Improving Auditing Solutions

With technological advancements that allow users to access large data volumes, there is now an opportunity to resolve many problems linked to inefficient information. In many cases, auditors may find it beneficial to predict the occurrence of complex situations and act before they take place. Because of this, the predictive audit is a technological solution targeted at enhancing audit procedures through the use of machine learning and overall automation. The predictive audit has been developed on the basis of advancements in data mining and analytics over the last ten years. It implies the examination of transactions’ validity prior to their execution by comparing them to timely normative models. This approach allows auditors to spot potentially complex and problematic transactions without letting them go through. Thus, there is an opportunity to manage, resolve, and investigate any problems without the need for reversing flagged transactions.

As analysts start implementing new solutions to enhance their work, they find out that there are multiple areas and procedures that can be improved with the help of machine learning and such technologies as predictive audits. The benefits of such solutions are the following:

  • The abundance of data is stored neatly in a digital format. This allows for the better organization and management of data used for audits. Not only does it save time but offers auditors more resources to be efficient in their work.
  • The adoption of enterprise resource planning (ERP). With the adoption of such systems, auditing companies will have unlimited access to models used for forecasting.

Since the benefits mentioned above show that machine learning and other tech-based solutions enhance the auditing work significantly, there is a range of specific areas that can be improved with their help. The list of these areas is presented below:

Financial distress modeling. This area can be improved through the use of data mining techniques that allow auditors to identify and predict financial failures (distress) of audited companies. Such techniques are also beneficial for helping auditors out when it comes to performing concern evaluations. There are multiple examples of researchers using data mining for forecasting financial distress and revealing the practicality and feasibility of these models. Particular effectiveness was noticed in the context of small and medium-sized businesses because they are the ones to be the most likely to be financially affected by unpredicted emergencies and disasters. Specific benefits of financial distress modeling include the following:

The opportunity to use predictions over large periods of time and not only one-year predictions;

  • Enhanced abilities of internal auditors to review the information and have more time for their work;
  • Supplementing the opinions of internal and external auditors in regards to the integrity of forecasts and the provision of valuable information to the head management of companies;
  • Having time for making important strategic decisions for reducing the impact of financial distress.

Financial fraud modeling. Similar to financial stress modeling, data mining tools can be used by auditors to predict risks of financial fraud. This risk poses many problems for the auditing process, and addressing them may make significant improvements. The high rates of financial fraud occurrence have encouraged researchers in the field to consider when applying big data techniques to detect, predict, and prevent fraud. By researching different methods in which financial fraud modeling can improve the auditing process, scholars discovered that meta-learning (a special type of machine learning combining the outputs of different techniques in a self-adaptive way) was the most effective way in comparison to other approaches. Key aspects and benefits of financial fraud modeling include the following:

  • Approaches used for conducting financial fraud modeling can range from one situation to another, especially since there are different capabilities of auditors in terms of the use of technologies;
  • Neural networks, decision trees, meta-learning, and a variety of other methods have been used by auditors for financial fraud modeling;
  • Financial fraud modeling is useful for internal auditors to draw the management’s attention to problem areas that need further investigation;
  • In the context of forensic accounting, financial fraud modeling is a useful tool for determining the probability of fraud occurrence for providing corroboration within an investigation.
  • Stock prediction and quantitative modeling. In addition to financial fraud and distress modeling, the third aspect is targeted at making stock market predictions and other procedures associated with quantitative modeling. This area is especially relevant for offering investment advice to investors and business managers. While there are no direct connections to auditing, it should be mentioned that relevant advice can be gathered from the way in which various big data and machine learning techniques will be applied in this sphere.

Auditing as a process. Auditing as a profession has been slow to implement big data techniques and use them to professionals’ advantage. There is major evidence to suggest that if auditors began using big data, they would improve their efficiency significantly. The following is the list of reasons why the auditing profession as a whole, and not only forecasting processes may benefit from the use of big data and other newly-developed technologies:

  • Big data analytics changing the entire process of auditing through improving financial statement audits;
  • Improved of education of professionals due to the increased use of new technologies within the sphere;
  • Better alignment with the practice of auditors’ clients. As data-driven approaches to business are becoming more frequent, there is an increased need for auditors to keep up to date with customers whose financial information they review;
  • There has been an ongoing trend for more and more companies inquiring about auditors’ use of data analytics, and the most advanced businesses expect to see such solutions used in the audits of their information;
  • Existing auditing standards emphasize sampling, presentation, and aggregation while big data is targeted at analyzing a variety of processes that generate data;
  • Such processes as population testing possible through the application of big data add value to the auditing profession as well as clients with whom they work. Therefore, there is a call for the change in standards in the profession overall;
  • Behavioral effects of big data technologies on the judgment of auditors are also positive, with professionals being less worried about such problems as data overload, pattern recognition, information ambiguity, and relevance;
  • Complementary sources of evidence for the function of an audit, and the improvement of audit sufficiency, relevance, and reliability.

Overall, the implementation of predictive analytics, machine learning, AI, and other technological advancements is seen to enhance the auditing profession. Auditors will be able to make useful financial forecasts, manage data more efficiently, and educate themselves as professionals when it comes to the use of machine learning and big data.

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