : Increased reliance on computers ; Developing CAATT's capabilities ; Integrated analysis and value-added audit ; Recognizing opportunities for CAATT's ; Developing a fraud investigation plan -- Solving the data problem : Setting audit objectives ; Defining the information requirements ; Accessing data ; Data paths ; Data file attributes and structures ; Assessing data integrity ; Overview of the application system ; Overview of the data -- Understanding the data : Computer analysis ; Analysis techniques ; Assessing the completeness of the data ; Expression/equation ; Gaps ; Statistical analysis ; Duplicates ; Sorting and indexing -- Overview of the data : Summarization ; Stratification ; Cross tabulation/pivot tables -- Working with the data : Aging ; Join/relation -- Analyzing trends in the data : Trend analysis ; Regression analysis ; Parallel simulation -- Known symptoms of fraud : Known and unknown symptoms ; Fraud in the payroll area ; Fraud in the purchasing area ; Symptoms of purchasing fraud -- Unknown symptoms of fraud (using digital analysis) : Data profiling ; Ratio / variance analysis ; Benford's law -- Automating the detection process : Fraud applications or templates ; Fraud application development -- Verifying the results : Confirmationletters ; Sampling ; Judgmental or directed sampling ; Statistical sampling ; Quality assurance ; Quality assurance methodology ; Preventive controls ; Detective controls ; Corrective controls ; Ensuring reliability ; Data analysis and prosecuting fraud -- Appendix A. Fraud investigation plans : Insurance policies: too good to be true ; Paid by the numbers -- Appendix B. Application of CAATT's by functional area -- Appendix C. ACL installation process.
520			_aThis thorough and highly readable guide is filled with authoritative tips, techniques, and advice on: what fraud is really costing your organisation; why use data analysis to detect fraud? How to assess the risk that fraud is occurring and automating your corporate fraud detection process.
590			_arpm 22/05/13
591			_aLoans
650		0	_aFraud.
650		0	_aFraud investigation.
650		0	_aFraud _xPrevention.
650		0	_aAuditing, Internal _xData processing.
942			_2ddc _cBOOK
999			_c181 _d181