- CONSTRUCTION AUDITING RISK DETECTION USING MACHINE LEARNING APPROACHES. - Audit report plays a key role in determining the validity of final accounting in the completion of any construction project. - However, the quality of reports depends heavily on the quality of the auditors themselves, whose variety of skill set and bias level could lead to different assessment outcome of the accounting risk level. - This paper presents a method that automatically detects auditing risk using machine learning approaches. - The criteria to assess auditing risks will serve as inputs in the machine learning algorithms, and the output will be the ranking of low, medium, high level of auditing risk. - The proposed two machine learning methods was tested on 80 construction projects in Vietnam and the result shows the high accuracy level of this method in auditing risk detection.. - Keywords: auditing, audit risk detection, neural network, random forest, machine learning.. - The purpose of the audit is to examine and verify the truthfulness of the financial statements provided by the accountant, thereby providing the most accurate information about the financial situation of the organization. - The final product of an audit is a report express the auditor’s opinion about the truthfulness and fairness of the financial statements as produced by the accountants. - The assessment of this audit risk depends heavily on the subjective opinion of the auditor. - In this method, the expert system is considered as symbolic module and artificial neural network is considered as non-symbolic module.. - In [3], Kasman (2010) proposed the method using neural network with back propagation learning algorithm to evaluate credit risk. - In [6] neural network has proposed to classify the credit risk into good vs. - bad consumer groups for the bank. - [7] presented a method of neural network to detect the audit risk in three level low, medium and high level.. - Although there are many research works that apply artificial intelligence in audit and financial, these methods focus on evaluating audit risk in company, where there is statistical &. - In this paper, we present a method of audit risk detection in construction project using two methods of machine learning inclusing neural network and random forest. - other audit projects is that construction projects usually have short execution time and the audit is implemented when the project is finished. - Criteria to assess audit risk will provide the inputs for a multi-layer perceptron neural network as well as random forest and the output are the three level of risk include low, medium, and high of audit risk. - The experimental results show the efficiency of the method. - In the first section, we describe the audit process and how neural network and random forest is applied to audit risk detection. - In the next section, real data will be used to illustrate the performance of the method. - Finally, we draw conclusion of the study and implication for future work.. - Auditing risk assessment. - In construction project, auditing risk is associated with important errors in the final project settlement report. - To control this risk, the audit plan must be appropriately established to detect fraud, risks, and potential problems and also ensure that the audit is completed on time. - The audit risk assessment process is shown in figure 1. - Audit risk detection process. - To assess the internal control system, several factors need to be considered such as model, operating framework and ability of the project management unit. - An assessment of control risk is to check the information of internal control system of the project management unit such as diagram of organizational structure, level of staffs, internal management documents, internal audit works. - Also, the auditor needs to observe activity of the unit and discuss with managers and employees to understand the organizational characteristic, personnel policies, qualification of the managers and employees. - They are Ability and quality of the project management unit (PMU) director, Risk management process, Information/report, Control operations, and Evaluation and Monitor (E &. - The detail of the criterions are shown in [7].. - These values will be the inputs of neural network. - Outputs of neural network are risks, measured on three levels of audit risk as low, medium and high.. - Neural network. - An Artificial Neural Network (ANN) is a computational model that simulates biological neurons and functions in the brain. - The nodes and their inter-connections are similar to the network of neurons in the brain. - Any basic ANN will always have multiple layers of nodes, specific connection patterns and links between the layers, connection weights and activation functions for the nodes that convert weighted inputs to outputs. - The learning process for the network typically involves a cost function and the objective is to. - The weights keep getting updated in the process of learning.. - For the audit risk detection, we have considered them as classification problem. - In this paper, we use a multiple outputs three-layer structure of multilayer perceptron (MLP) neural network. - Two layer feed-forward neural network. - where w ji are element of the weight matrix and b j are the bias parameters associated with the hidden unit. - Also, each variable aj was associated with each hidden unit and then transformed by the non-linear activation functions of the hidden layer.. - The output of the hidden units are then given by. - The z j are then combined with weights and biases of the next layer to produce values a k (2). - For the classification purpose, we consider the logistic sigmoidal activation functions as follow:. - Assume we have the target vector t for input data x, the error of the network, E, is defined as:. - Where y k n is the actual value of k th output unit for the n th input pattern, t k n is desired value of the k th output unit for the n th input pattern.. - The difference between the calculated output and the desired output is back- propagated to the previous layers, usually modified by the derivative of the activate function, and the connection weights are normally adjusted using the Delta Rule. - This process proceeds for the previous layers until the input layer is reached.. - Random Forest. - The Random Forest Classifier is a set of decision trees from randomly selected subset of training set. - It aggregates the votes from different decision trees to decide the final class of the test object. - The figure 3 describes the diagram of the Random Forest.. - The diagram of the Random Forest. - Each individual tree in the random forest spits out a class prediction and the class with the most votes becomes our model’s prediction. - The survey forms for the internal audit includes 46 criterion such as described in table 1. - These criterion have brought to the input of neural network. - The neural network structure here is designated with three layers, two hidden layers and an output layer. - The number of nodes in each layer is selected by experiment, we used twelve nodes for the first hidden layer, eight nodes for second hidden layer and three nodes for the output layer, corresponding three levels of audit risk, low, medium and high, respectively. - Activation function for each hidden layer was rectified linear unit (ReLU), and sigmoid function for the output layer.. - Ability and quality of the project management unit (PMU) director 28 criterions. - The data set was divided into two parts, 70% for training and 30% for testing for two machine learning methods. - The training result of neural network is shown in figure 3.. - However, once the number of iteration increases gradually, the value of lost function in the training set will be smaller than that of the testing function as data in the testing set is less than that of the training set. - Figure 4 shows that the accuracy of the testing set is more than 90%, while the accuracy of the training set is about 97. - The accuracy of the testing set is higher than training set about 2%. - Confusion matrix for Neural Network (left0of two machine learning methods (left: neural network, right: Random Forest). - We evaluate the accuracy of the methods using the ground truth notion of positive and negative detection. - confu-sion matrix for two methods neural network and random forest is shown in figure 5. - The accuracy of the method will be calculated as the percentage of correctly classified samples compared with the total number of samples.. - Similarity, in the random forest, 12 samples of high risk, 10 samples of medium risk and no sample of low risk were classified correctly. - The overall is 94% accuracy for neural network and 60% accuracy for random forest.. - This paper proposed two machine learning methods to detect the audit risk in construction projects. - By quantifying the criterion survey, these variables can be used as inputs to the neural network and random forest to train the model which can be used to detect the audit risk in any new project. - The experimental results show the efficiency of the neural network method. - This method can be applied to information system to quickly detect the audit risk and also recur the work load for auditors. - Eija Koskivaara, Artificial Neural Networks in Auditing: State of the Art, TUCS Technical Report No 509, 2003.
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