The IPSASB held its first meeting of the year in New York on March 21-25, 2022.
Sustainability Reporting
The IPSASB approved its global consultation on developing a sustainability reporting framework for the public sector. The IPSASB plans to launch this pivotal consultation in early May, alongside its Natural Resources Consultation Paper and the IPSASB Mid-Period Work Program Consultation Feedback Statement.
Advancing public sector sustainability reporting is both important and urgent. The IPSASB is pleased to be able to lead the debate. Watch this space for launch details and how to get involved.
Mid-Period Work Program Consultation
The IPSASB agreed to add new projects to its 2022 work program:
Presentation of Financial Statements; Differential Reporting;
Reporting Sustainability Program Information; and
Advancing Public Sector Sustainability Reporting Consultation Paper.
As resources become available in 2022, work on the above projects will commence.
The landscape for IPSASB’s work has changed since the Mid-Period Consultation was published, resulting in fewer resources being available than originally anticipated. The IPSASB will continue to monitor work program progress and resource availability in 2023, to look for opportunities to commence work on the limited scope projects proposed in the Mid-Period Consultation, which were strongly supported by constituents.
Natural Resources
The IPSASB approved the Consultation Paper, Natural Resources, which will be published in May 2022, and will be open for comment until October2022. The Consultation Paper includes the IPSASB’s preliminary views on issues related to the recognition, measurement, presentation, and disclosure ofnatural resources, usingexamples of subsoil resources, water, and living resources.
The IPSASB approved the project roadmap, including issuing an Exposure Draft as the next output for this project. The IPSASB also decided to analyze the arrangements from the perspectives of both:
Parties to the arrangements; and
The consolidated financial statements and separate financial statements.
The IPSASB plans to discuss concessionary leases and leases for zero or nominal consideration at the June meeting.
Revenue and Transfer Expenses
The IPSASB agreed to use the term ‘compliance obligation’to describe an entity’s legally binding obligation arising from revenue transaction with a binding arrangement. The IPSASB furtherdiscussed the implications of internal and external factors on the subsequent measurement of assets arising from binding arrangements. The IPSASB also continued discussing principles related to transfer expenses accounting, focusing on the timing and recognitionof transfer expenses in transactions with binding arrangements, and the allocation of consideration to the transferor’s transfer rights.
Measurement
The IPSASB performed a detailedreview of the responses to ED 77, Measurement. Respondents strongly supported most of the ED proposals. The IPSASB agreed to move forward with the proposals related to Fair Value and Cost of Fulfillment, and thatdisclosure requirements should be included in the relevant IPSAS. The proposed principles related to historical cost and the measurement model policy choice are areas where further clarification is needed.
Conceptual Framework-Phase I
The IPSASB reviewed responses to ED 76, Conceptual Framework Update: Chapter 7, Measurement of Assets and Liabilities in Financial Statements. The IPSASB decided to retain the three-level classification proposed in ED 76. However, the term ‘Subsequent Measurement Framework’will be adopted rather than ‘Measurement Hierarchy’.
The IPSASB decided to include fair value as defined in ED 76 and to delete market value. The IPSASB instructed staff to further analyze the case for deletionof net selling price, cost of release and assumption price.
Non-Current Assets Held for Sale and Discontinued Operations
The IPSASB approved IPSAS 44, Non-current Assets Held for Sale and Discontinued Operations with an effective date of January 1, 2025. IPSAS 44 aligns with IFRS 5, Non-current Assets Held for Sale and Discontinued Operations and provides the accounting requirements for assets held for sale and provides presentation and disclosure requirements for discontinued operations. IPSAS 44 is expected to be published in May 2022.
ISS Update
The IPSASB discussed the work done by the statistical community in updating the International Statistical Standards(ISS) and the IPSASB’s role in that process. The IPSASB also reviewed the new IPSAS-ISS Alignment Dashboard, which will be a standing agenda item for future meetings and captures the IPSASB’s long standing work to reduce unnecessary differences with statistical standards to make IPSAS information useful for statistical compilation purposes. The IPSASB discussed the importance of IPSAS-ISS alignment from both conceptual and practical perspectives.
Next Meeting
The next full meeting of the IPSASB will take place in June 2022. For more information, or to register as an observer, visit the IPSASB website (www.ipsasb.org).
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.
Welcome to the first market scan prepared by 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 will bring you a regular market scan focusing on various topics from the report around every two months. The market scans will consist of exciting trends in the area, including interesting developments on this topic, what this might mean for the IAASB, corporate and start-up innovation, and noteworthy investments.
In this market scan, we will explore Data Standardization Platforms for Enabling Data Access, which fallsunder the activity of Accessing Information & Data. We're starting with Data Standardization because establishing a common standard of how data is structured and accessed is a foundation block to the success and widespread adoption of other innovative technologies.
We will cover:
What is Data Standardization and why it is important?
What some of the latest exciting developments on this topic are, including the increasing maturity of Common Data Models and Knowledge Graphs.
What this could mean for the IAASB.
What is Data Standardization and why is it important?
Data Standardization is the process of converting data to a common format that allows users to better analyze and utilize the data, thereby enabling data collaboration, large-scale analytics and the use of more advanced tools to interrogate the data.
With exponential growth in the amount and variety of data that companies create and use, there is voluminous unstructured, or inconsistently structured, data in companies' repositories. This leads to data silos and data that is underutilized or unnecessarily hard to access.
One of the problems this has created, amongst others, is where auditors are spending more time on data management, particularly when trying to access, "map," and use the entity's data as well as when performing data analytics. When each entity uses different data models and systems or platforms to store, structure and extract data, the inefficiency is amplified. Some firms are developing internal tools to address these challenges, such as by building ETL (Extract, Transform, Load) tools to minimize this inefficiency e.g., KPMG had 25 different ETL projects in 2019. Auditor's time spent on data management may be better utilized on other areas of the audit, and many firms and practitioners may have challenges with obtaining tools to access, manage and evaluate data relevant for auditing and assurance engagements.
The data management industry, firms, and regulators are exploring various approaches to help audit and assurance professionals and other professional service providers with these challenges. The UK's Brydon review in 2019, for example, recommends initiatives to develop a standard method of data extraction covering both structured and unstructured data.
Data standardization complements this approach by helping to address the root cause of difficulties by converting the data to be extracted into a common format. In particular, digital multi-party platforms are gaining traction as a solution to provide standardized data structure and mechanisms to access data silos with non-uniform formats, thereby facilitating the sharing of data to unlock new values both internally and externally. One exciting development is the development of Common Data Models (CDM). A CDM is a shared data language, allowing standardized metadata and its meaning to be shared across applications easily. Unfortunately, at present, there is no one model adopted globally across specific industries or jurisdictions.
Recent Noteworthy Developments in Data Standardization
This section is designed to provide examples of recent developments that may signal future disruption in this area. It is not a complete list of all activities in the field of data standardization.
1. Disruptive start-ups are gaining traction
Engine B receives new financial and board-level investment
Institute of Chartered Accountants in England and Wales (ICAEW) upped its investment in Engine B to 10% and has taken a board seat.
Engine B is partnered with key organizations, including Microsoft and ICAEW, to create an Audit Common Data Model.
Part of this project involves creating an Intelligent Data Access Platform designed to be installed in a client environment and ingest corporate data, both structured and unstructured, to map it on an Audit CDM. This platform attempts to replace the need for complex ETL tools in favor of open data standards that facilitate the sharing of clean standardized data.
Working with 13 audit firms, Engine B aims to roll out its assets in late 2021, first in the UK, then the US, as it is collaborates with the AICPA. It aims to become a widely adopted infrastructure like Open Banking globally.
Other Data Standard Initiatives
Data standards exist in various forms already. Another example is ISO 21378, Audit Data Collection, issued by the International Organization for Standardization (ISO). This standard leveraged the American Institute of CPA American Institute of Certified Public Accountants Audit Data Standards.
2. InfoSum raised a further $65m in their Series B to scale its privacy-focused data collaboration platform:
InfoSum's 'non-movement of data' technology enables companies to connect their data (both internally and externally) to unlock new customer value. This works by having companies standardize their data (i.e., map their data) according to InfoSum's Global Schema rules and upload it to InfoSum's platform.InfoSum raised a further $65m in their Series B to scale its privacy-focused data collaboration platform:
What does this mean? It means investors see an opportunity for data standardization when companies want to collaborate and share data without moving data outside their companies. It also signals the growing maturity of the data collaboration space as InfoSum is a leading start-up and is raising large sums of money to upscale its operations.
Key Venture Capital & Investment Terms
Venture capital (VC) is a form of financing where capital is invested into a company, usually a start-up or small business, in exchange for equity in the company. VC funding stages can be useful signals for the maturity of the start-up and its products or services – the later the stage, the more developed and established in market the startup typically is. VC funding can also be a useful barometer for the interest in a particular technology and how influential it may be in the market as well as an indicator of potential wider adoption.
For simplicity it can be useful to group start-up funding stages into the below broad categories:
Pre-seed and Seed: start-ups in these stages are very early-stage start-ups, often prior to launching a product in market or with a few initial customers. They are typically seeking investments from various types of investors (e.g., individuals as well as firms) to establish themselves, build out the product, hire the core team and acquire more of their initial customer base.
Series A and Series B: start-ups in these stages are in growth mode, usually with a product/service that is market-ready and launched, with some revenue being generated. They are usually looking for funding to fuel the continued growth of the start-up.
Series C and beyond: start-ups in these stages are more mature, typically with products/services in market that have strong demand and likely have solid revenues and profits. Series C can be the last stage of VC financing (e.g., before an IPO) however many companies opt to raise more VC rounds such as Series D, E, etc. Funding at this stage is likely to be used to scale up operations and continue growth through entering new markets, R&D or making acquisitions.
2. Relevant industry players are taking more interest in data standardization
I. A leading US accounting firm custom-built a common data model
Besides a growing industry consortium for Engine B, a US accounting firm has partnered with Orion Innovation to build a CDM that enables uniformity in the data from hundreds of different ERP systems and technology platforms
The CDM made the data more understandable and useful to all its business activities, particularly auditing, data analytics, and advisory. Apart from unlocking advanced data analytics on the full population of data, it also unlocked automation options, which may provide greater consistency of audit quality.
II. The EDM Council, a global association created to elevate the practice of Data Management, is leading the development of an open-source semantic data standard
The EDM Council has published the Data Management Capability Assessment Model (DCAM). DCAM defines the scope of capabilities required for an entity to establish, enable and sustain a mature Data Management discipline. It addresses the strategies, organizational structures, technology and operational best practices needed to drive Data Management across the organization, and ensures the data can support digital transformation, advanced analytics such as artificial intelligence and machine learning, and data ethics.
The EDM Council partnered with CPA Canada to provide an overview of how DCAM can be leveraged for audit and business controls. A recording is available.
Since 2020, the EDM Council has been leading the Financial Industry Business Ontology (FIBO) initiative, which provides descriptions of the structure and contractual obligations of financial instruments and financial processes, to give meaning to the data.
The fundamental aim of the standard is to harmonize data across disparate repositories to validate data quality and improve risk analysis by making links between datasets that are understandable to both humans and software, i.e., resolve data silos.
3. Knowledge graphs for audit use cases is showing promising progress
I. Engine B's audit knowledge graph hopes to improve the quality of audits
Knowledge graphs developed by Engine B provide contextual relevance of data by looking at the relationships between all data elements (both structured and unstructured), which allow auditors to make context-driven decisions. In particular, they are looking at anomaly detection and fraud detection as their initial use cases.
These knowledge graphs can sit on top of their Audit CDM to perform visual and contextual data analysis on all relevant transactions. Here is an explainer video from the developers.
II. The EDM Council is creating an Open Knowledge Graph Lab (OKGL) on top of their FIBO initiative
Since late 2020, EDM Council has been developing OKGL as the infrastructure of knowledge graphs for application across different sectors. Particularly for the financial services sector, the EDM Council is exploring use cases in fraud, risk and anti-money laundering. The EDM Council is also currently preparing the rollout of a cloud sandbox to serve as a testbed to develop prototypes.
What might this mean for the IAASB?
The IAASB has an interest in improving the data available to assurance practitioners as this may enable the performance of more advanced analytics and otherwise improve areas of the audit that use data (e.g., evaluating models and related controls). Data standardization also enables collaboration between the entity and others, including auditors or assurance practitioners. Data standards are of particular interest in the sustainability space as a tool for entities to satisfy different reporting standards.
Data standardization is not within the IAASB's remit because it is fundamentally a matter for how the entity manages its data. Local law or regulation requiring the maintenance of books and records may be most relevant. However, because of the benefits to audit and assurance quality, the IAASB should stay close to the topic and take opportunities to raise it with other stakeholders, such as regulators, preparers, and assurance practitioners.
While the developments on data standardization are promising, there is still a considerable way to go before it is widely adopted by entities and therefore able to fully benefit audit and assurance. In the absence of a widely adopted CDM or another method to standardize data, the gap in data management capabilities between differently equipped firms and practitioners, including between jurisdictions, may grow. Furthermore, widespread adoption of innovative automated audit tools and techniques will be inhibited when data is not structured in a standard format.
As prominent CDMs are more widely adopted and supported by entities and made available to firms, there may be a need for standards on assurance services on whether data is compliant with the relevant data standard.
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What next?
Our next Market Scan bulletin will be distributed by January 2022.
Welcome to the second market scan from the IAASB's Disruptive Technology team. Building on our previous work, including the Innovation Report created with Founders Intelligence and discussed at the January 2021 IAASB meeting, we will issue a Market Scan focusing on topics from the report approximately every two months. Market Scans will 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 API Access to External Data Sources for Enriched Analysis, which fallsunder Accessing Information & Data, because establishing a method for obtaining relevant and reliable external data that can be used in an audit has the potential to reshape the audit process.
We cover:
What an API is and why it is important?
The latest exciting developments on this topic are, including Open Banking
Possible implications for the IAASB
What is an API and why is it important?
An API or Application Programming Interface is a set of defined rules that explain how computers or applications communicate with one another. They enable companies to open their application’s data and functionality to external third-party developers, business partners and internal departments within their companies. APIs use standardized requests that in turn return standardized outputs or responses.
APIs have been around as long as computers; modern day “web APIs” grew in use with the advent of social media platforms, like Facebook and Twitter. However, it was Amazon that created a fundamental shift in how digital resources are accessed with founder and CEO Jeff Bezos’ famous API mandate, issued in 2002. This manifesto requires all Amazon development teams to “expose their data and functionality through service interfaces”.
From an audit and assurance perspective, there are three key areas where APIs can be leveraged.
Enabling access to entity data (such as general ledger or sub-ledger data) for the purposes of inquiry or extraction.
Enabling access to entity-specific third-partydata such as bank transactions.
Enabling access to audit-relevant external information sources, such as macroeconomic or industry-specific data.
1. Access to entity data
In our last Market Scan: Data Standardization, we wrote about the exponential growth in available data that could be used in an audit as well as the challenges of obtaining standardized data and the use of common data models. The initial step in the data acquisition process may involve using an API to request the required data from the entity’s accounting system. Over the last five years, there has been growth in investment in this area both from within accounting firms and from third party vendors of data extraction, transformation and load (ETL) technology such as Engine B, Validis, Inflo, Galvanize and Workiva.
2. Access to entity-specific third-party data
This is the key area of potential disruption to the audit and assurance industry. Being able to directly access entity-specific third-party data, such as bank transactions, by using open banking APIs could revolutionize how audit evidence is obtained, particularly when connected with entity data and other relevant external information sources. Mandates such as the Second Payments and Services Directive in Europe, which required banks to open their payments infrastructure and customer data to third parties, have supported the growth in open banking. It is now a global initiative with 87% of countries having some form of Open Banking API. Below are details of some countries that use open banking, including where driven by government regulation.
3. Access to audit-relevant external information sources
Use of external information sources is commonplace in an audit. By using APIs, information can be obtained in a standardized format, which makes it easier to use, for example, in analytical procedures.
The benefits of APIs may include increased speed and access to data from varied independent reliable sources. Additionally, when coupled with other technology, such as robotic process automation, it can facilitate efficiencies in routine audit activities, such as using company registry information to identify related parties. Many audited entities seeking to leverage these benefits are using APIs within their IT environment to support business operations.
Alongside the significant growth in prevalence of APIs comes concerns about security and management of data. The UK and Australian governments have both issued API data standards and continued attention from governing bodies is likely.
Recent noteworthy developments in API access
This section is designed to provide examples of recent developments that may signal future disruption in this area. It is not a complete list of all activities in the field of API access. For a reminder of Key Venture Capital and Investment terms please refer to the previous Market Scan.
1. Open Banking experiences rapid growth
I. Fintech start-ups are shaking up the banking industry
There are a number of very active fintech start-ups developing APIs that allow easier sharing of financial data. Prominent examples around the world include:
Plaid, a San Francisco-based startup building technology platforms to connect applications to users’ bank accounts, has raised US$735m in funding with a latest US$425m Series D backed investors such as Andreessen Horowitz and Silver Lake. Acquisition by Visa was blocked by the US Department of Justice on the grounds that it would limit competition in the payments industry. Plaid was one of the first companies to create what is called a unified API—a single API that connects to over 11,000 financial institutions.
Tink and Truelayer, both based in Europe offer platforms and products to support open banking integration in applications. Tink was recently acquired by Visa.
Open banking has also gained traction in Asia with early-stage start-ups like Hong Kong-based Finverse, which has an ambitious goal to enable open banking throughout the Asia-Pacific region.
Additionally, there are start-ups serving the financial services industry with APIs providing access to payroll, insurance and credit data to support targeting of appropriate financial products to businesses. This is an area of significant growth and one to watch for future audit and assurance implications.
II. Recognition grows of the impact of Open Banking on Assurance services
Confirmation.com (part of Thomson Reuters) has provided audit confirmation services for nearly 20 years and recently completed a three-month pilot to test open banking, which received positive feedback from pilot audit firms.
Circit is a rapidly growing Dublin-based fintech startup launched in 2017 that provides a platform supporting confirmation requests, transaction verification, PBC client collaboration and document signing. It names banks, “big four” and mid-tier audit firms amongst its clients.
2. External Information Sources Related Activity
I. US PCAOB issues guidance on external information sources
In October 2021, the US Public Company Accounting and Oversight Board (PCAOB) issued staff guidance highlighting the importance of appropriately evaluating the relevance and reliability of information from an external information source that an auditor plans to use as audit evidence. The publication gave a number of examples and factors to consider. It notes that, “Advancements in technology in recent years have improved accessibility and expanded the volume of information available to companies and their auditors from traditional and newer external sources.”
II. Growth in data platform providers to support access to external data sources
People Data LabsraisedUS$45m in Series Bfunding to enable expansion of data products to support fraud detection and risk mitigation. TheSan Francisco-based company builds APIs that enable their clients to leverage vast datasets to build people profiles and records as well as power predictive modeling, drive artificial intelligence and build new tools. The new funding, announced in November 2021, will enable the company to expand its data products to support fraud detection, risk mitigation and insurance underwriting.
Demyst raised A$33m and announced plans to issue an IPO. Demyst is an external data deployment companythat works with banks, insurers and fintechs providing operationalized access to external data sources through a secure data platform.
What might this mean for the IAASB?
Access to quality data is at the heart of enabling technological transformation within the assurance profession. APIs represent a key route to success. The availability of accessible, standardized data created by APIs builds opportunities to improve finance functions, enhance audit quality, and radically streamline the audit process.
The increasing accessibility of entity-specific third-party data, such as entire populations of bank transactions that have been made possible by Open Banking APIs, may present a need to envision how this will reshape the audit process—particularly in regard to obtaining audit evidence.
The growing use of web APIs within entity core operations, across many industries, from retail to banking, may lead to web APIs becoming relevant to financial statements preparation and, therefore, auditors’ risk assessment procedures. Jurisdiction-specific guidance may help auditors better understand and assess how entities are managing the risks related to using APIs available in their jurisdiction.
Finally, the quantum of external data sources available to auditors presents an additional challenge of assessing the relevance and reliability of these data sources—and perhaps a need to address these matters centrally.
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The IPSASB held its fourth meeting of the year virtually on December 7-10 and 14-15, 2021.
Leases
The IPSASB approved IPSAS 43, Leases with an effective date of January 1, 2025. IPSAS 43 supersedes IPSAS 13, Leases and introduces the right-of-use model for lessees, aligning with IFRS 16, Leases. IPSAS 43 is expected to be published in January 2022. The IPSASB will continue consideration of public sector specific leasing issues, such as concessionary leases, in its Other Lease-Type Arrangements project.
Improvements
The IPSASB approved Improvements to IPSAS, 2021 with an effective date of January 1, 2023, except for the Interest Rate Benchmark Reform related amendments to IPSAS 29, Financial Instruments: Recognition and Measurement, which will have an effective date of January 1, 2022. Improvements to IPSAS, 2021 is expected to be published in January 2022.
Retirement Benefit Plans
The IPSASB voted to preliminary approve ED 82, Retirement Benefit Plans. ED 82 provides accounting and reporting requirements for public sector retirement benefit plans and is adapted from IAS 26, Accounting and Reporting by Retirement Benefit Plans. The IPSASB will finalize ED 82 at its February 2022 meeting.
The IPSASB approved ED 81, Conceptual Framework Update: Chapter 3, Qualitative Characteristics and Chapter 5, Elements. ED 81 will be published early in 2022 with a four-month consultation period. In December, the IPSASB finalized its proposals for the description of a resource and revisions to Chapter 5, which includes sections on unit of account and liabilities.
Natural Resources
The IPSASB reviewed updates to the draft Natural Resources Consultation Paper and performed a detailed page-by-page review of the introductory chapter, as well as the chapters on presentation, living resources, and water. Other than certain clarifications and editorial comments, no significant issues were noted by the IPSASB’s review. The Consultation Paper is expected to be approved at the March 2022 meeting.
Revenue and Transfer Expenses
The IPSASB discussed accounting models proposed for Transfer Expenses with, and without, binding arrangements and reviewed guidance related to specific aspects of the draft standard. The IPSASB confirmed an entity’s obligation in revenue transactions with binding arrangements is a narrower concept than ‘present obligation’ in the Conceptual Framework, and clarified how to distinguish individual obligations in a binding arrangement. The IPSASB agreed that specified activities and eligible expenditures are examples of ways in which an entity may fulfill obligations.
Mid-Period Work Program Consultation
The IPSASB performed its preliminary analysis of the responses to the Work Program Consultation. Based on the strong support from respondents, the IPSASB tentatively agreed to prioritize the two major and four minor projects proposed in the Consultation and decided a feedback statement should be developed to capture constituent feedback. The project prioritization and feedback statement are expected to be approved in March 2022. Finally, the IPSASB discussed the strong feedback received that indicated sustainability reporting should be prioritized.
Measurement Suite of EDs
A preliminary analysis of the responses to the Measurement Suite of Exposure Drafts (ED) 76-79 was discussed by the IPSASB. Respondents strongly supported most proposals, and it was clear the IPSASB’s efforts in developing an illustrative ED as part of the consultation process in 2019 paid dividends. The IPSASB focused its discussions on the diverse views related to the public sector specific measurement basis proposed and agreed the Board would have to dedicate resources in 2022 to address concerns identified.
The IPSASB approved its Mid-Period Work Program Consultation. This consultation seeks constituent feedback on which projects the IPSASB should prioritize as its resources become available. The IPSASB will hold several regional virtual outreach events during the consultation period to directly engage with constituents.
The consultation is expected to be published in July 2021 with a 4-month comment period. Watch for the consultation for the full details on the IPSASB’s proposals.
Improvements to IPSAS 2021
The IPSASB approved Exposure Draft (ED) 80, Improvements to IPSAS, 2021, which includes both general improvements and IFRS related improvements to IPSAS. General improvements consist of proposals for minor amendments to IPSAS identified by stakeholders. IFRS related improvements consist of proposals for minor amendments to IPSAS sourced from recent IFRS improvements and narrow scope amendment projects.
ED 80 is expected to be published in July 2021 with a 60-day comment period.
Natural Resources
The IPSASB reviewed the draft Consultation Paper (CP) and considered the general description of natural resources. The IPSASB discussions focused on the overall approach to determining the recognition, measurement, and disclosure of items which fit into this general description of natural resources, and those that do not, as well as how the description relates to the specific topics included in the CP. The IPSASB also discussed the description, recognition, measurement, and disclosure of water.
Revenue and Transfer Expenses
The IPSASB continued its discussions on Revenue and Transfer Expenses topics identified during its review of responses to the Exposure Drafts. Based on discussions, the IPSASB decided to retain the current definition of a binding arrangement, with minor revisions, and clarified specific considerations when assessing enforceability of a binding arrangement. The IPSASB also discussed the definition of a liability in the context of the ongoing projects.
Amendments to IPSAS 5, Borrowing Costs (Non-Authoritative Guidance)
The IPSASB approved IPSAS 5, Borrowing Costs – Non-Authoritative Guidance, which reaffirms the IPSASB’s decision to maintain the accounting policy choice to capitalize or expense borrowing costs directly attributable to a qualifying asset. The non-authoritative guidance added includes implementation guidance and illustrative examples to clarify how to determine the extent to which borrowing costs can be capitalized.
The main issues discussed related to prudence and materiality. The IPSASB decided not to adopt prudence as a separate qualitative characteristic (QC). Prudence will be discussed as a reinforcement of neutrality in the context of the QC of faithful representation.
The IPSASB also decided to add obscuring information to omitting and misstating information as factors that can influence the objectives of financial reporting - discharging accountability and decision making. Obscuring information by, for example, including immaterial disclosures can impair understandability.
Accounting and Reporting by Retirement Benefit Plans
The IPSASB decided the scope and the concept of a reporting entity in the Accounting and Reporting by Retirement Benefit Plans ED should be consistent with IAS 26, Accounting and Reporting by Retirement Benefit Plans. The IPSASB also decided the ED should require retirement benefit plans to prepare a statement of financial position, a statement of change in net assets available for benefits, a cash flow statement, notes to the financial statements and information on the changes of pension obligations.
Next Meeting
The next full-meeting of the IPSASB will take place virtually in September, 2021. For more information, or to register as an observer, visit the IPSASB website (www.ipsasb.org)
The IPSASB held its third meeting of the year virtually on September 14-18 and 22, 2020.
COVID-19: Deferral of Effective Dates
The IPSASB approved COVID-19: Deferral of Effective Dates to defer the effective dates of the following standards and amendments by one year to January 1, 2023:
IPSAS 41, Financial Instruments;
IPSAS 42, Social Benefits;
Long-term Interests in Associates and Joint Ventures (Amendments to IPSAS 36);
Prepayment Features with Negative Compensation (Amendments to IPSAS 41);
Collective and Individual Services (Amendments to IPSAS 19); and
Certain amendments included in Improvements to IPSAS, 2019.
The option to early-adopt the above standards or amendments continues to apply.
ED 74, Non-Authoritative Amendments to IPSAS 5, Borrowing Costs
The IPSASB approved Exposure Draft (ED) 74, Non-Authoritative Amendments to IPSAS 5, Borrowing Costs, and agreed on an exposure period ending March 1, 2021. ED 74 proposes implementation guidance and illustrative examples to clarify how to determine the extent to which borrowing costs can be capitalized.
Public Sector Specific Financial Instruments
The IPSASB approved Non-Authoritative Amendments to IPSAS 41, Financial Instruments, which includes additional implementation guidance and illustrative examples to clarify the requirements for classifying, recognizing, and measuring public sector specific financial instruments.
The IPSASB agreed an effective date of January 1, 2023, to align with the effective date of IPSAS 41, Financial Instruments.
Leases–IFRS 16 Alignment
The IPSASB reviewed draft ED 75, Leases and the Request for Information on Concessionary Leases and Other Arrangements Similar to Leases. The IPSASB decided not to amend the lessee’s requirements in draft ED 75 on discount rates as no public sector specific issues were identified. The IPSASB intends to approve ED 75, Leases at its December 2020 meeting.
ED 76 and ED 77, Conceptual Framework-Limited Scope Update and Measurement
The IPSASB continued developing its measurement hierarchy. The IPSASB agreed:
The hierarchy applies to subsequent measurement;
The measurement bases and techniques in the hierarchy; and
The allocation of measurement techniques to measurement bases.
The IPSASB will review draft EDs reflecting these decisions at its next meeting. The IPSASB will also further consider the approach to measurement at initial recognition and whether the definition of value in use should continue to include non-cash-generating assets or whether an alternative public sector concept should be developed.
ED 79, Non-Current Assets Held for Sale and Discounted Operations
The IPSASB approved ED 79, Non-current Assets Held for Sale and Discontinued Operations. This ED will be issued together with ED 76, Conceptual Framework—Limited Scope Update, ED 77, Measurement, and ED 78, IPSAS 17 Update (Comprehensive ED bringing together changes to IPSAS 17 from Measurement, Infrastructure Assets and Heritage Assets); all of which are currently noted for approval by the end of 2020 on the IPSASB’s work program.
ED 78, IPSAS 17 Update, Heritage and Infrastructure
The IPSASB completed its review of issues identified by constituents when accounting for heritage and infrastructure assets. The IPSASB agreed the proposed authoritative guidance, implementation guidance and illustrative examples would support constituents in applying the Property, Plant and Equipment principles to infrastructure and heritage items in practice.
The IPSASB will consider the proposed guidance in its entirety at its December 2020 meeting as part of its review of ED 78.
Natural Resources
The IPSASB agreed that a government’s sovereign power to issue licenses is not, in and of itself, an asset. The IPSASB also provided feedback on the staff’s survey to gather information regarding various jurisdictional legal frameworks for subsoil resources exploration, development and extraction, and on the draft structure of the consultation paper, its introduction and first chapter.
Next Meeting
The next meeting of the IPSASB will take place in December, 2020. For more information, or to register as an observer, visit the IPSASB website (www.ipsasb.org).
The third edition of IESBA Asia Dispatch highlights discussions in Hong Kong SAR as part of the ongoing Firm Culture and Governance Dialogues. The newsletter explores how trust is reinforced through continuous improvement, strong leadership, and a sustained commitment to ethical practice. Engagements with regulators, firms, and other stakeholders underscore the importance of embedding governance, accountability, and ethical decision making into organizational culture to support long-term confidence in the profession.
The second edition of IESBA Asia Dispatch reflects on the continuation of the Firm Culture and Governance Dialogues in Malaysia. Building on discussions in Singapore, this edition explores how purpose-driven leadership, strong organizational culture, and a clear commitment to the public interest translate into action. Through engagement with regulators, professional bodies, and market participants, the newsletter highlights practical insights on accountability, governance, and the role of ethics in shaping resilient institutions.
IESBA’s inaugural Asia Dispatch captures key insights from the launch of the Firm Culture and Governance Dialogues in Singapore. The edition highlights reflections from Chair Gabriela Figueiredo Dias, perspectives from CFO roundtable discussions, and conversations on technology, AI, and the evolving role of ethics in strengthening organizational culture. Drawing on engagement with regulators, firms, and professional bodies, the newsletter underscores the power of collaboration in advancing trust and governance across the region.
