06/17/2010 Meeting - Electronic Data Analysis Using ACL
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06/17/2010 Meeting - Electronic Data Analysis Using ACL 06/17/2010 Meeting - Electronic Data Analysis Using ACL Presentation Transcript

  • Data Analysis Using ACL An Overview Association of Certified Fraud Examiners San Jose June 17, 2010
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 1Partners in Learning Facilitators Dale Livezey Senior Manager NorPac Regional Technology Leader Audit and Enterprise Risk Services San Francisco, CA 415-783-4208 dlivezey@deloitte.com Greg Peuziat IT Internal Audit Director Visa USA Foster City, CA 650-432-4206 gpeuziat@visa.com
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 2Partners in Learning Agenda • Introductions • Objectives • ACL Interface Overview • Commonly Used ACL Commands – Quick Hits – Filtering – Basic Calculations (adding a field) • Example Journal Entry Tests – Step by Step Instructions
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 3Partners in Learning Objectives Participants will be able to: • Use basic commands and filters on journal entries and other data • Perform simple and advanced calculations • Apply the Summarize command in a variety of entity situations • Effectively use the Join command in a variety of entity situations • Use ACL best practices for journal entry testing • Use ACL to perform tailored, entity-specific tests to provide meaningful data results
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 4Partners in Learning DA and ACL — In the Spotlight • For many years, ACL was a hard sell • Nice sounding, but ―next year‖ • Technical challenges • Limited training • Limited support Recent changes in the profession have made Data Analysis a must!
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 5Partners in Learning Our Professional Standards are Changing Right Before us! • In some circumstances, for example when testing journal entries, we may be inclined to test substantially all items in certain populations (e.g., related parties, nonsystematic transactions). • With the volume of most entities‘ data continuing to increase, data analysis may be the only efficient, effective, and thorough way to perform tests such as these!!
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 6Partners in Learning ACL Interface Overview
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 7Partners in Learning ACL PROGRAM WINDOW North America 131 offices in 2 countries Ranked No. 2 LACRO (Latin America and Caribbean) 69 offices in 28 countries Africa 46 offices in 21 countries Europe 297 offices in 47 countries Ranked No. 2 Ranked No. 2 in UK Ranked No. 3 in France Middle East 29 offices in 16 countries Asia Pacific 113 offices in 26 countries Ranked No. 2 Ranked No. 1 in Taiwan
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 8Partners in Learning North America 131 offices in 2 countries Ranked No. 2 LACRO (Latin America and Caribbean) 69 offices in 28 countries Africa 46 offices in 21 countries Europe 297 offices in 47 countries Ranked No. 2 Ranked No. 2 in UK Ranked No. 3 in France Middle East 29 offices in 16 countries Asia Pacific 113 offices in 26 countries Ranked No. 2 Ranked No. 1 in Taiwan ACL PROGRAM WINDOW — Display Area
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 9Partners in Learning ACL PROGRAM WINDOW — Display Area Tabs North America 131 offices in 2 countries Ranked No. 2 LACRO (Latin America and Caribbean) 69 offices in 28 countries Africa 46 offices in 21 countries Europe 297 offices in 47 countries Ranked No. 2 Ranked No. 2 in UK Ranked No. 3 in France Middle East 29 offices in 16 countries Asia Pacific 113 offices in 26 countries Ranked No. 2 Ranked No. 1 in Taiwan
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 10Partners in Learning ACL PROGRAM WINDOW — Project Navigator North America 131 offices in 2 countries Ranked No. 2 LACRO (Latin America and Caribbean) 69 offices in 28 countries Africa 46 offices in 21 countries Europe 297 offices in 47 countries Ranked No. 2 Ranked No. 2 in UK Ranked No. 3 in France Middle East 29 offices in 16 countries Asia Pacific 113 offices in 26 countries Ranked No. 2 Ranked No. 1 in Taiwan
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 11Partners in Learning ACL PROGRAM WINDOW — Overview and Log Tabs North America 131 offices in 2 countries Ranked No. 2 LACRO (Latin America and Caribbean) 69 offices in 28 countries Africa 46 offices in 21 countries Europe 297 offices in 47 countries Ranked No. 2 Ranked No. 2 in UK Ranked No. 3 in France Middle East 29 offices in 16 countries Asia Pacific 113 offices in 26 countries Ranked No. 2 Ranked No. 1 in Taiwan
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 12Partners in Learning ACL PROGRAM WINDOW — Log Tab
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 13Partners in Learning Commonly Used Commands
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 14Partners in Learning Quick Hits Commands • Classify — Provides totals for one of every different value in a field • Statistics — Totals, averages, and other key statistics • Stratify — Stratifies amounts into ranges and provides totals • Age — Performs aging analysis based on specified cutoff date
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 15Partners in Learning Filtering – Commonly Used Commands • Numeric — E.g., AMOUNT > 0 • Character — E.g., STATE= ―CA‖ • Date — Filtering using date values, E.g., ‗20050930‘ - INVDATE > 120 • Find Function — Word search • Match Function — Finding multiple values
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 16Partners in Learning Misc. Commands • Export — Export an ACL Table to an Excel file. • Extract — Create a new ACL table from filtered criteria. • Summarize — Collapse data and create a new table.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 17Partners in Learning Calculations • In Excel, you type ―=‖ followed by a calculation • In ACL, you define calculations in the ―table layout‖ • The table layout contains: – Field definitions for the entity‘s data – Calculations that you define
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 18Partners in Learning Mechanics of Performing Calculations Two steps: 1. Define the calculation. 2. Add the newly created field to your view.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 19Partners in Learning Defining a Calculation • Click on Edit/Table Layout. • Click on the ―Add a new expression icon‖ [Fx]. • Under ―Name‖ type a new field name. • Under ―Default Value‖ enter the calculation. • Click the green checkmark. • Click the [X] to exit Edit/Table Layout.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 20Partners in Learning Adding the New Field to Your View • You add columns using the following icon: • Click the icon, • Double-click on your new field (it jumps to the right), • Click OK and it becomes your rightmost field. • Later, you can drag the fieldname left or right to move it to where you desire.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 21Partners in Learning Common Journal Entry Tests
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 22Partners in Learning Common Journal Entry Tests • Seldom used accounts • Users who seldom post JE • Back date / pre-date • Entries on weekend and holidays • Key word search
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 23Partners in Learning Seldom Used Accounts To create a record count summary by account • Click on Analyze / Summarize • On the ―Main‖ tab, select the Account_number field under [Summarize on], • Select the Acct_description field under [Other Fields] • Select the Debit and Credit fields under [Subtotal Fields] • Go to the ―Output‖ tab and put ACCT_SUMMARY next to [Name] • Click [OK]
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 24Partners in Learning Seldom Used Accounts (cont‘d) • A new field called ―Count‖ is created which contains the number of entry lines hitting each particular account for the period your data file covers. • Review the count column for low numbers. • You can set a filter, COUNT < 5 (or whatever you consider as being low)
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 25Partners in Learning Users Who Seldom Post JEs To identify individuals that do not typically prepare entries Review the count column for low numbers. • Click on Analyze / Classify • Under [Classify on…] select the Username field • Under [Subtotal Fields…] hold the (Ctrl) key and click to select Debit and Credit. Or Amount if there is a single column • Click [OK] • Review the ―Count‖ column for low numbers.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 26Partners in Learning Back Date / Pre-Date Journal Entries To Identify large date differences between post and effective dates. If you have a field (column) representing Post or prepared date, called POST_DT and a field representing effective date called, EFF_DT, you can set a filter: • Back Date: POST_DT – EFF_DT > 30 to see the entries booking amounts to a prior period • Pre-Date: EFF_DT – POST_DT > 30
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 27Partners in Learning Entries on Unusual Days To identify entries booked on unusual days (e.g. weekend days and holidays). • Click on Analyze / Classify • Click the [Classify On…] button • click on the [Expr…] button • Under where it says Expression, type CDOW(ENTRY_DT,3) • Click [OK],[OK] • Under ―Subtotal Fields‖, select Debit and Credit, or Amount if Debit and Credit are netted • Click [OK] to run the command
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 28Partners in Learning Entries on Unusual Days (cont‘d) To set a filter for a particular day of the week, again assuming your field is called ENTRY_DT, type: • CDOW(ENTRY_DT,3)=‖Sun‖ and click the green checkmark To set a filter for both Saturdays and Sundays, type: • MATCH(CDOW(ENTRY_DT,3),‖Sat‖,‖Sun‖) and click the green checkmark
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 29Partners in Learning Entries on Unusual Days (cont‘d) To identify entries made on a holiday, or a day the entity is supposed to be closed such as a company picnic, you will need to set a filter. • For example, to set a filter for entries made on Christmas Day, type: ENTRY_DT=`20051225` and click the green checkmark
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 30Partners in Learning Key Word Search To identify entries with key words of audit interest • Use the FIND function • Syntax of the Find Function is FIND(―what I am looking for‖, Field being searched). • There are quotes around what you are searching for, but not around the field name. Examples: Find(―reverse‖,DESC)
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 31Partners in Learning Key Word Search (cont‘d) " ADJ " CONFIDENTIAL DUMMY IMPROPER " RECL " SCREEN "ADJUST" CONTROLLER EARLY INAPPROPRIAT E RECLASS " SECRET " " ALTER " COOKIE JAR " EBIT " INCREASE RECLS SMOOTH AS DIRECTED CORRECT EBITDA KITTY RECONCILE SPREAD AS REQUESTED " COVER " ERROR MANAGE EARNINGS REDUCE " TEMP " BURY COVERUP FICTITIOUS MANIP REDUCT " TEST " CAPITAL COVER-UP FRAUD MISSTATE RESTATE TRANSF " CEO " D&T HIDE OPPORTUNIT " REV " TSFR " CFO " DELETE HIDDEN "PER " REVERSAL TXFR CLASSIF DELOITTE HOLDBACK PLUG REVERSE UNSUPPORTED CONCEAL " DT " IMMATERIAL PROBLEM RISKS Examples of key words:
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 32Partners in Learning ACL Best Practices — JE Testing (cont‘d) You may, or may not have realized… • Prior to this learning unit, the steps may have looked ominous; when you use the document now, it will be easy reading!
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 33Partners in Learning Appendix –Advanced Calculations –Summarize –Join –Match
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 34Partners in Learning Advanced Calculations • Using conditions • Default value works in all cases • Except where there is one or many conditions • For example, for a sales tax field, the default rate could be 5% but if the county is ―Sonoma‖, it is 9%
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 35Partners in Learning Advanced Calculations (cont‘d)
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 36Partners in Learning Conditions • When you double-click ―Condition‖ • You enter a criteria, similar to what you would type when filtering. • And, you enter a value or calculation when the criteria is met, which overrides default value.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 37Partners in Learning Concatenation Combines multiple character fields and strings into one field. For example: • A YEAR field has ―2005‖ • A QUARTER field has ―Q1‖ • With concatenation, a PERIOD field can be assembled: QUARTER+―-‖+YEAR • Result Q1-2005
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 38Partners in Learning Character Functions • Sub — Chops sections from existing character fields. • AT — Searches within a field and returns the location as a number. • Date — Converts a date into character so it can be used to derive periods. • CDOW — Provides the day of week from a date field. • FIND — Locates the first record with the specified characters.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 39Partners in Learning Character Functions (cont‘d) Functions for formatting: • TRIM(field) — Removes trailing blank spaces. • LTRIM(field) — Removes leftmost spaces. • ALLTRIM(field) — Removes trailing and leftmost spaces. • PROPER(Field) — Makes a field proper case. • UPPER(field) — Makes a field upper case. • LOWER(field) — Makes a field lower case.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 40Partners in Learning Summarize Command The Summarize command creates a new file by collapsing an existing one. For example: • By summarizing on a customer number or vendor number in an accounts receivable or accounts payable invoice file, you get a master file (table) with customer or vendor totals. • By summarizing on a G/L account in a detailed journal entry file, you get a file with totals by account. In the P&L section, you‘ll get ending balances, and in the balance sheet accounts, you‘ll get net activity. In many cases, you‘ll be able incorporate beginning balances, calculate ending balance, and be on the road to reconciliation.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 41Partners in Learning Summarize Command (cont‘d) Using Character functions, formulas, and concatenation, you can modify an account number in a Trial Balance to remove a sub-account or branch. Then, you can summarize on the NewAccount, and export the collapsed Trial Balance to Excel.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 42Partners in Learning Summarize — How to Run • Click on Analyze/Summarize. • Under Summarize on…, click the field(s) you want the data summarized or collapsed on. • Under Other fields… click on the fields which you want included into the new summarized file. • Under Subtotal fields…, click the fields you want added up. • On the “Output” tab, name your new file(table). • BY ALL MEANS keep “Presort” checked.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 43Partners in Learning Join Command • The JOIN command creates a new file by combining the fields in two files. • It can be used to add information to an existing file, compare the information in two files, or eliminate specified information from a file.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 44Partners in Learning When is Join Used? • You have a table with accounts receivable invoice detail which includes a customer number. In a separate master file, you have customer number, name, address, and credit limit; one row per customer. By JOINing based on the customer number common to both files, you can fill in the name, address, and credit limit information alongside the invoice detail.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 45Partners in Learning When is Join Used? (cont‘d) • You can use JOIN to combine last year‘s trial balance with this year‘s trial balance, to get ACCT, DESC, PYBAL, and CYBAL. • You can JOIN a payment register with an employee file by Social Security Number. Any matched records would be your catch. • You can JOIN a journal entry file, after being summarized by account, to a trial balance in order to reconcile the data.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 46Partners in Learning When is Join Used? (cont‘d) • You can join an expense transaction file with a small file containing accounts to be excluded, using an ―unmatched‖ option. The result would be expense detail for all other accounts.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 47Partners in Learning Join Command — Key Points • You JOIN two files. One is a ―Primary‖ file, and one is ―Secondary.‖ The primary file is the table that is open when you invoke the JOIN command. Upon invoking the JOIN command, you select the secondary file (table). • ―Key‖ fields are the fields on which you join the two files. They are common to both files.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 48Partners in Learning Join Command — Key Points (cont‘d) • ―Common‖, with respect to ―Key‖ fields means: They are both character. They are the same case, same length, and have the same appearance. 12345-XX and 123-45XX are the same in most respects but they are not the same in appearance. You‘ll get no matches. • For example, you have two files and you want to join using Social Security Number as a key. One file has dashes (123-45-6789) and the other file doesn‘t (123456789). To make these into workable keys, you need to add dashes to the one; or, remove dashes from the other.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 49Partners in Learning Level of Detail, Three Scenarios • One-to-one — There are no duplicate occurrences of content in your key fields for both files. • For example, last year‘s trial balance vs. the current year trial balance. In both files, the data is one row per unique account number. It is ok if both files have account numbers not contained in the other file. But to be one-to-one, there are no repeats of an account number in either file.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 50Partners in Learning Level of Detail, Three Scenarios (cont‘d) • Many-to-one — There are multiple occurrences of content in the key field of one file, but there are no duplicate occurrences of content in the key fields of the other file. • For example, one file has detailed invoice data. Often, many invoices are outstanding per customer. The file contains a customer number, which repeats alongside the customer‘s respective invoices. This is the ―many‖. The other file is a demographics file which has one row per unique customer number. This is the ―one‖. Many-to-one situations are common and workable.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 51Partners in Learning Level of Detail, Three Scenarios (cont‘d) • Many-to-many — Not workable. Your only hope is the possibility that you can summarize one of the files by a key field and not lose the substance needed for the join. The summarized file would become the ―one‖ and the other file, the ―many‖.
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 52Partners in Learning Matching Options • MATCHED RECORDS — Using Join as a Search Tool Matched records means your new table will contain all primary records which had a match in the secondary table. If there is no matching key in the secondary file, the primary record is dropped. For example, when joining a vendor payment file with an employee file using Social Security Number as the key, the output of the join will only be the records that matched (i.e., the vendor payments made where the Tax Identification Number was the same as an employee‘s Social Security Number).
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 53Partners in Learning Matching Options (cont‘d) • MATCHED RECORDS, ALL PRIMARY — Using Join as a Data Fill — One-to-many scenarios Whether there is a match or not, the output of the join will have all records contained in the primary file. If there isn‘t a match, fields designated to be included from the secondary file will be blank (character), and zero (numeric).
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 54Partners in Learning Matching Options (cont‘d) • MATCHED RECORDS, ALL PRIMARY AND ALL SECONDARY — Using Join as a Data Fill — One-to- One Scenarios Whether there is a match or not, the output of the join will have all records contained in the primary and secondary files. If there isn‘t a match, fields designated to be included from the ―other‖ file will be blank (character), and zero (numeric).
  • Copyright © 2010 Deloitte Development LLC. All rights reserved. 55Partners in Learning Matching Options (cont‘d) • UNMATCHED — Eliminating unwanted data: Unmatched eliminates primary file records where there is a match with the secondary file. For example, you have voluminous expense detail in your primary file. However, you want to eliminate all records for 25 accounts. Make a small file of the 25 accounts in Excel. Get the file into ACL. Make sure the keys have the same attributes. Make the expense detail the primary file, and the 25 elimination accounts the secondary file. Join with the UNMATCHED option.