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ACL

ACL. General Audit Software. Overview. ACL never alters data file Input file definition Link to the data file Tells ACL how to read the data file Describes structure and content of a data file Field name Data types Where each field starts Length of each field.

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ACL

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  1. ACL General Audit Software

  2. Overview • ACL never alters data file • Input file definition • Link to the data file • Tells ACL how to read the data file • Describes structure and content of a data file • Field name • Data types • Where each field starts • Length of each field

  3. Basic Operation of ACL(viewing data) • Opening a document • Workbook.acl • Includes INVENTORY, AR…. • Records vs. Field

  4. Overview • Select an input file • Modifying the view window • Removing columns • Adding columns • Moving columns • Changing (modifying) the display of columns • Changing the font styles

  5. Analyzing data • Counting data • Totaling data • Viewing statistics • A set of descriptive statistics • Defining an IF statement • Stratifying data • Intervals (e.g., 10 equal intervals) • Free • Graph output option • Classifying data • Count the number of records • Accumulate totals for each strata of a numeric field • Aging data • Cut-off date • Global filter

  6. Sampling • Sampling Approaches • Two methods • MUS (Monetary unit sampling) • Record sampling • Difference: sample unit • MUS: $1 • Record sampling: each record

  7. Advantages/disadvantages • MUS: • the chance of an item being selected is directly proportional to its size. • The larger the $, the more probable to be sampled • Useful for substantive tests or overstatement tests (the higher value items have greater risk of containing a material error) • Record sample: • Each record has equal chance of being selected • A $100 item has the same chance of selection as $1,000,000 item • Large items could be overlooked • Useful for compliance test (test of control) or understatement testing (larger amounts are least likely to be understated)

  8. Sample selection method • Fixed interval sample (Systematic method) • E.g., start of 5 and an interval of 20 • Random sampling • Cell sample (random interval sample) • Population is broken into groups by the size of the interval • One random item is chosen from each group • Random sampling • Random sample from the whole population

  9. Determination of sample size • Record sampling • ARACR (Acceptable risk of assessing control risk too low) • Population • TER (Tolerable error rate) • Maximum number of errors auditors are willing to accept • EPER (expected population error rate) • In ACL • ARACR = confidence level • Confidence level is the opposite of ARACR • Population = population (numbers) • TER = upper error limit • EPER = expected error rate

  10. RELATIONSHIP FACTORTO SAMPLE SIZE ARACR TER EPER Pop. size Inverse Inverse Direct No effect (if pop.> 5000)

  11. MUS • ARIA (Acceptable risk of incorrect acceptance) • Compare to ARACR • Both refer to a chance of misjudgment • Population • TM (Tolerable misstatement) • Compare to TER (Tolerable error rate) • Maximum $$ (number) of errors auditors are willing to accept • EPER (expected population misstatement) • In ACL • ARACR = confidence level • Confidence level is the opposite of ARIA • Population = population ($$) • TM = materiality • EPER = expected total errors (expected total $ amount of errors in the population)

  12. Examples of sampling • Record sampling:Use INVENTORY file • What is sample size? • Confidence = 95% • Population • Upper error limit = 15% • Expected error rate = 2% • Fixed interval • Interval? • Start = 4 • Cell sampling/random sampling • Seed = 5 • Select no repeat in the option • Evaluation • Number of errors = 2 • What is your conclusion?

  13. MUS: use INVENTORY file • Size • Confidence = 95% • Population = total $ of “value” • Materiality = $5,000 • Expected total errors = $2,000 • Fixed interval • Interval? • Start = 4 • Evaluation • Errors found Error 1: $45 error in an item of $5,000 Error 2: $200 error in an item of $7000

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