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Data Analytics in Auditing

Over the past decades, profession of auditing has gained a great attention mainly after the WorldCom, Enron and other scandals related to auditing. Auditing includes examination of non-financial and financial records of the organizations for establishment of patterns of events and guidelines are being examined for adequate guidance of the procedures and processes in audit to gather evidence and the process is subjective on the auditor’s professional judgement. The engagement of audit includes different stages like audit planning, performance of internal control test, performing risk assessment of the client, collection of evidence.

Technology advancements played and is still playing a very crucial role in the development of the audit profession worldwide. The introduction of advanced technology such as data analytics, artificial intelligence and digitalization have changed the method of auditing processes which improves the output of engagement. The incorporation and development of technology into methods of auditing calls for auditors for widening their scope of knowledge in relation with usage of technology in different stages of auditing. From the previous three decades, auditors are trying to make the best use of the technology available, such as data analytics for conducting risk assessment and understanding the audit clients in a better way.

Data analytics have been around us since a long time in different forms as they are everywhere in the business today. It helps various firms for identifying new opportunities and enabling them to make effective decisions much faster. According to the International Auditing and Assurance Standards Board (IAASB), data analytics for auditing is an art and science of analyzing and discovering patterns, inconsistencies and deviations, and extraction of other beneficial data in the information related or underlying to the subject matter for the required audit by analyzing, sculpting, and visualizing for the planning purpose and audit performance. The usage of data analytics has been evolving all around the world including the UK as there are various benefits provided by using it.

According to PWC, data analytics is the communication and discovery of meaningful patterns in data. As per KPMG, it is an analytical process through which we can extract insights from financial, operational, and other forms of electronic informational internal or external to the firm. EY define data analytics as a technique which is used for exploring plausible relation between non-financial and financial data for deriving greater insight of the firm’s operation and the risks faced by them. Finally, Deloitte mentions data analytics as the practice in which data is used for deriving business performance and strategy which consists of various solutions and approaches from looking backward for evaluating what has occurred in the past to looking forward for predictive modelling and scenario planning.

Data analytics application in the auditing encompasses the usage of software for identification of significant trends and these software’s may be used for basic analysis of data but many internal auditors are still relying on basic spreadsheet applications and tools instead of using advanced analytics tools. In a recent survey from Deloitte, it was found out that two-third of internal auditors use basic analytics tools such as the spreadsheets and only one-third are using the advanced level data analytics tools such as ACL, Teammate etc.

Computer-Assisted Audit Techniques (CAATs)

The use of data analytics in financial statements auditing process has been increasing in the global market of auditing including the UK. Auditors have used computers for analyzation of data in auditing performance since firms first started computerizing their system for accounting. Such techniques were known as Computer-Assisted Audit Techniques (CAATs) which were used typically for analyzation of sets of identification of data meeting specific traits for the audit team to test further and the CAATs could be tailored specifically to the firm that is being audited which required investment of time and were not used widely among the auditing firms. Data analytics are similar to CAATs which are more evolved and have enabled the auditors for using techniques in visualizing data and using it throughout the whole auditing process.

Continuation in development of technology mean now it is much easier for auditors for capturing, transforming, analyzing the datasets entirely allowing interrogation of all the transactions within the population. The main characteristic for development of usage of data analytics is the rolling out of standard data analytics tools and techniques, tested, and coded by expert workforce and implemented with central support which means its use if more reliable, consistent, and efficient. Using many of the data analytics tools employs techniques of data visualizations. Information graph, plots, and graphs can be used for placing the data to visual context which will enable outliers, correlations, trends, and patterns that might be unnoticed in the data which is based on text to be recognized more easily.

Traditional Vs Modern Methods of Auditing

The traditional methods of audit involved obtaining samples of data and reporting according to those whereas, data analytics allows the auditors to work with 100% transactions within a population of data. As a result, auditors will be able to derive a combination of value and quality from its usage. Data analytics can be applied in the entire audit process starting with planning the audit to evaluation of results. It is can exploratory or can be used for performing the audit procedures such as substantive procedures and test of controls which make it easier to conduct all the procedures.

In the planning process, data analytics is used for assessing the risk engagements for deciding if they can accept the client or not. They can gain an improved understanding of their data by comparing it with the historical or industry data. In the next step of risk assessment, they can use data analytics to gain better understanding of client’s industry, environment and business which will help them in assessing the risk of material misstatement. Data analytics will help in combining the files of database that will be analyzed and can be viewed from different angles which make it a great tool for forensic auditing and detection of fraud for example, the transactions with high values can be linked to segregation of the thresholds of payments.

In the process of evidence gathering, the data analytics can be very effective for identification of anomalies and improve the quality of evidence, for e.g., by detecting duplicates and evaluating data of accounts receivables and accounts payable by analyzing purchasing to payments, testing accounts receivables and search if any payments are duplicated, etc. Anomalies are the instances in which the data of the client does not match the expectation of the auditor as per their knowledge regarding the client’s business.

Another benefit of using data analytics is that it can be used for continuous auditing which is a process of gathering evidence for audit as a reasonable basis for rendering an opinion on the presentation of financial statements which are prepared without paper using real-time system for accounting. Data analytics has the ability of building a data base of knowledge about each engagement which can be shifted from one year to another; for e.g., auditors test and collect transactions samples and use its judgement on areas which are difficult for testing like estimates of management.

Moreover, it helps in reduction in the cost for auditing as the auditors can perform the test in less time compared to the manual testing as all the data can be assessed from central systems by which they get the data much faster. Second, as mentioned earlier, with the help of data analytics, the whole data can be tested instead of selecting samples which will lead to more assurance, for example, it may be missed by the auditor that many of the transactions of the firm occurred in weekend when the workings days are only from Monday to Friday which will be easily captured by data analytics as “Unusual Days” and third benefit is that the financials audits quality will be improved as continuous auditing will allow the auditor at understanding the client’s environment and business.

Auditor can easily manipulate the data for audit testing. For e.g., sensitivity analysis can be performed on the assumptions of management. Other benefit is that as the large volumes of data can be processed very fast and analysis can be provided to the auditors on which their conclusions are based on, this will save up their time and allow them to focus on risk and judgmental areas. Data analytics uses the external and non-financial data for forming better audit planning and those areas requiring judgement are audited effectively such as going concern or valuation.

Professional Audit firm’s perspective

KPMG which is a global audit firms that provides audit services and have invested highly in data analytics. It stated the increase in their audit quality with the help of data analytics as they test complete data population and identifies the firm’s reasons behind anomalies and outliers. EY is a UK based first which operates internationally as well and audits firms such as Apple, Starbucks, etc. EY have said that the recent advancements in technology have given them an opportunity for rethinking the ways of executing audit and it provides them all the benefits that are being discussed above. They can better identify operational business risk, fraud and financial reporting and can tailor their approach and provide more relevant audit report.

In 2015, State of the Internal Audit Profession Study stated that PWC have revealed use of data analytics in their audit functions and mostly in fraud managements in which 48% are using analytics for it and the rest 33% are planning to do so. They have reported that these tools have become more advanced have helps firms to generate more information. Some of the main benefits of data analytics were highlighted by Deloitte in 2016 report which stated it provides faster, better quality and cheaper audits.

As mentioned above, using data analytics will have a positive impact on the audit, but it is attached with certain conditions. The standards of audit must be adapted as over the last 50 years, auditing has been performed in the same way and the basic rules for conducting audit were set many years ago. The challenges of data analytics for auditing usually fall in three categories which are expectations of the financial statement users and the regulators; data integrity, relevance, and availability; and finally, expertise and training of auditors. Although, new systems for auditing are being implemented which will need the audit standards to be changed for efficient usage of these systems. However, there is not any guidance provided to the auditors regarding the usage of data analytics. The auditors will need to improve their skills and knowledge as they need to have enough knowledge for usage for information technology, machine learning methods, etc. for creating a technical characteristic of auditing.

At some cases, auditors may find it inconvenient to use data analytics for auditing as it may be difficult to extract the data required which is because the data may not be available at the client or the client’s IT environment may be differently set-up which will make it difficult for the auditor for obtaining the data required. Besides, application of data analytics for auditing requires a huge investment and the auditor will have to make a cost benefit analysis for analyzing if using data analytics will be profitable. Thus, it is very costly and time consuming and there is always a certain amount of risk involved even if the auditor uses the data analytics at a new client by thinking of the payoff in the coming years. This kind of investment includes risk and cannot always be profitable.

As mentioned above, a large assignment will need to include different departments which will usually consist of the audit and IT department. The IT department will be required for extraction of information from the system of the client and building of queries for the process of analysis as auditors are not capable of dealing with IT. Most of the times, the IT experts deliver extra material will is not useful and will make the auditor to spend time on things that are irrelevant. There is also a high risk of confidentiality and data privacy involved as storing or copying client’s data can be misused by the companies or if their security is weak, it can be hacked.

Inappropriate or insufficient evidence can be retained on file because of failure in understanding or documenting the inputs and procedures fully. For e.g., sheet shot of the results file of an auditing procedure which was performed by using data analytics might not be recording the detail and input condition of the testing and other drawback is that it leads to issues of practice management related to data accessibility and storage. The data is required to be held for several years that should be in a form that can be retested as this data is usually in a very large volume which will require the firms to spend on hardware for supporting its storage or they will have to outsource data storage that composites the risk of privacy issues and lost data.

Another major drawback which firms all over the world face is how the data analytics will be viewed by the regulators and investors. In previous years, there used to be an expectation gap between what is expected by outsiders for the auditors versus what auditors are providing according to standards and this gap is occurred when it is believed by the users that the financial statements provided by auditors are 100% fairly stated whereas the auditors are only provided a reasonable assurance based on sampling method. When the auditors are using data analytics, there is possibility that the users of financial statements and board of directors may hold auditors to a higher standard of detecting fraud and misstatements in financial statements. for example, under the traditional method, auditors could defend themselves for not covering a fraud if the selected sample does not include the smoking gun which indicates the fraud. In addition, as the data analytics focuses on the non-financial data and the regulators are fearing the possibility that the less attentions may be paid on auditing and more on non-audit services.

As per PWC Middle East, data analytics has various benefits and have identified it as an integral part for auditing to bright insight, robust and value finding efficiently, but they have also identified that it comes with several drawbacks which is the information presented cannot always be trusted and more is spend on checking data other than using it for deriving decisions. RSM Audit UK LLP mentions that the main ethical issue of using data analytics is confidentiality and for auditing internationally, sharing of data between different jurisdictions may lead to legal challenges.

The CEO of Grant Thornton in the UAE have stated that as the accessibility of data is being increased with data analytics, there is still requirement of human intervention for filtering and communicating the data. He has further stated that human input has still not been replaced by artificial intelligence and machine learning which states that there is still requirement a human to be involved to perform the process effectively even with the use of data analytics and need to form their own opinion.

Final thoughts

Thus, this essay highlighted the benefits and drawbacks of data analytics. Data analytics have been experienced by auditors an effective and efficient tool and the main reason for this is their ability to test the entire population which gives them more assurance and they can find anomalies more quickly. This tool is applied within the procedures of auditing. Auditors are able to conduct an in-depth investigation and its makes auditing easier and faster than using the traditional method. As a result, the audit report presented will be more reliable and accurate. It also comes with some drawbacks related to privacy, confidentially, compatibility, storage of data, irrelevant data, high investment requirement and the skills required for operating it. The understanding for use of data analytics by internal or external auditors is still limited as there is no specific guidance provided. Irrespective of the drawbacks, it has still been implemented in majority of the auditing firms include the Big 4 auditing firms. Altogether, data analytics has been experienced by auditors as a good technique and is emerging among the auditing firm. Thus, negative aspects are exceeded by the good ones, but there is still room for development.