Value of IDEA and ACL in Accumulation and Analysis of Data for Forensic Auditor and Accountant - Literature review Example

The Value of IDEA and ACL in the Accumulation and Analysis of Data for the Forensic Auditor and Accountant Insert Date The Value of IDEA and ACL in the Accumulation and Analysis of Data for the Forensic Auditor and Accountant Introduction Computer-Assisted Techniques (CAATs) like the Audit Command Language (ACL) and the Interactive Data Extraction and Analysis (IDEA), are some of the most important elements of the forensic auditor’s toolkit. The forensic auditor needs these data analysis tools given that his main objective is to find out whether fraud has or, has not occurred in the organisation (Rittenberg, Schweiger and Johnstone 2008). Furthermore, he is to get the perpetrators to confess in case fraud has been detected. This paper discusses the value of software packages such as IDEA and ACL in the accumulation and analysis of data for the forensic auditor and accountant. In the viewpoint of Rittenberg, Schweiger and Johnstone (2008), forensic auditing is often carried out for purposes of investigating and prosecuting criminal activities like; embezzlement of funds, corruption, bribery, asset misappropriation, fraudulent disbursements and other forms of fraud. The application of information technology has helped forensic auditors in the process of gathering evidence and analysing data in line with reducing or totally preventing the occurrence of fraud within firms. Software Packages in the Accumulation and Analysis of Data (IDEA and ACL) Coderre (2009) argues that information technology is all-pervasive and very essential to auditors who are required to analyze information and raw data, and give a report on them. The use of information technology has become true and necessary in instances where most of the information is accessible only through the use of a computer. Turley (2005) on the other hand argues that the application of computer technology in auditing ids not a modern day thing but arose in the early years as a result of expediency. This was motivated by the fact that audit clients were capable of using computers to process their financial data and information. This access made auditors totally incapable of auditing the systems of their clients more effectively and efficiently unless they also employed a similar technology. Other sources suggest that the increased use of technology in auditing could be because of the constant downward pressure on audit fees that was experienced in the 1980s and 1990s (Turley 2005, p. 256). Audit firms at this time period were motivated to look for new ways of improving the effectiveness and efficiency of their audit work to counter this downward pressure. However, there did not appear to be any significant change in the auditing techniques which could have accomplished sought-after gains. Consequently, no systematic attempt to use information technology within the existing auditing framework was experienced. Towards the end of the 1990s, the larger audit firms introduced information technology to replace and enhance some of the auditing tasks that were performed manually like; accounting standards and Companies Act checklist, the use of templates for analytical review, and the preparation of standard documentation among others (Turley 2005) Another reason that could have motivated the use of information technology in auditing is the desire by the audit clients that the audit quality be made clear and visible to them. In the perspective of Turley (2005), examples of this visibility included management letters prepared by the auditor and addressed to the board of directors of the company and the input-criteria like the attire of the audit staff among others. Additionally, the highly visible utilization of computers by the audit staff was viewed as an additional way through which the audit firms could improve their reputation and prestige. Coderre (2009); in line with the use of information technology in auditing, claims that a large variety of micro-computer based audit tools have been in existent for quite along time. This author further claims that these audit tools have had significant impacts on the function of audits. These tools include spreadsheets, databases, presentation graphics, and more. Majority of the audit organizations today are implementing software packages in the development and maintenance of their audit universe. These firms are also conducting risk assessments in their audit coverage planning process, scheduling and managing audit resources, and improving the ability of their auditors to make use of and share information. Additionally, automated tools are also being used in tracking audit-related issues, and in monitoring follow-up on audit recommendations. The list of and types of computer-assisted auditing techniques (CAATs) continues to grow in complexity, number, and utility. These types of tools encompass a variety of software packages and programs that have been designed to; assist auditors perform their audits, report the results of their work, and not just perform a data analysis (Basu 2009). These tools include software for text search and retrieval, database creation and manipulation, flow charting, electronic working papers, and telecommunications among others. CAATs have been defined as techniques that use the computer as a tool for conducting audits in a bid to enhance the efficiency and effectiveness of the audit procedure (Basu 2009). Puttick, Esch and Kana (2007, p. 492) describe CAATs as computer applications and data analysis tools that the auditor utilizes as a component of the audit process for purposes of processing data of significance to auditing. This data is mostly contained in the information system of the entity being audited. The data may be transaction related information on which the auditor intends to perform substantive procedures or tests of control, or they may be other data types like security files that relate to accessing program libraries. The auditor can make use of CAATs to review and acquire evidence concerning the appropriate operation of the programs used by the client and correctness of the data they have generated. The nature and types of CAATs that organizations have commonly used include; package programs, utility programs, system management programs and purpose written programs (Puttick, Esch and Kana (2007, p. 492). Package programs are the category of generalized programs that have been designed to carry out data processing functions like; reading data, carrying out calculations, selecting and analysing information, creating data files and reporting them in the auditor’s specified format. Package programs include software packages like; DIAL, SAS, ACL and IDEA (Puttick, Esch and Kana 2007, p. 492). The purpose written programs carry out audit tasks in certain specific situations. The utility programs are used by entities in carrying out common data processing duties like creating, printing sorting files. The system management programs on the other hand are enhanced productivity tools that are part of a sophisticated operating systems environment, like code comparison or data retrieval software (Basu 2009). IDEA (Interactive Data Extraction and Analysis) and ACL (Audit Control Language) have been defined as specialized CAAT programs that assist auditors in selecting, analyzing, and reporting audit findings (Swanson 2010, p. 274). These programs also provide functions and libraries of prewritten queries that assist in sampling and examining data sets. ACL is mainly used in data extraction and analysis, and in the detection and prevention of fraud. This auditing tool operates by sampling a large data and using it to discover irregularities or patterns within transactions that could show fraud or control weakness. IDEA on the other hand is a package program; or a generalized audit program that is PC-based and allows auditors, accountants, and financial managers to extract, view, sample, test, and analyze data from any system. IDEA program was first released in 1994 and its Windows compatible version released a year later in 1995 (Swanson 2010, p. 274). The Windows compatible version could not effectively compete with ACL Windows version given that it was not fast as ACL and ACL had been aggressively marketed. Later in the year 2002, vigorous innovations in IDEA enabled it to provide the highest overall satisfaction to its users in line with CAATs programs. Presently, IDEA has a lot of important features that other CAAT programs including ACL lacks. IDEA and Forensic Auditing Forensic auditing has been described as the mix of conventional accounting, auditing, and pecuniary detective work (Beretz 2010. P. 181). According to Basu (2006) forensic auditing has a main objective of relating audit findings through gathering legally tenable evidence which shall enable the lifting of the corporate veil is lifted to identify fraud within the company. Basu (2006) further purports that forensic audits are carried out by the examination of legalities more so, through putting together the techniques of propriety, investigative, regulatory, and financial audits. This blending is carried out with a main objective of finding out whether or not the true business value has been reflected in the financial statements, and whether any fraud has taken place. Technology has been used in forensic auditing like in instances where complex data analysis techniques are needed to flag down areas that require more investigations. Forensic auditing avails managers with toolsets that they can use to detect and carry out investigations on the various whiter-collar financial improprieties and the inappropriate and uneconomical use of company resources (Salinger 2005, p. 331). In most instances, as the structure and controls of companies become more complex, the scope for employees with increased specialized knowhow of functionality of control systems, and how to by pass them also increases. According to Manning (2011), superior forms of audit have usually been employed in companies after major control breaches have been revealed. With forensic auditing, company executives will be able to identify the various weaknesses and vulnerabilities in financial control. The major advantages of forensic auditing include the act of strengthening the company’s control mechanism, protecting the business against any financial crime whether they are the catastrophic one-off events or smaller scale ones (Manning 2011). Additionally, forensic auditing helps protect companies from long-term damage of reputation caused by the much publicity that is associated with insider crimes. Lastly, forensic auditing improves efficiency in a company by identifying areas of waste. IDEA has been used by forensic auditors and data analysts for quite some time. According to Nigrini (2012), this versatile software has the capability of importing data from, and exporting data to a large number of formats. This functionality includes reading, displaying, analyzing, manipulating, sampling or extracting information from data files of nearly all sources ranging from mainframe to personal computers. It is only the processing power of the host computer that usually limits the size of files this program can handle. The use of IDEA in forensic auditing is motivated by its many powerful, efficient, and effective built-in audit routines. These audit routines include the primary, superior, and associated Benford’s law tests (Nigrini 2012, p. 318). Probst, Hunker and Gollmann (2010), argue that IDEA and other fraud detection tools like ACL operate by analysing data that has already been stored and processed by the use of a number of information systems. What makes IDEA effective in fraud detection above other fraud detection programs like ACL, Active Data for Excel, Excel, and Access; is that it has numerous data analysis features. These features include functionalities like aging, appending/merging of two files, cross tabulation, calculated field/function creation, digital analysis/Benford’s law features, duplicate identification, export of files, extraction/filtering of files, identification of gaps, indexing/sorting and joining/relating of files among others (Probst, Hunker & Gollmann 2010, p. 79). Value of IDEA in the Accumulation and Analysis of Data for Forensic Auditing The value of IDEA in the accumulation and analysis of data is best illustrated through its features mentioned above. Detailed application and value of IDEA is seen in sort and field statistics, summarization, stratification, extraction or analytical review, and gap detection or duplicate invoices. In the opinion of Albrecht et al. (2010), IDEA often works in a process of three steps which are importation of data, analysis and review of results. The importation of data stage involves the transfer of data from almost any source ranging from PDFs to ERPs. The analysis stage involves extraction, sorting, searching, grouping, calculated fielding, stratifying, summarizing, aging and gapping among other functions. The review results stage involves the publishing of results as pivot tables, charts, reports, exports, and project overview. 1. Sort and Field Statistics Sorting of files is done in descending or ascending order wheras the field statistic calculates the various statistics within selected numeric fields. These statistics may be total negative items, positive items, average balances, and so on. Hartigh (2010) asserts that field statistics usually avails useful information concerning the numeric, time fields and date statistics within the IDEA database. The statistics for several date, time fields and numeric are displayed at a go, making it easy to carry out comparison. The numeric statistics, which are the totals, maximum and minimum, and averages; serve a vital function in the reconciliation of the imported data with the original data found on the system of the host computer. The date and time statistics on the other hand ensure that the date of the data imported is for the correct audit year. This is seen especially when cut-offs are being tested or when period analysis of the date is being conducted. A print screen illustration of field statistics is shown below Field Statistics Field statistics (IDEA 2006, p. 20) 2. Summarization Summarization is the accumulation of numerical values based on specific key fields, like in the case of summarizing travel and food expenses by employee for purposes of identifying anomalously high payment amounts. In the opinion of Albrecht et al. (2010, p. 179), summarisation goes hand in hand in hand with stratification as its extension. Instead of producing a number of sub-tables; like in stratification, summarization runs a single or several calculations on the sub-tables to come up with one record summarizing each of the case values. Using an example where an investigator wanted to find out the average price of each product in a provided purchasing table, a summarization function would first of all stratify this data set by product. After the stratification, the summarization function, would calculate the average price for each sub-table and then return a single table containing a set of two columns. The first column is the product name, and the other column, the average price. Summarization often takes different forms in different applications, where the basic summarizations produce single results table with a single record per case value as shown below. Summarization Summarization (IDEA 2006, p. 63) 3. Stratification Codorre (2010) defines stratification as the splitting of complex data sets into case-specific tables. In most instances, databases often contain cases from a variety of vendors, companies, employees or customers. When these cases are analyzed together, it can be impossible to see the meaning of the data. For example, a purchasing data set is provided which contains purchases for push brooms, cleaning supplies, uniforms and other numerous items. If it is assumed that brooms cost fifteen dollars and uniforms eighty-five dollars, doing a simple average across these sets of purchases can make some sense. For clarity purposes, this data set must be stratified into several sub-tables before analysis can be carried out. In this push brooms example, stratification will separate the data into a table for push brooms, a table for cleaning supplies, a table for uniforms and so on. In instances where the data sets are numerous, stratification results into thousand of sub-tables which are easily analyzed in IDEA and ACL. Stratification can also be used to count the number and the dollar value of the records of a population that fall within set intervals among other functions. A good example of stratification print screen report is illustrated below. Stratification report Stratification report (IDEA 2006, p. 70) 4. Extraction (Analytical Review) Extraction of data in IDEA is the method of isolating desired records from the database in which copies of the desired records are recorded to a separate database. According to Singleton et al. (2006), extraction mostly uses the “if” or “where” statements, for example, when extracting balances that are above a given predefined limit. Furthermore, in IDEA the original data that has been extracted from the client’s system, cannot be deleted from, modified, or have anything added to it, since it is a copy of the client’s data. This makes it impossible to corrupt the client’s audit data. Extractions are also the most frequently utilized functions in the IDEA program. Kranacher, Riley and Wells (2011) define extractions as exception testing methods that are used in identifying items that satisfy certain characteristics like payments of over ten thousand dollars or transactions carried out before a certain date. The extraction criteria are entered using the equation editor function which allows the creation of simple or complex extractions through the use of an intuitive interface. Furthermore, fifty separate extractions can be performed using one pass through the database. An illustration of a set of extracted data is provided below Extracted Data Extracted data (IDEA 2006, p. 50) 5. Gap detection (Duplicate Invoices) Gap detection involves identifying missing and duplicate invoices within the database. According to Fujita and Marik (2009), gap detection involves the application of the duplicate key extension function which is also known as the same-same-different to identify duplicates in instances where specified fields are different. Gap detection in invoice numbers follows the Benford’s Law requirement which states that the digit sequences and digits in a specified dataset must follow predictable patterns (Fujita & Marik 2009). In this respect the duplicate key extension function counts digit occurrences of values within the database and evaluates the totals with the predicted results in line with Benford’s Law. An illustration of a gap detection report is given below. Gap Detection Report Gap detection report (IDEA 2006, p. 64) Other Values and Shortcomings CAATs especially ACL and IDEA provide auditors with the ability to carry out analyses on large amounts of data. It has been argued that electronic analysis of data can be used to test one hundred percent of the information for the period being audited (Fujita and Marik 2009). Additionally, these programs enable the testing of anomalies or interrelationships among the data elements since they utilize large amounts of data. In relation to fraud detection, these programs are effective and efficient in identifying policy and procedure non-compliance or even fraud within the organization. Additionally, they have been used in conducting continuous auditing which has become a trend among many of the audit organizations today. Despite the effectiveness of CAATS in analysing financial data for purposes of forensic auditing, they have a number of shortcomings. First of all the CAATs auditing software often require the auditor to read the client’s data files in which he is to locate the information that is necessary to carry out the different auditing tasks (Fujita & Marik 2009). This aspect of reading the client’s data files requires training which is an additional cost to the forensic auditor. In addition, the auditor has to ensure that these programs are adaptable from one computer to another. Conclusion In conclusion CAATs like ACL and IDEA software programs are vital tools for the forensic auditor in the world today. Forensic audit involves determining whether fraud has taken place in an organisation and getting the perpetrators to confess. The use of technology in auditing enables the testing and scanning of a large variety of data, and because of this it has become common with forensic auditors. IDEA has proved superior to ACL since it has additional features that enable the analysis of data and the identification of anomalies and non-compliance to policy and procedures which indicate the presence of fraud. Additionally, IDEA enables the forensic auditor to copy files and data from the clients system, which can act as evidence of fraud in case it is detected. Both ACL and IDEA have proved useful in the extraction and analysis of data; a mechanism that has made easy the fraud detection process. In this paper, the value of IDEA to forensic auditing and accounting has been seen in its features like summarization of data, stratification of data, extraction of data, gap detection, and sorting among others. Despite these benefits, the use of IDEA in forensic auditing and accounting requires skills like reading and interpreting clients’ data files. This skill requirement represent only a minor shortcoming which cannot outweigh the overall benefits and value that companies and audit firms have derived from the application of IDEA in conducting forensic audits. These benefits can be seen in the modern-day trend of carrying out continuous auditing for purposes of detecting fraud early and preventing their future occurrences. References Albrecht, WS, Albrecht, CO, Albrecht, CC & Zimbelman, MF 2010, Fraud examination, South-Western Cengage, Mason, OH. Basu, SK 2006, Auditing: principles and techniques, Dorling Kindersley, New Delhi. Basu, SK 2009, Fundamentals of auditing, Dorling Kindersley, New Delhi. Beretz, P 2010, Approaches to enterprise risk management, Bloomsbury Information Ltd, London. Coderre, D 2009, Internal audit: efficiency through information, John Wiley and Sons, Hoboken, NJ. Coderre, D 2010, Computer aided fraud prevention and detection: a step by step guide, John Wiley and Sons, Hoboken, NJ. Dickerson, MO, Flanagan, T & O’Neill, B 2010, An introduction to government and politics: a conceptual approach, 8th edn, Nelson Education, New York, NY. Fujita, H and Marik, V 2009, New trends in software methodologies, tools and techniques, IOS Press BV, Amsterdam. Hartigh, NJ 2010, Faruditing, roadmap to fraud investigation, Kluwer, New York, NY. IDEA 2006, IDEA data analysis software version seven, viewed 26 March 2012, < http://www.bhutanaudit.gov.bt/contents/IDEA/t.htm>. Kranacher, MJ, Riley, R & Wells, JT 2011, Forensic accounting and fraud examination, John Wiley & Sons, Hoboken, NJ. Manning, GA 2011, Financial investigation and forensic accounting, CRC Press, Boca Raton, FL. Nigrini, MJ 2012, Benford’s law: applications for forensic accounting, auditing, and fraud detection, John Wiley & Sons, Hoboken, NJ. Probst, CW, Huner, J & Gollmann, D 2010, Insider threats in cyber security, Springer, Ney York, NY. Puttick, G, Esch, SV & Kana, S 2007, 9th edn, The principles and practice of auditing, Juta & Co, Cape Town. Rittenberg, LE, Schwieger, BJ & Johnstone, KM 2008, Auditing: a business risk approach, Thomson Higher Education, Mason, OH. Salinger, LM 2005, Encyclopaedia of white-collar &corporate crime, Sage Publications, Thousand Oaks, CA. Singleton, TW, Singleton, AJ, Bologna, GJ &Lindquist 2006, Fraud auditing and forensic accounting, John Wiley & Sons, Hoboken, NJ. Swanson, D 2010, Swanson on internal auditing: raising the bar, IT Governance Publishing, Cambridgeshire. Turley, S 2005, Current issues in auditing, Paul Chapman, London. Read More