Introduction – Going Concern Assessment

Research shows that the going concern assessment is one of the most important challenges within the decision-making process of companies [1]. Title 9, Book 2 of the Dutch Civil Code lays down the legal rules with respect of the financial statements and the management report. The law stipulates that the management should value the assets and liabilities under the going concern assumption, unless serious doubts concerning the continuity exists (2:384 paragraph 3 of the Dutch Civil Code). When drawing up financial statements, the going concern assumption is therefore leading. The auditor assesses this going concern assumption in both statutory and voluntary audit engagements, but also in review engagements and compilation engagements [2]. The corresponding rules are laid down in ISA-2400 & NV COS 2400 and ISA-4410 & NV COS 4410 respectively [3][4]. 

Past experience shows that, despite the fact that an unqualified audit opinion has been issued, organizations can still go bankrupt in the foreseeable future. An example of this is the inadequately evaluated going concern assessment of Imtech in 2012 [5] or the unseen irregularities within Steinhoff in 2017 [6]. In the latter case the auditor was accused by the Dutch Authority for Financial Markets (AFM) of not obtaining sufficient and appropriate audit evidence to identify the fraud [6]. In addition, the auditor’s unconscious biases, caused by among other things client relations and confidentiality, do not contribute to an independent going concern assessment [7].

Method – Forecasting algorithms

The going concern assessment is still largely dependent on the professional judgement of the auditor, who must assess whether management has made proper considerations. Research shows that technology can contribute to an improvement of audit quality [8]. By automating the going concern assessment, more time and resources can be allocated to the interpretation of the going concern analysis. This maximizes the dual aspects of audit quality: independence and expertise [8][9].

To support the individual and personal professional judgment of the auditor an additional automated going concern assessment, a more direct source of information with possibly higher reliability could provide a solution. Automated forecasting of figures and numbers is possible with the use of forecasting algorithms. An algorithm is an instruction in the form of code that can be used, among other things, for forecasting time series [10]. Similar to previous research we apply PyCaret to test multiple algorithms in concurrence [11][12].The use of PyCaret leads to an automated going concern analysis that is less dependent on the individual and personal professional judgment of the auditor, thus a possible improvement of uniformity can be realized.

To obtain the source data, the existing period balances were exported from Exact Online for eight to thirteen years, depending on the administration and availability. After cleaning and merging the data, the individual conditions (e.g., current liabilities, equity, sales, etc.) were filtered, leading to a dataset per administration per condition per month. In which the word conditions refers to the various data points that can be used to assess the going concern of an organization.

The dataset per administration per condition per month for the available years, is then presented to PyCaret’s regression module (v 2.3.2). The regression module is a supervised machine learning module used for estimating the relationship between a dependent variable and independent variables. PyCaret uses different mathematical models translated into similarly named forecasting algorithms to see which model makes the best predictions. Each forecasting algorithm is tailored to specific situations, such as seasonal data. In addition, each forecasting algorithm works with its own assumptions and interpretations, resulting in different forecasts [10]. By comparing these predictions with each other and the actual realized figures, a complete picture is generated.

In doing so, each forecasting algorithm works with its own assumptions and interpretations, resulting in different forecasts. The model that best fits the administration is shown at the top and the model with the least fit is shown at the bottom.  The predictions and the true value of the top three are then stored in one file and compared. To assess the accuracy of the predictions, the (percentual) deviations per month and at the total level were calculated from these real figures. The overall structure of the used algorithms in shown in figure 1.