Cryptocurrency continues to reshape the financial landscape. As cryptocurrency moves from niche to mainstream, companies are grappling with how to account for these volatile digital assets. New research from Scheller College of Business accounting professor Robbie Moon, and his co-authors Chelsea M. Anderson, Vivian W. Fang, and Jonathan E. Shipman, sheds light on how U.S. public companies have navigated crypto holdings and accounting practices over the past decade.
ASU 2023-08, the Financial Accounting Standards Board’s (FASB) newly enacted rule, aims to bring clarity and consistency to crypto asset reporting with the mandate for fair value reporting. Moon’s research, which examined a comprehensive set of companies from 2013 to 2022, looks at the exponential rise in corporate crypto investments and the diverse, and often inconsistent, ways firms have reported them.
In “Accounting for Cryptocurrencies,” Moon and his co-authors work to better understand this pivotal point in financial reporting with research that dives into why firms hold crypto—whether for mining, payment acceptance, or investment—and how reporting practices have evolved to meet this current moment. The work is published in the Journal of Accounting Research.
Keep reading to learn more about Moon’s research and why it matters right now.
Why do companies hold cryptocurrencies, and how has this changed over time?
Companies hold cryptocurrency for three main reasons: they mine it, they accept it as payment, or they consider it an investment. Early on, most businesses kept crypto because customers used it to pay for goods and services. Around 2017, that trend declined, and more companies began mining crypto themselves. Today, mining accounts for about half of corporate crypto holdings, while payment acceptance and investment make up the rest.
What were the main challenges companies face when trying to report cryptocurrency holdings in their financial statements?
Until the end of 2023, there were no official rules on how companies should report cryptocurrency on their financial statements. Back in 2018, the Big Four accounting firms (Deloitte, PwC, EY, and KPMG) stepped in with guidance, suggesting that crypto be treated like intangible assets, similar to things like patents or trademarks. This is known as the impairment model.
What is the difference between the ‘fair value model’ and the ‘impairment model’ for accounting crypto assets, and why does it matter?
The two accounting methods differ in how they handle changes in crypto value. The fair value model updates the value of a company’s crypto to match current market prices every reporting period. If the price goes up or down, the change shows up on the company’s income statement as a gain or loss.
The impairment model only lets companies record losses when the value drops below what they paid. If the price goes up, they can’t record the increase.
The difference in the two approaches can best be seen when crypto prices rise. Under the impairment model, companies’ balance sheets understate the true value of the crypto since the gains cannot be recorded. The fair value model allows companies to adjust the balance sheet value of crypto as market prices change.
What factors led ASU 2023–08 to favor fair value reporting?
When the FASB was trying to decide if they should add crypto accounting to their standard setting agenda, they reached out to the public for feedback. The response was overwhelming and most practitioners and firms called for the use of the fair value model.
How do big accounting firms, like Deloitte or PwC, influence how companies report their crypto holdings?
When there aren’t official rules for complex issues like crypto accounting, the Big Four firms often step in to guide companies. In 2018, they recommended using the impairment model, which they viewed as most appropriate based on existing standards. After that, most companies switched from fair value reporting to the impairment approach.
Their guidance in 2018 was based on what was allowed under the standards at that time. With the new rule in place, the firms will likely help clients manage the transition.
Does using fair value accounting for crypto make a company’s stock price more volatile or its earnings reports more useful to investors?
The primary downside of using a fair value model for a risky asset like crypto is how volatility affects earnings. Moon’s research suggests that stock price volatility increases for firms using the fair value model, and it doesn’t appear the model makes earnings more useful for investors. That said, the results should be viewed cautiously because the study’s sample largely consisted of smaller companies.
Why does this research matter right now?
This research matters because more companies are investing in cryptocurrency. That trend is only expected to grow. This research looks at how businesses handled crypto before official rules came out in 2023, showing that many treated it like traditional investments. This provides a baseline against which future research can evaluate the new rule.
The research also warns that the fair value approach could make stock prices more volatile without necessarily making earnings reports more useful for investors.
More information:
Chelsea M. Anderson et al, Accounting for Cryptocurrencies*, Journal of Accounting Research (2025). DOI: 10.1111/1475-679x.70018
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A changing reporting landscape at the intersection of accounting and cryptocurrency (2025, November 17)
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Wrapping up 204 days in orbit, three Chinese astronauts flew back to Earth aboard a Shenzhou spacecraft Friday, leaving three crewmates behind on the Tiangong space station with a busted lifeboat.
Commander Chen Dong, concluding his third trip to space, and rookie crewmates Chen Zhongrui and Wang Jie touched down inside their spacecraft at the Dongfeng landing zone at 1:29 am EST (06:29 UTC) Friday. The parachute-assisted landing occurred in the mid-afternoon at the return zone, located in the remote Gobi Desert of northwestern China.
Chinese space officials upended operations on the country’s Tiangong space lab last week after astronauts found damage to one of two Shenzhou return capsules docked at the station. The China Manned Space Agency, run by the country’s military, announced changes to the space station’s flight plan November 4, the day before three crew members were supposed to depart and fly home.
Chen and his crewmates were preparing to board the Shenzhou 20 spacecraft for the ride back to Earth a few days after the arrival of three replacement crew members on the newly launched Shenzhou 21 capsule. Shenzhou 20 is the same spacecraft that launched Chen’s crew in April.
But a little more than a week ago, Chinese officials said the Shenzhou 20 spacecraft was “suspected of being impacted by small space debris” and confirmed the return trip would be postponed. Officials provided no additional details.
China’s human spaceflight agency released a cryptic statement earlier this week saying preparations were underway for the crew’s undocking and landing, but the circumstances of the return remained opaque until hours before the astronauts’ homecoming. Finally, officials confirmed the details of the return to Earth late Thursday.
“Based on preliminary analysis of photographs, design review, simulation analysis, and wind tunnel tests, a comprehensive assessment determined that the Shenzhou 20 manned spacecraft’s return capsule window glass had developed a minor crack, most likely caused by an external impact from space debris,” the China Manned Space Agency wrote on Weibo, the Chinese social media platform. “This does not meet the release conditions for a safe manned return.”
Chen Dong, commander of the Shenzhou 20 mission, arrives at the Dongfeng landing site in the Gobi Desert, Inner Mongolia, China, after landing on November 14, 2025.
Photograph: STR/Getty Images
Swapping Spacecraft in Low-Earth Orbit
With their original spacecraft deemed unsafe, Chen and his crewmates instead rode back to Earth on the newer Shenzhou 21 craft that launched and arrived at the Tiangong station October 31. The three astronauts who launched on Shenzhou 21—Zhang Lu, Wu Fei, and Zhang Hongzhang—remain aboard the nearly 100-metric ton space station with only the damaged Shenzhou 20 craft available to bring them home.
China’s line of Shenzhou spaceships not only provide transportation to and from low-Earth orbit, they also serve as lifeboats to evacuate astronauts from the Chinese space station in the event of an in-flight emergency, such as major failures or a medical crisis. They serve the same role as Russian Soyuz and SpaceX Crew Dragon vehicles flying to and from the International Space Station.
Another Shenzhou spacecraft, Shenzhou 22, “will be launched at a later date,” the China Manned Space Agency said in a statement. Shenzhou 20 will remain in orbit to “continue relevant experiments.” The Tiangong lab is designed to support crews of six for only short periods, with longer stays of three astronauts.
Services provided by accountants can help small business owners manage cash flow better, a study commission by Intuit Software has reported, but the role of accounting is changing as more technologies such as artificial intelligence (AI) are embedded into accounting software packages.
The study, conducted by Chris Brauer, director of innovation in the Institute of Management Studies (IMS) at Goldsmiths, University of London, Symmetry Research and the Association of Chartered Certified Accountants (ACCA), noted that there is a substantial opportunity for both SMEs and accountants to drive meaningful economic impact.
Based on a survey of 4,000 small and mid-sized businesses, the study reported that 71% of the businesses polled agree that professional accounting services improve cash flow management, which makes both current and future business decision-making run more smoothly. In addition, 73.1% said that using professional accounting services has strengthened their financial reporting, and this alone has offered increasing opportunities to get bank loans or government grants.
Around 80% of SME leaders who have used an accountant said it has had either a moderate, significant or transformational effect on their financial literacy. Accountants can also serve an integral role as strategic financial advisors, counselling on business planning, tax compliance and financial management. Among the challenges facing the accounting profession is that the trajectory AI is taking may well remove much of the work they need to do in terms of how small business owners manage their finances.
Marianna Tressel, executive vice-president at Intuit, believes the way AI changes business is just getting started, adding: “We’re just at the beginning, at the first few innings of what will be possible with AI and how people use AI.”
According to Tressel, AI is an accelerant in everybody’s work: “We were talking to a lot of small businesses about how they use AI for all sorts of elements of their work. It’s an accelerant, but also it’s a disruptor.”
Tressel believes conversational AI powered by a custom large model changes human computer interaction and this is something Intuit has begun doing, with a custom LLM based on open source technology, which, she said, can handle queries extremely cost effectively. This potentially has an impact on the role of an accountant.
Aaron Patrick, head of accounts at the UK-based cloud accountancy firm Boffix, said: “If we’re really honest, accountants and bookkeepers are starting to become less relevant.” However, for Patrick, there is now an opportunity for accountants and book keepers to become business advisers and start helping clients by showing value to their clients.
“Niche expertise is becoming a game changer for accountants. By specialising in specific sectors [such as] e-commerce, we can offer tailored advice that directly impacts a client’s success. Coupled with proactive communication, we’re not just checking boxes anymore – we’re building long-term relationships where we actively help SMEs make strategic decisions, thrive, and grow.”
For Intuit customers, this opportunity is made possible through its $12bn acquisition of MailChimp in 2021. The developer of QuickBooks used the acquisition as an opportunity to re-engineer its applications as a new platform.
In October, Intuit launched Intuit Accountant Suite, an AI-native platform, which the company claims provides accounting firms with the tools they need to scale and manage their clients, firms and teams, all in one place.
Discussing how the technology that is built into the new Intuit platform changes the role of an accountant, Patrick said: “We now have an opportunity to understand why sales has gone up or why expenses has gone down.”
According to Patrick, the new Intuit platform provides access to data silos: “As accountants, we’re going to have the opportunity not only to be able to tell the story based on the numbers, but understand what’s happened with the CRM system, such as assess how a company’s marketing is going.”
A study by Boston Consulting Group (BCG) suggests that organisations that lead in technology development are gaining a first-mover advantage when it comes to artificial intelligence (AI) and using agentic AI to improve business processes.
What is striking about BCG’s findings, according to Jessica Apotheker, managing director and senior partner at Boston Consulting Group, is that the leading companies in AI are mostly the same ones that were leaders eight years ago.
“What this year’s report shows is that the value gap between these companies and others is widening quite a bit,” she says. In other words, BCG’s research shows that organisations that have invested disproportionately in technology achieve a higher return from that investment.
Numerous pieces of research show that a high proportion of AI initiatives are failing to deliver measurable business success. BCG’s Build for the future 2025 report shows that the companies it rates as the best users of AI generate 1.7 times more revenue growth than the 60% of companies in the categories it defines as stagnating or emerging.
However, there is certainly a case to build on previous initiatives such as robotic process automation (RPA).
Speaking at the recent Forrester Technology and Innovation Summit in London, Bernhard Schaffrik, principal analyst at Forrester, discussed how agentic AI can be built on top of a deterministic RPA system to provide greater flexibility than what existing systems can be programmed to achieve.
The analyst firm uses the term “process orchestration” to describe the next level of automating business processes, using agentic AI in workflow to handle ambiguities far more easily than the programming scripts used in RPA.
“Classic process automation tools require you to know everything at the design stage – you need to anticipate all of the errors and all the exceptions,” says Schaffrik.
He points out that considering these things at design time is unrealistic when trying to orchestrate complex processes. But new tools are being developed for process orchestration that rely on AI agents.
A strong data foundation
Boston Consulting Group (BCG) says prerequisites for the successful roll-out of AI agents include strong data foundations, scaled AI capabilities and clear governance.
Standardisation of data is a key requirement for success, according to Twig. “A big part of the issue is data,” he says. “AI is only as strong as the information it runs on, and many companies don’t have the standardised, consistent datasets needed to train or deploy it reliably.”
Within the context of agentic AI, this is important to avoid miscommunications both at the technology infrastructure level and in people’s understanding of the information. But the entire data foundation does not have to be built all at once.
BCG’s Apotheker says companies can have an enterprise-wide goal to achieve clean data, and build this out one project at a time, providing a clean data foundation on which subsequent projects can be built. In doing so, organisations are able to gain a better understanding of the enterprise data these projects require while they ensure that the datasets are clean and good data management practices are followed.
A working agentic AI strategy relies on AI agents connected by a metadata layer, whereby people understand where and when to delegate certain decisions to the AI or pass work to external contractors. It’s a focus on defining the role of the AI and where people involved in the workflow need to contribute.
This functionality can be considered a sort of platform. Scott Willson, head of product marketing at xtype, describes AI workflow platforms as orchestration engines, coordinating multiple AI agents, data sources and human touchpoints through sophisticated non-deterministic workflows. At the code level, these platforms may implement event-driven architectures using message queues to handle asynchronous processing and ensure fault tolerance.
Data lineage tracking should happen at the code level through metadata propagation systems that tag every data transformation, model inference and decision point with unique identifiers. Willson says this creates an immutable audit trail that regulatory frameworks increasingly demand. According to Willson, advanced implementations may use blockchain-like append-only logs to ensure governance data cannot be retroactively modified.
Adapting workflows and change management
Having built AI-native systems from the ground up and transformed the company’s own product development processes using AI, Alan LeFort, CEO and co-founder of StrongestLayer, notes that most organisations are asking completely the wrong questions when evaluating AI workflow platforms.
“The fundamental issue isn’t technological, it’s actually organisational,” he says.
Conway’s Law states that organisations design systems that mirror their communication structures. But, according to LeFort, most AI workflow evaluations assume organisations bolt AI onto existing processes designed around human limitations. This, he says, results in serial decision-making, risk-averse approval chains and domain-specific silos.
When you try to integrate AI into human-designed processes, you get marginal improvements. When you redesign processes around AI capabilities, you get exponential gains Alan LeFort, StrongestLayer
“AI doesn’t have those limitations. AI can parallelise activities that humans must do serially, doesn’t suffer from territorial knowledge hoarding and doesn’t need the elaborate safety nets we’ve built around human fallibility,” he adds. “When you try to integrate AI into human-designed processes, you get marginal improvements. When you redesign processes around AI capabilities, you get exponential gains.”
StrongestLayer recently transformed its front-end software development process using this principle. Traditional product development flows serially. A product manager talks to customers, extracts requirements and then hands over to the user experience team for design, the programme management team then approves the design, and developers implement the software. It used to take 18-24 months to completely rebuild the application in this process, he says.
Instead of bolting AI onto this process, LeFort says StrongestLayer “fundamentally reimagined it”.
“We created a full-stack prototyper role-paired with a front-end engineer focused on architecture. The key was building an AI pipeline that captured the contextual knowledge of each role: design philosophy, tech stack preferences, non-functional requirements, testing standards and documentation needs.”
As a result of making these workload changes, he says the company was able to achieve the same outcome from a product development perspective in a quarter of the time. This, he says, was not necessarily achieved by working faster, but by redesigning the workflow around AI’s ability to parallelise human sequential activities.
LeFort expected to face pushback. “My response was to lead from the front. I paired directly with our chief product officer, Joshua Bass, to build the process, proving it worked before asking others to adopt it. We reframed success for our front-end engineer around velocity and pioneering new ways of working,” he says.
For LeFort, true speed to value comes from two fundamental sources: eliminating slack time between value activities and accelerating individual activity completion through AI automation. “This requires upfront investment in process redesign rather than quick technology deployment,” he says.
LeFort urges organisations to evaluate AI workflow platforms based on their ability to enable fundamental process transformation, rather than working to integrate existing inefficiencies.
Getting agentic AI decision-making right
Research from BCG suggests that the best way to deploy agents is through a few high-value workflows with clear implementation plans and workforce training, rather than in a massive roll-out of agents everywhere at once.
There are different models with different strengths. We want to use the best model for each task Ranil Boteju, Lloyds Banking Group
One of the areas IT leaders need to consider is that their organisation will more than likely rely on a number of AI models to support agentic AI workflows. For instance, Ranil Boteju, chief data and analytics officer at Lloyds Banking Group, believes different models can be tasked with tackling each distinct part of a customer query.
“The way we think about this is that there are different models with different strengths, and what we want to do is to use the best model for each task,” says Boteju. This approach is how the bank sees agentic AI being deployed.
With agentic AI, problems can be broken down into smaller and smaller parts, where different agents respond to each part. Boteju believes in using AI agents to check the output from other agents, rather like acting as a judge or a second-line colleague acting as an observer. This can help to cut erroneous decision-making arising from AI hallucinations when the AI model basically produces a spurious result.
IT security in agentic AI
People in IT tend to appreciate the importance of adhering to cyber security best practices. But as Fraser Dear, head of AI and innovation at BCN, points out, most users do not think like a software developer who keeps governance in mind when creating their own agents. He urges organisations to impose policies that ensure the key security steps are not skipped in the rush to deploy agentic AI.
“Think about what these AI agents might access across SharePoint: multiple versions of documents, transcripts, HR files, salary data, and lots more. Without guardrails, AI agents can access all this indiscriminately. They won’t necessarily know which versions of these documents are draft and which are approved,” he warns.
The issue escalates when an agent created by one person is made available to a wider group of colleagues. It can inadvertently give them access to data that is beyond their permission level.
Dear believes data governance needs to include configuring data boundaries, restricting who can access what data according to job role and sensitivity level. The governance framework should also specify which data resources the AI agent can pull from.
In addition, he says AI agents should be built for a purpose, using principles of least privilege: “Just like any other business-critical application, it needs to be adequately tested and ‘red-teamed’. Perform penetration testing to identify what data the agent can surface, to whom, and how accurate the data is. Track and audit which agents are accessing which data and for what purpose, and implement real-time alerts to flag unusual access patterns.”
A bumpy ride ahead
What these conversations with technology experts illustrate is that there is no straightforward path to achieving a measurable business benefit from agentic AI workflows – and what’s more, these systems need to be secure by design.
Organisations need to have the right data strategy in place, and they should already be well ahead on their path to full digitisation, where automation through RPA is being used to connect many disparate workflows. Agentic AI is the next stage of this automation, where an AI is tasked with making decisions in a way that would have previously been too clunky using RPA.
However, automation of workflows and business processes are just pieces of an overall jigsaw. There is a growing realisation that the conversation in the boardroom needs to move beyond the people and processes.
BCG’s Apotheker believes business leaders should reassess what is important to their organisation and what they want to focus on going forward. This goes beyond the build versus buy debate: some processes and tasks should be owned by the business; some may be outsourced to a provider that may well use AI; and some will be automated through agentic AI workflows internally.
It is rather like business process engineering, where elements powered by AI sit alongside tasks outsourced to an external service provider. For Apotheker, this means businesses need to have a firm grasp of what part of the business process is strategically important and can be transformed internally.
Business leaders then need to figure out how to connect the strategically important part of the workflow to what the business actually outsources or potentially automates in-house.
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