What is AI?
Today’s machines have intensified the ability to ease human life by integrating cognitive ability. This ability is made possible through the aid of computers, which, when programmed, implement the act of acquiring, understanding, and deciding. A technology that aims to create intelligent machines, and introduces concepts and tools that mimic human intelligence is known as artificial intelligence (AI).
The common application of AI is machine learning, and with machine learning, the machine gradually improves in terms of performance proportional to the number of examples it goes through. In other words, the machine learns for itself, without being explicitly told what to do. Then machine learning functions result in processing, perception, intelligence, and algorithmic perspective of human-level creativity.
Machine learning introduced various methods and tools to attain the desired cognitive ability, such as search, probabilistic reasoning, statistical learning, neural network, and deep learning. Among these tools, the neural network is the most popular since it mimics the human brain activity and approximates a function to match the expected output for classification or pattern recognition. On the other hand, deep learning uses advanced neural networks, with multiple non-linear processing in both supervised and unsupervised learning. No doubt, AI presents the most advanced tools in intelligent systems; thus, its applications invaded real and practical solutions that support human decisions and eliminate the risk of human errors.
Why is there a need for AI and big data techniques in Auditing?
The emergence of AI is due to the presence of big data, and growing business analytics thus presents an opportunity in the field of accounting and finance to bring standard efficiency on their sets of decisions. Researches of big data in these fields are categorically used for modeling financial distress, financial fraud, market prediction and quantitative analysis, and auditing.
Among these tasks, auditing is not so receptive to the utilization of the measured big data since auditors feared to adopt more sophisticated auditing techniques which are far ahead of client firms. Although human intuition is a cognitive wonder itself, its advanced adaptability and flexibility have its limits; hence, regularly exposed to its own inconsistencies and biases.
Moreover, AI and big data techniques are valuable in the auditing profession due to its rigorous analytical procedure. With AI, innovative ideas are introduced to unburden workloads for practitioners on analytics modeling and quantitative analysis. Also, this would aid regulators on the updating of standards in auditing and suggest best practices and analytical methodology in the audit engagement.
What are the existing integrations of AI in Auditing?
Auditing is the act of gathering, examining, and evaluating the financial data of the organization or individual in an intensive method by an independent third-party expert. The auditors see to it that the financial information provided by the management is true and compliant with the accounting standards, and relevant legislation. Auditors are classified as internal or external, with three identified differences in their activities, namely, appointment, objectives, and responsibility.
The appointment, objective, and responsibility of the external auditors are independent of the company’s senior management and are defined by the state. On the contrary, the management sets the objectives for internal auditors in which they are accountable to.
The analytical techniques used to examine the data for auditing are categorized into audit examinations, regression, supervised, unsupervised, and other statistical tools. The recent integrations of artificial intelligent systems auditing consider IT-based decision audit using expert systems (ES) and neural networks (NN), which are both supervised analytical procedures. These two AI tools for auditing are discussed subsequently.
- Expert System
An expert system is an intensive software-based tool that considers the experts’ point-of-view and expertise in decision-making methods. This system imitates the judgment of the experts about an application attribute and justifies its reasoning understandable to the auditor. The focus attributes of ES are considered in the different phases of audits such as planning, risk assessment, compliance testing, review, and opinion formulation, which provides considerable benefits to the audit profession. In fact, the audit has the most ES systems developed by the accounting firms.
- Neural networks
A neural network (NN) is a tool for classification and clustering of data for estimation, decision, and prediction. This tool is made up of three layers such as input, hidden, and output, integrated with connection weights. These interconnections of the input signals to the processing elements create trends and relationships for future happenings. With this capability, NN is used to develop fraud classification model with financial data as factors and will prompt auditors when the financial statement is fraud. In fact, the NN through logistic regression models are significantly more accurate in identifying fraud than practicing auditors. However, the models are likely more important in organizations that generate large data. Experimental results using neural network models show that auditors’ internal control assessments can be simulated 65% of the time.
What are the challenges of AI in Auditing?
The limitation of AI is established on the data available. When the input datasets in the ES and NN used are insufficient or biased, then the machine learning model created will have some problems. To deduce the result, in this case, it needs higher degrees of confidence. Also, not all tasks on auditing can adopt AI since the platform works well with some degree of repeatability. The cost-benefit study should be done on the AI system in audit to weigh its worth in the audit expectations-performance gap. These opportunities of using AI paves the way for faster-outsourced accounting services, but the human can do many things naturally.
What is the promising future of AI in Auditing?
The benefits of adopting the intelligent system with the costs, design, and monitoring of audit effectiveness will eventually be ironed out in the future; thus, the AI in Auditing will pose interests in more firms. Also, future software developments in this area will tend to include assessing the current level of audit committee’s judgment through artificial intelligent systems. In the future, it is predicted that various machine learning tools will be introduced to the implementation of task for audit risk reduction and other audit-related concerns in a specific industry.
