This is the eighth installment in the Anti-Money Laundering, The Basics series.
The series provides professional accountants with a better understanding of how money laundering works, the risks they face, and what they can do to mitigate these risks and make a positive contribution to the public interest.
Welcome to the third Market Scan from the IAASB's Disruptive Technology team. Building on our previous work, which included the Innovation Report created with Founders Intelligence and discussed at the January 2021 IAASB Meeting, we issue a Market Scan focusing on topics from the report approximately every two months. Market Scans consist of exciting trends, including new developments, corporate and start-up innovation, noteworthy investments and what it all might mean for the IAASB.
In this Market Scan, we explore Artificial Intelligence (AI), which is used in a broad range of technologies across the audit and assurance value chain. This Market Scan provides a high-level primer on Artificial Intelligence as it is one of the most significant and potentially disruptive technologies in audit and assurance. Future Market Scans will build on this by focusing on some of the specific AI-powered technologies highlighted below.
We will cover:
What is AI, including related concepts of machine learning and deep learning
AI use cases in audit and assurance
AI challenges
AI developments
What is Artificial Intelligence?
Artificial Intelligence (AI) is a broad discipline of computer science that refers to the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision making, and translation.
AI also describes a broad range of technologies shown in the diagram below. Many of the technologies we use every day contain one or more of these capabilities; for example, a smart speaker contains speech recognition (to turn our speech into text), natural language processing (NLP) (to understand the request and generate a response) and machine learning (to improve the quality of responses over time).
Overview of AI Technologies
Intelligence in this context is the ability to perceive or deduce information, retain it as knowledge and apply it to making decisions. In computers this is done by analyzing large quantities of data using advanced statistics (including probability analysis) to find patterns and make predictions.
Types of AI
Narrow AI (Today’s AI, weak)
General AI (Future AI, strong)
Applications that model human behavior to perform a specific task or function, e.g., face recognition, speech detection
Currently hypothetical but refers to machines that have full human cognitive abilities
What is an algorithm?
Algorithms are in use all around us, although the term may not be fully understood as frequently. Think of it as a recipe used by computers: a finite sequence of well-defined instructions, typically used to solve a class of specific problems or to perform a computation. An algorithm takes an input (e.g., a dataset) and generates an output (e.g., a pattern that it has found in the data). It is like taking your ingredients and following a recipe to bake a cake.
Algorithms are not exclusive to AI. They are likely used in every audit to complete procedures such as identifying sample sizes or performing data analytics, such as ratio or regression analysis.
What Is Machine Learning?
Machine learning is about using algorithms to guide predictions. The goal of the machine learning process is to create a model, which is based on one or more algorithms. The model is developed through training with the goal that the model should provide a high degree of predictability.
One of the earliest examples of a machine learning system was a computer checkers game created by Arthur Lee Samuel at IBM. Arthur demonstrated how machine learning could work by creating a computer function to measure the chance of winning based on the position of pieces on the board. The computer then used function to determine the move most likely to lead to a successful outcome, that is, winning. The computer learns by using the feedback from playing as its data and using Arthur’s function to guide its prediction model to get to a preferred outcome.
In its simplest form, machine learning requires a five-step process:
Get and organize the data
Choose a model (one or more algorithms)
Train the model (using training data, about 70% of your data set)
Evaluate the model (using test data, about 30% of your data set)
Fine-tune the model and implement
Machine Learning Process
The main challenges with implementation of machine learning are in relation to what data to use (and how to get it) and what model to use, that is, which algorithms to apply.
Machine learning approaches
There are three main types of learning approach used in machine learning; determining which approach to use largely depends on what data you have available.
Supervised learning is an approach used when large amounts of labelled data are available. This enables the technology to learn by comparing its results to the correct answer. There are effectively two types of algorithms that are used within supervised learning—one is classification, where you divide the dataset into common labels. A common form of classification algorithm is called Naïve Bayes Classifier, which is used in text analysis (e.g., for sentiment analysis, email spam detection). It uses frequency and patterns in data to come up with a prediction model based on probabilities.
The other type of algorithm used in supervised learning is regression, which finds continuous patterns in data. A common form of regression algorithm is linear regression, which shows the relationship between variables and uses this to predict outcomes based on inputs, e.g., predicting expected sales per square foot of sales floor space.
Unsupervised learning is used when the available data is unlabeled, so the algorithms used seek to put the data into groups. The most common approach is called Clustering, which is grouping similar items together and then iterating the model to get better results. There are a variety of quantitative methods, i.e., ways of grouping items. Common uses of unsupervised learning are customer segmentation for targeting marketing messages where similar customer characteristics are expected to share similar preferences.
Finally, Reinforcementlearning is commonly used in gaming and robotics, effectively learning through a process of trial and error to get the most effective outcome (such as winning the game or navigating successfully around a space).
Deep learning is a subfield of machine learning that uses neural networks for learning and bear some resemblance to how the human brain works. This way of processing data is more granular than with machine learning and involves more layers of analysis. Although the concept of deep learning has been around since the 1970s, its recent growth is due to the significant advancements in computing power. It is commonly used for speech and image recognition.
An artificial neural network ingests data through an input layer, processes it through a complex network (known as the hidden layer or layers) to provide an output. The word “hidden” in the hidden layer simply refers to the fact that the units in the layer are not visible to external systems and are “private” to the neural network.
Example of a neural network used to identify the number 4 (From Deep Learning with Python by Francois Chollet)
Each of the processing units in the network is called a neuron. A neuron is a container with an input value, a weighting, and a bias (which is a constant). These are computed together and then an activation function is applied, which is effectively a mathematical operation that normalizes the inputs and produces an output that is then passed onto neurons in the next layer.
The weightings along with the bias can change the way the neural networks operate and are used to refine the model to get to the preferred outcome.
The most common types of neural networks are called fully connected neural networks, referring to all the neurons having connections from layer to layer. Other neural networks include recurrent neural networks, convolutional neural networks and generative adversarial networks.
In Recurrent Neural Networks (RNNs), the function not only processes the input but also prior inputs across time. An example of this is with predictive text, as you start to type, different word options are presented based on what the system predicts you are typing.
In Convolutional Neural Networks (CNNs), data is processed in stages from easy to complex with each of the stages being a convolution. CNNs are often used in computer vision applications such as image recognition software.
Generative Adversarial Networks (GANs) are a relatively new but powerful class of neural network used for unsupervised learning. They are made up of a system of two neural network models (a generator and a discriminator) that compete with each other and are able to analyze, capture and copy the variations in a dataset. It is this technology that gave rise to creation of deepfakes; they have also begun to be used by the financial services sector to help with fraud identification.
There are many ways that AI may be deployed to support the audit process.
Audit Planning
Resource optimization using AI technology to analyze staff profiles and experience to bring together the best team for the type of audit engagement
Client acceptance procedures using AI to analyze data from non-traditional sources,such as social media, emails, phone calls, public statements from entity management, etc., to identify potential risks relevant to client acceptance and continuance assessments.
Understanding the entity and its systems, and identifying risks
Using natural language processing and machine learning AI technologies to analyze structured and unstructured information, such as global regulatory notices, industry reports, regulatory penalties, news, public forums, etc., to detect risks of audit relevance
Intelligent document analysis, such as optical character recognition natural language processing and machine learning technologies, to derive insight from unstructured data sources like email, documents, transcribed voice, images, etc. to support understanding of the entity’s information system and related controls.
Quickly and more efficiently understanding the entity's internal controls by summarizing and extracting what has been documented in process documents, emails, articles, and from employee inquiries.
AI-powered behavioral analytics to identify suspicious or unusual entity employee behavior and intent, such as data exfiltration, employee collusion or abuse from privileged users.
Enhancing an audit team's judgments on higher-risk areas of audit engagements by using AI to identify common risks relevant to entity’s industry, regulatory environment, operating locations and other external factors.
Substantive Procedures
AI tools, benefiting from increases in the quality and quantity of available “training” data, can be applied to data sets to algorithmically identify outliers and anomalous data and to perform predictive analytics for use in areas such as testing large transaction populations, auditing accounting estimates and going concern assessments.
Document processing, review and analysis by using optical character recognition to identify and extract key details from contracts (e.g., leases) and other documents (e.g., invoices)
Inventory and physical asset verification procedures through use of drones with computer vision (image recognition) particularly for larger capital assets, such as trucks, or the inspection of large-scale business sites, such as wind farms.
Conclusion Procedures
AI technologies to support auditors’ work on financial statement disclosures enabling easier identification of missing disclosure requirements and non-compliance.
AI technologies to support tick and tie of underlying audit work through to financial statements and related disclosures
Some of these technologies will be explored in more detail in future Market Scans.
Many organizations are expanding their use of AI across parts of their business with the goal of driving operational efficiencies, better informed decision making and generating growth through innovation. As a result, it is likely that this technology will become a relevant consideration when performing audit procedures, particularly regarding risk identification and assessment, and risk response activities.
Where AI is deployed, whether by the auditor in carrying out their procedures or by an audited entity within their business operations, the associated risks need to be identified and appropriately managed. Many assurance firms and organizations have developed methodologies that provide a framework for identifying and managing AI related risks. In September 2021, COSO issued new guidance setting out how to apply “the COSO framework and principles to help implement and scale artificial intelligence”.
This guidance identifies five areas of AI related risks:
Bias and reliability breakdowns due to inappropriate or non-representative data
Inability to understand or explain AI model outputs
Inappropriate use of data
Vulnerabilities to adversarial attack to obtain data or otherwise manipulate the AI model
Societal stresses due to rapid application and transformation of AI technologies
It concludes that appropriate risk management is needed to ensure that AI solutions are “trusted, tried and true”.
Auditing AI may require a different set of skills to those currently applied in today’s audits and many firms are updating their recruitment strategies, training curricula and audit methodologies to respond to the growing need for AI competencies. Future Market Scans will explore some of these challenges in more detail.
The global AI market is expected to achieve a compound annual growth rate of nearly 40% over the next five years and whilst AI technologies such as natural language processing and speech recognition are maturing, others such as deep learning and Generative AI have significant scope for development.
Here are some recent noteworthy developments:
Regulation and Explainable AI
One of the issues that has arisen with AI is around the negative impact of biases in algorithms and the harm that this can cause. In a recent survey more than one in three companies surveyed disclosed that they had suffered losses (revenue, customers or staff) due to AI bias in their algorithms. In response, there is an expectation regulation will be established in the near future. The EU, in its white paper, “On Artificial Intelligence—A European Approach to Excellence and Trust”, noted that explainability is a key factor to improving trust in AI. Many companies are, therefore, expected to look to implement explainable AI in which the results of the solution can be understood by humans.
Efficient AI
DeepMind, the company behind the AlphaGo program that was the first to beat a professional Go player, has developed an AI large language model—that is, a statistical tool to predict words—called RETRO (Retrieval-Enhanced Transformer). This AI technology, built to generate convincing text, chat with humans and answer questions is said to match the performance of neural networks 25 times its size through use of a text database.
Decision Intelligence
One of the top technology trends for 2022 noted by Gartner is decision intelligence, which is using AI to enhance and support human decision making. Peak.ai, a UK based start-up, raised US $75m in series C funding in August 2021 to enable it to build out its “decision intelligence” platform to expand into new markets and help non-tech companies make AI-based decisions.
Funny Story
AI argues for and against itself in Oxford Union debate: Megatron, an AI developed by Google and Nvidia, was given access to huge quantities of data to enable it to both defend and argue against the motion, “This house believes that AI will never be ethical”. It’s not clear which argument was more compelling!
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What next?
Our next Market Scan bulletin will be distributed in April 2022.
The International Federation of Accountants (IFAC) welcomes the U.S. SEC’s proposal on enhanced climate disclosures, as we continue our work in support of a global system for delivering consistent, comparable and assurable sustainability information. The SEC’s action is one more important demonstration of the need to enhance and evolve corporate reporting.
Sustainability-related disclosure is now a core component of the corporate reporting ecosystem, as reflected in global and jurisdiction-specific initiatives. Climate and other ESG matters are decision critical. Regulatory frameworks must promote rigor, clarity, and consistency of information, both to meet investor demands and those of other stakeholders.
IFAC CEO Kevin Dancey said, “This SEC proposal allows policymakers and stakeholders worldwide to compare and contrast important initiatives that are taking place on climate-related disclosure, including the work of the newly established International Sustainability Standards Board under the IFRS Foundation, that of the European Union, and others.
“Alignment among all these initiatives is imperative for decision-useful information. It’s critical that we create a harmonized, global system of sustainability and climate disclosure and avoid a patchwork of standards and regulations emerging across jurisdictions. IFAC also supports regulations that promote narrative as well as performance-focused measurements and that better align sustainability disclosure with financial reporting.”
Read more about IFAC’s support for global sustainability-related standards on the IFAC website.
About IFAC
IFAC is the global organization for the accountancy profession dedicated to serving the public interest by strengthening the profession and contributing to the development of strong international economies. IFAC is comprised of 180 members and associates in 135 countries and jurisdictions, representing more than 3 million accountants in public practice, education, government service, industry, and commerce.
The International Federation of Accountants (IFAC) today released a new resource, Exploring the IESBA Code, A Focus on Technology: Artificial Intelligence. This two-page publication highlights the application of the International Code of Ethics for Professional Accountants (including International Independence Standards) (the Code), in particular, the relevance of the Code’s fundamental principles and its conceptual framework to addressing ethics issues that might arise when artificial intelligence (AI) is used or implemented by professional accountants. Specifically, the installment sets out an AI scenario to assist accountants in identifying, evaluating and addressing threats to compliance with the Code’s fundamental principles.
Originally launched in 2019, the Exploring the IESBA Code is a unique resource developed in collaboration with the staff of the IESBA. This installment focusing on AI has also benefited from input from the IESBA’s Technology Working Group. The Exploring the IESBA Code series is intended to promote awareness of the Code and support its global adoption and implementation. It is non-authoritative and is not a substitute for reading the Code. Each installment highlights important concepts and topics in the Code and seeks to help readers understand how to use and navigate the Code so that they can quickly identify and access the ethics and independence standards and guidance relevant to them.
Previous installments have been translated into multiple languages. Topics previously covered by the series include: the fundamental principles, the conceptual framework, auditor independence, conflicts of interest, inducements, responding to non-compliance with laws and regulations (NOCLAR), pressure, the role and mindset expected of the professional accountant with a focus on bias, and the “building blocks” structure of the Code. Click here to access this and previous installments.
About IFAC
IFAC is the global organization for the accountancy profession dedicated to serving the public interest by strengthening the profession and contributing to the development of strong international economies. IFAC is comprised of 180 members and associates in 135 countries and jurisdictions, representing more than 3 million accountants in public practice, education, government service, industry, and commerce.
About IESBA
The International Ethics Standards Board for Accountants (IESBA) is an independent global standard-setting board. The IESBA’s mission is to serve the public interest by setting ethics standards, including auditor independence requirements, which seek to raise the bar for ethical conduct and practice for all professional accountants through a robust, globally operable International Code of Ethics for Professional Accountants (including International Independence Standards) (the Code).
New publication spotlights artificial intelligence
IAASB’s second disruptive technologies roundtable was held in February 2022. The roundtable was part of the IAASB’s ongoing exploration of disruptive technologies. These technologies already affect the audit and assurance profession with potential for even greater affects in the future.
The International Public Accounting Standards Setting Board (IPSASB) welcomed the release of the new Pathways to Accrual, a digital platform with resources designed to help governments and other public sector entities planning and undertaking the transition from cash to accrual accounting including adopting and implementing International Public Sector Accounting Standards (IPSAS). The platform was developed by the International Federation of Accountants (IFAC) with content provided by the Chartered Institute of Public Finance and Accountancy (CIPFA) and feedback from IPSASB and other international stakeholders.
With additional, easy to navigate digital content, Pathways to Accrual builds upon the IPSASB’s publication Study 14, Transition to the Accrual Basis of Accounting: Guidance for Governments and Government Entities.
“There is significant accrual adoption and implementation activity underway across all regions of the world,” said Ian Carruthers, International Public Sector Accounting Standards Board (IPSASB) Chair. “By 2025, 50% of the jurisdictions in the 2021 International Public Sector Accountability Index are forecast to report on accrual basis, and Pathways to Accrual will be instrumental in supporting both these transitions and the many others planned for subsequent years.”
The International Public Sector Accounting Standards Board (IPSASB) works to strengthen public financial management globally through developing and maintaining accrual-based International Public Sector Accounting Standards® (IPSAS®) and other high-quality financial reporting guidance for use by governments and other public sector entities. It also raises awareness of IPSAS and the benefits of accrual adoption. The Board receives support from the Asian Development Bank, the Chartered Professional Accountants of Canada, the New Zealand External Reporting Board, and the governments of Canada and New Zealand. The structures and processes that support the operations of the IPSASB are facilitated by the International Federation of Accountants (IFAC). For copyright, trademark, and permissions information, please go to permissions or contact permissions@ifac.org.
