Analytics for External Auditors - CaseWare · CA411AnalyticsforExternalAuditors 6 GeneralLedgerTransactionsCompletenessTest 31 GeneralLedgerTests-UnderstandingtheData 39 Test1a:SummaryofJournalEntrybyEmployee/User

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CA411

Analytics for ExternalAuditors

Applying Data Analytics to Your Clients' Critical Business Processes

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CA411 Analytics for External Auditors

Version: CA411_A4_01

Published on March 27, 2019

https://idea.caseware.com/

Contents

Section 1Introduction 15

Introduction 16

Data Collection Plan 17

What Information is Required? 17

Step 1: Identify the Questions and the Assertions You Want to Answer 17

Step 2: Confirm the Data Requirements to Meet the Audit Objectives 17

Step 3: Determine the Location or Source of Data and Understand BusinessProcesses and Procedures 17

Step 4: Refine Data Requirements 18

Step 5: Determine the Format of the Data 18

Step 6: Determine How the Data Will Be Transported 20

Step 7: Request and Verify Provided Data 21

Conclusion 21

Collecting and Importing Data from Third-Party Accounting Packages 22

Section 2General Ledger 23

Why is Data Analysis Important? 24

Using Data Analytics to Assist in Substantive Audit Testing 25

Using Data Analytics to Provide Insight and Added Value Back to the Client 26

Best Practice for Performing GL Data Analytics 27

What is Our Audit Objective? 27

Planning – What Data is Required? 27

Obtaining and Normalizing the Data 28

The Key Fields Required in the Data Extract(s) 28

General Ledger Initial Check 30

Initial Checks 30


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General Ledger Transactions Completeness Test 31

General Ledger Tests - Understanding the Data 39

Test 1a: Summary of Journal Entry by Employee/User 39

Test 1b: High Value Users and Account Type 40

Test 2: Summary by Transaction Type 41

Test 3. Summarizing by Posting Period and Amount 41

'Manual' Journals Testing 43

Identifying the Journal Entry Type 43

Identifying What is a 'Manual' Journal Entry 43

General Ledger Data Analytics – Manual Journal Entry Tests 45

Manual Journal Test 1: Isolate and Highlight Large Value Journal Entries 45

Manual Journal Test 2: Journal Entries With No Description 47

Manual Journal Test 3: Rounded Amounts 48

Manual Journal Test 4: Weekend Journal Postings 50

Manual Journal Test 5: Bank Holiday Postings 51

Manual Journal Test 6: Out of Office Hour Postings 53

Manual Journal Test 7: Highlighting Key Words within Journal EntryDescriptions 54

Manual Journal Test 8: Suspense and Contra Account Postings 56

Manual Journal Test 9: Same Posted by and Approval User 58

Manual Journal Test 10: Postings Close to Year End 59

Manual Journal Test 11: Postings at the Start of the Year 61

Manual Journal Test 12: Unbalanced Journals 62

Testing the High Risk Manual Journal Entries 65

Section 3Revenue Assurance 69



Telecoms - Customer Invoicing 71

Financial Services – Loan Company Interest Calculations 72

Test 1: Check Interest Income Has Been Calculated Correctly 72

Education – Student Invoicing 75

Subscription Based Revenue 75

Deferred or Accrued Income Calculations (Balance Sheet) 78

On-line Betting – Net Gaming Revenue 78

Final Note on Revenue Assurance Data Analytics 79

Contents

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Section 4Accounts Receivable 81


Using Data Analytics to Assist in Audit Testing 83



Best Practice on Performing the Data Analytics 87




Required Datasets 88


Accounts Receivable Initial Check 90

Accounts Receivable Completeness Tests 91

Test 1: High Value Accounts Receivable Balances 91

Test 2: Extract High Value Credit and Related Party Balances 94

Test 3: Age Open Items 95

Test 4: Select Samples for Further Testing 96

Accounts Receivable Data Analytics – Transaction Tests 98

Test 5: Check Existence and Recoverability of Customer Balances 98

Identifying Duplicates and Gaps on Account Receivable Transactions 106

AR Duplicate Test 6a 106

AR Duplicate Test 6b 107

AR Duplicate Test 6c 107

AR Duplicate Test 6d 108

AP Gap Detection Test 7 109

Section 5Accounts Payable 111





Best Practice on Performing the Data Analytics 116





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Required Datasets 117


Accounts Payable Initial Check 120

Accounts Payable Completeness Test 121

Test 1: High Value, Debit and Related Party Accounts Payable Balances 121

Test 2: Extracting High Value Supplier Balances, Debit and any RelatedParty Balances 123

Test 3: Age Open Items 125

Identifying Top Suppliers in the Year 127

Test 4: Extract Top Suppliers Year To Date (YTD) with Purchase Value andYear-end Balances 127

Duplicate Purchase Invoices - Preparation 132

Duplicate Detection Testing 134

AP Duplicate Test 1 134





Fuzzy Duplicate Purchase Invoices 159

Test 6: Fuzzy Duplicate Invoice Numbers 159

Supplier Master Vs. Employee Master Tests 161

Test 7: Supplier Master and Employee Bank Details Check 161

Test 8: Check Revenue Implication if Master Data Matches Found 163

Test 9: Supplier Master and Employee Bank Address Check 164

Section 6Payroll 173





Best Practice when Performing Payroll Data Analytics 179





Payroll Initial Check 182

Payroll Data Analytics – The Basics 183

Contents

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Test 1: Random Selection of Joiners and Leavers to Check for IneffectiveInternal Processes 183

Preparation - Payroll Data Transactions Tests 185

Duplicate Employee Payments 190

Test 2: Testing for Duplicate Employee Payments 190

Duplicates Employee Master Records Test 192

Test 3: Testing for Duplicates Employee Records 192

Employee Master vs. Payroll Transactions 197

Test 4: Potential Overpayments 197

Test 4a: Overpayments – When Payment Date Data is Available 199

Test 4b: When Payment Date Data is not Available 199

Test 5a: Ghost Employees – Payment to Employees Where No Time Worked 201

Section 7Fixed Assets 205

Why is Data Analysis Important to External Audit? 206




Defining Audit Objectives and Data Requirements 210

What is the Audit Objective? 210




Fixed Assets Initial Check 212

Fixed Assets Data Analytics – The Basics 213

Recalculation of the Depreciation Charges 213

Method 1: Recalculation - Straight Line Method 214

Method 2: Recalculation - Reducing Balance Method 216

Section 8Inventory 219





Defining Audit Objectives and Data Requirements 225


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Inventory Initial Check 228

Inventory Completeness Test 229

Inventory Data Analytics – The Basics 231

Test 1: Understanding the Profile of Item Values, Quantities and Unit CostPrices 231

Test 2: Inventory Listing Comparison Between Two Year-ends 237

Slow and Obsolete Items 241

Test 3: Checking Calculation of Provisions 241

Test 4: Identify Slow Moving and Obsolete Items 243

Test 5: No Movement or Sales in the Post Year-end Period 244

Section 9VAT 249




Best Practice on performing VAT Data Analytics 253

What is our Audit Objective? 253




VAT Data Analytics – Data Preparation 256

Preparing Accounts Receivable Data 256

Preparing Accounts Payable Data 259

VAT Total Checks 262

Test 1: Extract Records Where the VAT Rate Calculated Is Unusual 263

Test 2: Extract Records Where the VAT Rate Calculated is Unusual 265

VAT Posting Period Checks 267

VA 001.1 All Sales Transactions (Accounts Receivable) 267

VA 002.1. All Purchase Transactions (Accounts Payable) 270

Foreign Invoice VAT Testing 275

VA 001.2 Sales Transactions with Customer Details 275

VA002.1 Purchase Transactions with Supplier Details 277

Contents

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Section 10Next Steps 281

The Next Steps 282

Analytics Automation 283

Integrating Data Analytics to Working Papers 284

Preface

Attribution StatementOriginal content for this guide was developed by AuditWare Systems Ltd. of the UnitedKingdom.

AuditWare has been delivering technology solutions for analytics applied to businessassurance for more than 30 years.

For more information, visit www.auditware.co.uk.

PurposeThe goal of this guide is to help external auditors properly understand the application ofdata analytics. This guide is intended to aid independent CPA (Chartered ProfessionalAccount) and external audit firms in the introduction and application of data analytics tothe critical business processes of their clients.

This guide can help you:

l Expand your skill set.

l Generate additional fee income.

l Improve the quality of your audit work.

l Understand how to apply data analytics using IDEA.

l Compete with other firms able to offer this knowledge and service.

http://www.auditware.co.uk/

Section 1

Introduction


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Introduction

In recent years there has been an increasing adoption of the use of Data Analytics withinExternal Audits. Data Analytics is sometimes a new discipline for Auditors and can be off-putting due to the requirement for a change in approach to audits, an awareness of howdata is stored and flows through a client's organization and the adoption of new tools andtechniques.

Initially, these can appear to be barriers to adopting Data Analytics but once overcomethese tools and techniques can be applied to a variety of assurance engagements andenables Auditors to manipulate a complete data set, delivering a higher quality of auditevidence with more relevant business insights and added efficiency.

CaseWare IDEA is a powerful and versatile Data Analytics solution that is gearedspecifically to the requirements of Auditors. It enables the improvement of businessperformance, extends auditing capabilities and adds more quality to the overall auditsthemselves. IDEA is the principle Data Analytics solution in use at many External Auditfirms.

This essential guide provides guidance and best practice of obtaining the right data andhow to apply Data Analytics, specifically using IDEA, to the following business processes,enhancing the service you offer to your clients, either as part of a consulting service or aspart of a standardized Audit.

l General Ledger

l Revenue Assurance

l Accounts Receivable

l Account Payable

l Payroll

l Fixed Assets

l Inventory

l VAT

Within this publication, we will provide not only the IDEA techniques needed to performthe Data Analytics, but also detailed information of the relevance of each of the DataAnalytics reports it refers to and best practice advice on how to interpret the results.

Section 1: Introduction

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Data Collection Plan

What Information is Required?Within an External Audit engagement, knowing where, how, and what to look for withincompany data is crucial to meeting the audit objectives. Therefore, data gathering is ahighly important activity in any audit.

To ensure and achieve successful Data Analytics, it is important to put together a DataCollection Plan that specifies the data required, giving greater assurance that the dataprovided by the client is meaningful, valid and in a condition that will require minimalmanipulation before testing.

The following data collection steps provide some guidance that will enable you to capturethe right data for your audits.

Step 1: Identify the Questions and the Assertions You Want to Answer

The Auditor must first understand what the objective is for the audit and what assurancesare being tested, including verification that:

l All assets, liabilities, and equity interests have been recorded and disclosedcorrectly.

l Confirm the existence of assets, liabilities, and equity.

l Valuation of Assets, liabilities, and equity are reported at the correct amount.

l Transactions and events are recorded in the correct accounting period.

Step 2: Confirm the Data Requirements to Meet the Audit Objectives

Once the objectives and assurances have been identified, the data requirements to meetthese objectives can be determined to ensure that the Auditor can draw reasonableconclusions.

Determine what data exists to meet the objectives set and how much data is needed toprovide answers for objectives and assurances the Auditor is trying to gain.

In this section, rather than referring to all of the specific data requirements needed for thetesting objectives in this guide, all of the relevant data requirements for each of thebusiness processes are detailed within each of their specific sections.

Step 3: Determine the Location or Source of Data and Understand Business Processesand Procedures

This step is critical to understand where the information is stored and how informationflows through the business and accompanying systems.

It is also important to identify all of the individuals within the business process who willneed to be involved in the data collection process. This can include, but not be limited to:


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l Key stakeholders who may be needed to sign off on the relevant data collection.

l Key contact who the Auditor will work with to identify the information that the auditrequires.

l An individual who will perform the data extractions. This could be a member of staffwho works within the business process itself or depending on the size of theorganization may be a member of the IT department/database administrator.

It is also important to note that companies invest significantly to protect their data,including multi-layered approval processes and technology safeguards; therefore, it maybe a time-consuming process to obtain client approval for the required data.Understanding the business process and data handling protocols will pinpoint any datarestrictions in data collection not otherwise recognized.

Step 4: Refine Data Requirements

Some of the testing objectives that would have been defined in the early stages of theaudit, as well as from discussions with the client themselves, may also be based onprevious experience with other similar clients or from information obtained fromprofessional bodies or solution providers. Due to this, during the third stage of thisprocess, it may become apparent that the client does not record or have access to certaininformation that is required. Depending on any regulatory requirements that require theclient to provide this information or further opportunity to access this information(Outsource to a 3rd party to produce a report with the required information), you mayhave to reduce some of your data requirements and testing objectives.

Step 5: Determine the Format of the Data

Its recommended to determine the type of file formats that the client can select whenextracting the data from their systems. It is worth noting that in most cases many of thecommon file formats have their own issues/limitations. Incorrect selection of a file formatcan potentially cause reporting on inaccurate data. It can also cause delays in the DataAnalytics process due to the need for manual modification of the data to put it into thecorrect format.

This will need to be determined on a client by client basis. Some organizations use 3rd

party tools to extract the data from their systems. This means that although the Auditormay have come across the accounting or ERP (Enterprise Resource Planning) softwarewith another client, the formats available may vary.

In order to reduce any issues with requesting the wrong format, it is recommended thatthe Auditor works with the individual tasked with extracting the data. Based on the advicein this guide and in due course personal experience, identify the top three file formats andextract a small subset of the data (1 weeks' worth) and import them into IDEA. Thisshould hopefully identify any importation issues and identify the relevant file format to beused when extracting the full periods worth of data.

CaseWare IDEA has the ability to import data from a variety of file formats including:AS400, dBase, Microsoft Access, Microsoft Excel, Print reports and Adobe PDF, SAP/AIS,Text and XML.

Three of the most common type of file formats available are Microsoft Excel, delimitedtext files, and Print reports.


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l Microsoft Excel

Microsoft Excel is one of the most common file types used and can be imported intoIDEA.

There are some specific rules about the structure that the data must be storedwithin the Excel file. For example, there must be:

l No blank rows or columns

l No title rows

l No sub-totals or grand-totals

This means that the data must be a straight extract from the system without anygrouping or formatting applied. The Auditor must be aware that reports in a print toview/paper format can be output to an Excel file format and will most likely requiremanual modification to remove groupings, blank lines, subtotals and grand totalswhich in itself is open to potential mistakes.

One reason that the Microsoft Excel format should not potentially be the firstconsidered format, is due to the fact that depending on the quality of the reportingsoftware all values will be written into the Excel file in the 'General' format. Thepotential impact being that reference numbers will be treated as numbers andleading zeros will be removed. Specific examples of this would be telephonenumbers and bank sort codes and account numbers. Should the Auditor want tocompare these to other datasets, this could lead to valid matches not beingidentified.

l Plain Text Files

Plain text files are commonly used to transfer large files between companies andsystems. A plain text file is a file storing tabular data in plain text.

There are two main types of plain text files that IDEA can import:

l Fixed Length

Common file extensions include .txt, and .fxd.

They are known as fixed length due to the fact that each record in the file isexactly the same amount of characters and that the fields contained within therecords always start in the same character position.

l Delimited

One of the most common types of delimited files are CSV (Comma SeparatedValue) files. Other common file extensions include .asc, and .del.

Unlike fixed length files that use spaces to ensure that fields start in the sameposition, delimited files use special characters to define where fields start andend and is the most efficient type of text file, due to the fact that the recordsare only as long as the values contained within them.

It is important to note that delimited text files can sometimes be exportedfrom core systems configured to use different delimitators. Examples includetab, colon, semicolon and space. The choice of delimiter can, in some cases,be critical as common delimiters can also exist within the actual data itself forexample, commas can also be used in names and addresses.


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If the option to select your own delimiter is available, we recommendselecting pipe (|), tilde (~), or not sign (¬). In essence any selected delimitermust not exist within any of the records other than being used as a delimiter.

Another option sometimes available are the inclusion of text encapsulators,the most common being quote marks (""). By wrapping encapsulators aroundtext values any field delimiters that occur between the encapsultors areignored (e.g., "Smith, John", "12, High Street",).

Whether it be a delimited or fixed length file that is chosen, it is advised toobtain technical information about the file's record layout. Key informationwhich will assist when importing these files include:

l Field Name (If not already included within the files themselves)

l Field Type (Numeric, date, time, character)

l Delimiter (Delimited file only)

l Encapsulator (Delimited file only)

l Field length (Fixed length files only)

l Number of decimals

l Thousands separator and decimal point characters.

l Print Reports

Print reports are probably one of the easiest file types to obtain as nearly allsystems have the ability to produce printed reports and are simply an electronicversion of a printed document and often contains extra formatting and information.The additional formatting is used to make the printed document as clear as possiblefor the reader.

IDEA has the capability to import print reports using a built-in tool, Report Reader,and can extract the required data for import from the file. Importing a print reportagain allows for a greater degree of control, as it allows the user to select andimport the fields required for analysis, add in field descriptions and amend fieldtypes prior to import.

Bear in mind that although this is a powerful functionality that allows the Auditor toconvert print to view reports into structured data for easy analysis, this file typeshould only be considered if less time-consuming options are not available.

For more guidance on the process of importing data into IDEA, refer to the IDEAand Report Reader Tutorials located in the IDEA documentation folder. Moreinformation and guidance can also be found using the IDEA Help function (F1). Alsonote that if you have access to CaseWare Passport support website furtherinformation, tutorials, and videos are also available.

Step 6: Determine How the Data Will Be Transported

Careful consideration also needs to be given to how and when the Auditor will receive therequired data. Ideally, all of the pre-preparation and importation of the data at aminimum will have taken place prior to the Auditor going on-site with the client.

The most efficient way of achieving this will be to use a web-based file hosting service(OneDrive Drobox etc..). The Auditor will need to ensure that the chosen service can


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demonstrate appropriate levels of security to satisfy clients legal requirements andredundancy against loss of data. To reduce upload times of the data and to add furtherlevels of security, it is recommended that the client converts the data into a compressedformat (.zip, .rar, .tar) and adds a secure password.

Although using web-based file hosting services are the most efficient way of sharing data,some clients will only allow the sharing of data to take place physically when the Auditoris on-site. As previously mentioned, this is not ideal as the Auditor should have alreadypre-prepared the data and also gained some insights to assist the audit. In this case it isworth considering, depending on the size of the client, physically collecting the data priorto the start of the audit. This will add additional cost to the audit and should only beconsidered if the data collected will be vast or complex in nature, or the value of audit willallow.

In addition to the above points, some clients may restrict the use of their data to onlyallow analysis to take place internally using their own IT equipment. This may haveimpact on the audit, due to pre-preparation process of data, and should be considered andfactored in as part of the audit programme.

Step 7: Request and Verify Provided Data

The final step is to request the data from the key contacts or individuals identified in Step3. The data can then be provided to the Auditor for verification and analysis.

An important part of the import process is to verify totals prior to any testing. Some fileformats such as Microsoft Excel allows the Auditor to compare totals in the files providedby the client with its IDEA equivalent. This is assuming that the data extracted is correct.So, for completeness and accuracy control totals should be provided by the client basedon information available within the user interface of the accounting software or ERPsystem. It is up to the Auditor whether a screenshot or a physical confirmation (Auditorviews the totals when on-site with the client) is applicable.

Conclusion

It is important to note that regardless of the type of audit being completed an accurateand efficient data collection process is essential to maintaining the integrity and timelineof the audit or analysis. Understanding the data, the formats available, the data sourcesand any data restrictions in place can reduce the likelihood of errors occurring. A datacollection process is essential to ensure that the data gathered is both well-defined andaccurate. This provides confidence that the assurances tested are valid and true.


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Collecting and Importing Data from Third-PartyAccounting Packages

The CaseWare Cloud Import Utility is a very useful tool to assist in the collection ofdata. This powerful tool enables the auditor to obtain data from just about any accountingpackage on the market and enables the CaseWare Accounting Package ImportComponent (available as an optional import component in IDEA) to import GeneralLedger and Trial Balance tables directly into IDEA for analysis.

In any engagement that requires analysis of General Ledger and or the Trial Balance, theCaseWare Cloud Import Utility simplifies the data collection process from the nativeAccounting Package installed at the client location. The standalone utility is used to obtaindata directly from the Accounting Packages and standardizes the data into a single outputcompressed file (CaseWare Accounting Package file) that is consumable by IDEA. Thiseliminates all the headaches of having to ask the client to extract data from theirproprietary accounting software and then figuring out what all the column names are etc.;this is all taken care of by the utility.

As a standalone utility, it provides great flexibility in the extraction and collection of theclient’s data. For instance, just one auditor can be responsible for extracting the GL andTB on-site at the client and distribute the IDEA consumable file to other auditors on theteam, or perhaps the client can run the tool locally and send the resulting file to theauditor. This flexibility will enhance the overall Data Collection process.

Once the import utility creates the CaseWare Accounting Package file, this file can then beimported into IDEA using the CaseWare Accounting Package Import Componentthat generates Balance, Company, Journal, and LedgerAccount databases.

The CaseWare Cloud Import Utility and the CaseWare Accounting PackageImport Component are ideal tools to complement the overall Data Collection Plan. Withsupport for over 90 Accounting Packages, it is highly likely that the process of datacollection and import will be greatly simplified and save a lot of time. They can be used toimprove the efficiency of nearly every step of the plan.

Section 7

Fixed Assets


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Why is Data Analysis Important to External Audit?

1. Meeting Audit Regulator Expectations

In recent years the Financial Reporting Council's (FRC) have been focusing on theadoption of Data Analytics to improve Audit quality across the Audit profession.Fixed assets is an area where the data is most likely to be held in a spreadsheet.Regulators are looking for Auditors to robustly test the integrity and accuracy of thedata and coded formulae; therefore, performing Data Analytics using an DataAnalytics tool, such as IDEA will provide a good demonstration of this.

2. Improve Audit Quality and Efficiency

Many clients store their Fixed Assets records within spreadsheets, which may beprone to errors. Therefore, using an Data Analytics tool such as IDEA, is a veryeffective way of checking the integrity and accuracy of the calculations including,depreciation charges and ensuring totals are calculated correctly.

3. Managing the Audit Risk

Although the main risk with auditing Fixed Assets is around establishing whether theassets exist, there are some other risks where Data Analytics can help in the auditprocess.

The risks normally attached to the audit of Fixed Assets include:

l Incorrect calculation or manipulation of depreciation rate and calculation

l Overstatement of Fixed Asset

l The existence of the reported Fixed Assets

Therefore, there is a need to calculate the depreciation and test the accuracy of theFixed Assets, especially where clients keep their assets records on spreadsheets.

4. Providing Insight back to the client

As previously mentioned, with the majority of Fixed Asset registers being held onspreadsheets, there is a high risk that these registers will be prone to human error.Any errors or anomalies identified when performing Data Analytics will be highlyuseful as these results can be fed back to the client for errors to be corrected.

Section 7: FixedAssets

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Using Data Analytics to Assist in Audit Testing

Fixed Assets are the long term, non-current assets with a useful life of more than one year. Their value is recorded in the balance sheet and show abalance at the end of the reporting period. Fixed Assets are normally large in comparison to current assets and; therefore, are generally considered as asensitive area from an audit perspective.

To determine where Data Analytics can assist in the audit testing process, the Auditor must first understand what testing is required to give assurance thatthe Asset balances are not materially misstated. Below is a table showing the common Audit assertions and tests relating to Fixed Assets.

REF Audit Assertions Common Testing

EOCP

Existence/OccurrenceCompleteness

l Reconcile Fixed Asset listing/register back to the General Ledgerl Check the mathematical accuracy of the Fixed Asset listing/register (Grossè depreciationè NBV)l Inspect Fixed Assetsl Review Repairs and Renewals accounts for anything that should be capitalised

VG Valuation Gross l Check large additions in the year to supporting documentation (also Sample list of additions)l Examine supporting documentationl Check whether the depreciation method is appropriate

l Identify significant disposals in the year and check for appropriate treatment

VN Valuation Net l Review depreciation methods for consistency with prior yearsl Check that depreciation charges are accurately calculated and recorded


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Using Data Analytics to Assist in Substantive Audit Testing

Having identified the Audit Assertions and the types of tests that can be used to validate Inventory data, the next stage is to identify whether the use ofData Analytics will either enable the Auditor to perform a test or help them carry out the test more efficiently AND with greater quality.

The table below contains a list of Data Analytics which can be completed for the various testing areas identified on the previous page. This will giveassurance over the accuracy and completeness of the data, identify large additions and disposals, identify any anomalies in the data (e.g., negative NetBook Values) and recalculate the depreciation charges, based on the agreed rates and method.

Data Required Data Analytics That Can Be Performed Testing Area (Assertions)

Fixed AssetRegister/Listing

l Cast and recalculate the listing producingl Extract items with a large Net Book Valuel Extract items with a zero or negative Net Book Valuel Sample the listing for physical verification of assetsl Extract large additions and disposals for further testingl Recalculate the depreciation charged in the yearl Extract assets where the depreciation rate looks unusual

Check accuracy of asset listing (EO, CP)Check accuracy of asset listing (EO, CP)Check accuracy of asset listing (EO, CP)Inspect of Fixed assets (EO)Check additions and disposals (VG)Check accuracy of the depreciation charge (VN)Check accuracy of the depreciation charge (VN)

General LedgerTransactions Listing

Identify all Repair and Renewal General Ledger accounts and extract all thetransactions in the year for those accounts.

Review Repairs and Renewals accounts for anything thatshould be capitalized (EO)


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Using Data Analytics to Provide Insight and Added ValueBack to the Client

If the Fixed Assets register is held in a spreadsheet there potentially may be errors in theformulae, hard coding of numbers, and incorrect summation of row and columns. UsingIDEA will highlight any problems within the spreadsheet, which can be fed back to theclient to correct.


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Defining Audit Objectives and Data Requirements

What is the Audit Objective?Fixed Assets are long term property used in the production of income. Inaccurate recorddocumentation may imply that there is an issue with the overall validity of transactionsand potentially a significant issue with the process managing assets. Fixed Asset reportingis also a fundamental part of the Balance Sheet; therefore, if asset records are notaccurate or reported correctly the balance sheet will be incorrect.

The overall objective of a Fixed Asset Audit is to validate and test that Fixed Assets areALL accurately recorded and within the appropriate financial period.

Testing objectives include:

l Validation that fixed assets physically exist and that the company have the rights ofownership

l Verification of compliance of the assessment and classification of fixed assets

l Verification of compliance relating to the acquisition and disposal of fixed assets

l Inspection and assessment of the current enterprise accounting costs of fixed assetsimprovement and their repair with contract and economic ways of execution andbased on the accepted company accounting policies.

l Verification of results produced by the revaluation of fixed assets

l Validating Depreciation charges are correctly calculated and recorded.

Planning – What Data is Required?For an effective Fixed Assets Audit the minimum data required is the list of assets heldat the end of the audited year. The data will either be held in spreadsheets, or withina Fixed Assets module of an accounting software or ERP system. For most smaller tomedium sized client's data is held in spreadsheets.

Obtaining and Normalizing the DataThe data can be obtained directly from the accounting software or ERP systems and shouldcontain the year-end position of the Fixed Assets with a list of the transactions which havetaken place within the year.

If the Fixed Assets data is held in spreadsheets, the data may require some data cleansingbefore import. Issues may include, but are not limited to:

l Each category of the Fixed Asset register is held within different worksheets in thespreadsheet. A common issue is where the formats may differ between worksheets.

l The spreadsheet contains header data such as company name, year-end etc…

l The spreadsheet contains subtotals and totals rows in the data


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The solution is often to compile all the data into a single worksheet, ensuring all columnsare aligned and deletion of unnecessary header data has taken place. Any subtotals orgrand totals should be deleted prior to importing into IDEA.

The Key Fields Required in the Data Extract(s)

Year-end Fixed Asset file

Asset Code/Number

Asset Description

Asset Category

Date of Acquisition

Date of Disposal

Original Cost of Asset

Cost Brought Forward

Accumulated Depreciation Brought Forward

Additions in the Year

Disposals in the Year

Depreciation Charge in the Year

Cost carried Forward

Accumulated Depreciation Carried Forward

Net Book Value Carried Forward

(Annual) Depreciation Rate


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Fixed Assets Initial Check

Initially verify that the data imported into IDEA is complete and accurate. One way toeasily achieve this is by using the Field Statistics property which provides an overviewof the imported data fields. This can be used to easily identify any incorrect totals,unusual trends, missing values and incorrect date periods. This allows the Auditor agreater chance of identifying any issues that will cause invalid test results.

Compare the totals obtained from the client and verify that they reconcile to the totalswithin Field Statistics. This can be completed by selecting the Field Statistics propertywithin Properties window.

Any discrepancies should be clarified with the client before proceeding further with theData Analytics element of the Audit.


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Fixed Assets Data Analytics – The Basics

Auditors would normally start with ensuring the data adds up correctly by comparing thetotals of the various imported numeric fields back to the totals in the Fixed Asset registerand check the cross calculations.

Another test that should be completed is to check that the Net Book Value has beencalculated correctly. This will identify any discrepancies in the underlying data especiallywhen spreadsheet based.

This can be completed by appending a new field using Field Manipulation.

Parameter Calculation: (COST_BROUGHT_FORWARD + ADDITIONS_IN_THE_YEAR -DISPOSALS_IN_THE_YEAR) - (ACCUMULATED_DEPRECIATION_BROUGHT_FORWARD +DEPRECIATION_CHARGE_IN_THE_YEAR)

l Field name: Z_NBV_CHECK

l Field type: Virtual Numeric

l Decimal places: 2

l Parameter: (COST_BROUGHT_FORWARD + ADDITIONS_IN_THE_YEAR -DISPOSALS_IN_THE_YEAR) - (ACCUMULATED_DEPRECIATION_BROUGHT_FORWARD + DEPRECIATION_CHARGE_IN_THE_YEAR)

Recalculation of the Depreciation ChargesEstablishing the correctness and appropriateness of the depreciation schedule for FixedAssets is an important auditing task, which reviews the list for reasonableness anddetermines if the account balance on the Financial Statements matches the depreciationschedule.

l Required Datasets: Year-end Fixed Assets Database

l Required Fields: Cost Brought Forward, Depreciation Rate, Acquisition Date,Additions in the Year, Disposals in the Year

l Objective: To recalculate the depreciation charge to determine if the amountsreported are accurate.

l Brief: Appending new fields to recalculate the calculated depreciation charges.

The recalculation of the depreciation charge in the year can be completed by appendingcomputed fields using Field Manipulation in IDEA for the following methods ofdepreciation:

1. Straight Line Method

2. Reducing Balance Method


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Method 1: Recalculation - Straight Line Method

This method allocates an even rate of depreciation every year over the useful life of theasset.

Annual Depreciation Expense = (Asset Cost – Residual Value)/Useful lifeof the Asset

Analytical steps in IDEA:

Recalculating the charge can be completed by appending four new fields using FieldManipulation:

1. Field 1: Recalculation of Cost Brought Forward

This shows costs for the Assets at the beginning of the year with no disposals.

Parameter Calculation: COST_BROUGHT FORWARD x (Annual) DEPRECIATIONRATE

l Field name: RECALCULATION_CBF


l Decimal places: 2

l Parameter: COST_BROUGHT_FORWARD * ANNUAL_DEPRECIATION_RATE

2. Field 2: Recalculation of Additions in Year

This recalculates the depreciation for the inventory additions that have occurredwithin the audit year.

Additions Calculation: Addition cost X (annual) depreciation rate X (months fromDate of Acquisition to year-end/30/12)

l Field name: RECALCULATION_ADD


l Decimal places: 2

l Parameter: ADDITIONS_IN_THE_YEAR X DEPRECIATION_RATE X ((@Age("YEAR_END_DATE", DATE_OF_ACQUISITION)/30)/12)


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Within IDEA, to calculate the number of days, the @Age function is used whichreturns the number of days. This will need to be converted to months, then dividedby 12 to calculate the final year charge.

3. Field 3: Recalculation Disposals

This recalculates the depreciation charge for the disposals that have taken placewithin the audit year.

Disposals in the Year Calculation: Cost Brought Forward X (Annual)Depreciation Rate X (months from start of year to disposal/12)

l Field name: DISPOSALS_IN_YR


l Decimal places: 2

l Parameter: COST_BROUGHT_FORWARD * (Annual) DEPRECIATION_RATE *(@Age(DATE_OF_DISPOSAL ,"20170101")/30)/12)

4. Field 4: Recalculation of the Depreciation Charge

Create a computed field to total the 3 depreciation charge elements which willcalculate the total depreciation charge for the year using the straight-line method.

Total Depreciation Charge: Recalculation CBF + Recalculation Additions +Recalculation Disposals

l Field name: RECALCULATION_DEPRECIATION_CHARGE


l Decimal places: 2

l Parameter: RECALCULATION_CBF + RECALCULATION_ADD + DISPOSAL_IN_YR


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Method 2: Recalculation - Reducing Balance Method

This method charges depreciation at a fixed rate; however, the rate percent is calculatedon the book value of the asset (deducting depreciation from its cost). The depreciationrate is applied on the reducing balance of asset; therefore, the % rate remains the same,but the depreciation expenses reduces gradually.

Analytical steps in IDEA:

To recalculate append the following fields using Field manipulation:

1. Field 1: Recalculation of Assets at the Beginning of Year with NoDisposals

This recalculates the deprecation charge for existing assets with no disposals.

Parameter Calculation: NET BOOK VALUE x (annual) DEPRECIATION

l Field name: RECALCULATION_ASSET_VALUE_BEG_YEAR


l Decimal places: 2

l Parameter: NBV x (annual) depreciation

Net Book Value is calculated as the original cost of an asset, minus anyaccumulated depreciation, accumulated depletion, accumulated amortization,and accumulated impairment. If a NBV field is not included, minus theaccumulated depreciation value from Purchase price.

Revised Calculation: (Purchase Price – Accumulated Depreciation broughtforward) * annual depreciation rate

l Parameter: (ORIGINAL_COST_OF_ASSET - ACCUMULATED_DEPRECIATION_BROUGHT_FORWARD) * ANNUAL_DEPRECIATION_RATE

2. Field 2: Recalculation Additions in the Year

This is the same as field 2 added as part of the straight line method.

Calculation of Additions in the Year: Addition cost X depreciation rate X(months from purchase to year-end/30/12)

l Field name: RECALCUALATION_ADD


l Decimal places: 2

l Parameter: ADDITIONS_IN_THE_YEAR * ANNUAL_DEPRECIATION_RATE *(@Age("20171231", DATE_OF_ACQUISITION)/30/12)


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3. Field 3: Disposals in the Year

This calculates the depreciation charge for assets disposed in the year using thereducing depreciation method.

Calculation of Disposals in the Year: Net Book Value brought forward X(annual) depreciation rate X (months from start of year to disposal/12)

l Field name: DISPOSALS_IN_YR


l Decimal places: 2

l Parameter: (PURCHASE_PRICE -ACCUMULATED_DEPRECIATIONC/F) *DEPRECIATION_RATE * ((@Age(DATE_OF_DISPOSAL, DATE, DATE_OF_ACQUISITION)/30)/12)

4. Field 4: Reducing Depreciation Recalculation

Finally create a computed field to total the three depreciation charge elements.

Calculation: Recalculation Additions + Recalculation asset value beg year +Recalculation Disposals

l Field name: REDUCING_DEP_RECAL


l Decimal places: 2

l Parameter: RECAL_ASSET_VALUE_BEG_YEAR + RECAL_DIS +RECALCULATION_ADD

Interpretation of the Results

Records where the recalculated asset value and book value reported within the balancesheet have variances will require further investigation. Misrepresentation of asset value is


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a popular fraud activity for some businesses who have weakening financial positions. Bymisrepresenting major Fixed Asset values, it can improve a company's overall assetposition.

It is important to bear in mind that a company can adopt different depreciation methodsfor different types of assets, provided that the methods are adopted consistently over theyears. If a company changes depreciation method, then depreciation should berecalculated applying the new method from the date on which the asset is put to use forthe first time.

Documents

Analytics for External Auditors - CaseWare · CA411AnalyticsforExternalAuditors 6 GeneralLedgerTransactionsCompletenessTest 31 GeneralLedgerTests-UnderstandingtheData 39 Test1a:SummaryofJournalEntrybyEmployee/User