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AI council launches finance certification in Singapore. Tue, 13th Oct 2026 (Today) The AI Assurance Council has opened examinations in Singapore for its Certified AI in Finance & Accounting credential. The launch reflects survey data on rising AI use in the profession and limited formal training. The certification is designed to test whether finance and accounting professionals know when to rely on AI output, when to verify it, and when to escalate concerns. Candidates will sit a remotely proctored two-hour exam based on applied scenarios rather than knowledge of individual tools. Two levels are available. Fundamentals is aimed at finance and accounting professionals who use AI in analysis, close, and reporting. Professional is aimed at senior finance leaders responsible for decisions about how AI is used in their functions. Each credential is valid for two years and appears on a public register that employers can check. Fees are USD $695 for Fundamentals and USD $895 for Professional, and a video course is included for candidates. Training gap The launch follows a series of surveys suggesting AI adoption in finance is outpacing workforce preparation. Research by Chartered Accountants Worldwide and Ipsos, published by the Institute of Singapore Chartered Accountants, found that 89% of Singapore accounting professionals expect AI to significantly change how they do their jobs within five years. The same survey found that 34% said the value accountants provide could be replaced by automated systems. Only 45% of respondents said they had taken part in AI training through their employer, while 51% cited a lack of skills and training as the biggest barrier to AI adoption in the profession. Another recent marker came from Gartner, which said low AI literacy is now "the most significant barrier" finance leaders must address. The concern comes as AI is increasingly used in work that can feed into forecasts, reconciliations, and disclosures reviewed by auditors, audit committees, and regulators. Workiva's 2026 survey of 2,272 finance, risk, and sustainability professionals pointed to a similar tension. It found that 84% of executives said they were at least somewhat confident in AI output without human review, yet 26% said internal audits had caught AI errors that reached boards or external audiences. Regulatory backdrop The certification launch also comes against Singapore's regulatory backdrop. Proposed guidelines from the Monetary Authority of Singapore on AI risk management would require financial institutions to ensure that their internal expertise matches the AI systems they use. That focus on oversight has become more prominent as finance teams use AI in work that may ultimately support external reporting or internal control processes. The issue is less about whether AI is present in finance functions than about who is accountable when its output is wrong. The AI Assurance Council was founded by Danny Heng and Durga Rajesh R, the organisation's Director of Assurance and Technology. According to the organisation, Heng previously worked in finance software sales across Asia Pacific at BlackLine, Thomson Reuters, and Epiq. In the announcement, Heng linked the new certification to changes he had seen in finance teams as automation spread. "I've seen both sides of automation in finance. Processes got faster and more accurate, and some of the people who used to do that work were no longer needed," said Danny Heng, Executive Director, AI Assurance Council. "AI is doing the same thing again, faster and to far more roles. The professionals who stay valuable will be the ones who can look at what AI produces and tell when it's wrong. Right now they have no way to prove that. This certification gives them one." According to the council, Rajesh came to the issue from an engineering background after building AI agents, retrieval systems, and blockchain infrastructure for regulated financial firms operating under Singapore's technology risk rules. It said that experience shaped its focus on evidence, verification, and documented judgment rather than tool familiarity alone. The council describes itself as an independent certification body focused on the use of AI in professional work, starting with finance and accounting. It aims to address the absence of a shared standard for deciding when AI-generated work can be relied on, when it should be checked more closely, and when it should be escalated. Alongside the paid exams, the organisation has also opened a free AI Readiness Check for public use. The short assessment is intended to give finance professionals an initial view of their preparedness to work with AI in their roles.

Thomson Reuters sued over Casetext acquisition, integration. October 2, 2026, 11:46 am CDT Thomson Reuters is facing a purported antitrust class action lawsuit over its massive $650 million acquisition and integration of Casetext into its Westlaw system. (Image from Shutterstock) Thomson Reuters is facing a purported antitrust class action lawsuit over its massive $650 million acquisition and integration of Casetext into its Westlaw system. In an Oct. 1 complaint filed with the U.S. District Court for the Northern District of California, the Rubin Law Office alleged that Thomson Reuters violated state and federal antitrust law by purchasing a lower priced competitor and then folding it into its more expensive ecosystem, forcing Casetext users to pay more per month. Calling Casetext a "nimble, budget-conscious horizontal challenger" to Westlaw, the Rubin Law Office accused Reuters of executing a "killer acquisition" that was designed to get rid of a competitor. Casetext famously launched its generative artificial intelligence-powered legal research tool CoCounsel on MSNBC's Morning Joe in March 2023, setting off an AI arms race in the legal industry. "Faced with immediate technological obsolescence and a profound threat to its structural market moat, [Thomson Reuters] did not compete on the merits," the Rubin Law Office, based in Carlsbad, California, said in its complaint, adding that Thomson Reuters' technological capabilities before buying Casetext were "lagging," and that Westlaw "lacked any standalone or integrated generative AI capabilities." "They did not spend the years or capital required to organically build a comparable large language model application layer. Instead, Defendants used their massive capital reserves to short-circuit the competitive process," according to the complaint. According to the complaint, after the Casetext acquisition and integration into Westlaw, the Rubin Law Office went from paying $32.50 per month for Casetext to $111.48 monthly for Westlaw Classic, complete with a three-year mandatory subscription with annual 4% increases built in. "By permanently removing the independent Casetext alternative from the legal ecosystem, Westlaw eliminated the downstream contract-negotiation leverage and pricing discipline that historically suppressed Westlaw's standard contract rates. Free from the threat of user defection to Casetext, Westlaw has been empowered to maintain and escalate its standard baseline subscription fees at artificial, supra-competitive levels across its entire domestic customer base," the law firm stated in its complaint. The antitrust complaint is requesting that the court certify a class action on behalf of users throughout the country, as well as a subclass of California-based users. It is also requesting treble damages under the Clayton Antitrust Act and the California Business and Professions Code, as well as an order for Thomson Reuters to divest, spin off or reestablish Casetext as an independent company. Thomson Reuters did not respond to a request for comment from the ABA Journal. A spokesperson told Bloomberg Law that "Thomson Reuters has not been served with any complaint related to this matter." "Until we have received and reviewed any such filing, we are not in a position to comment," the spokesperson said to Bloomberg Law. Discover more Consult Lawyers Legal News Subscription Court Case Updates

If AI can do the work, then what are you actually charging for? Nadya Britton October 2, 2026 As tax prep becomes automated, what clients pay for is changing, moving from the work itself to a question of whether someone is in their corner before a problem becomes a surprise. That shift is going to force tax, audit & accounting firms to stop billing by the hour and start pricing for their judgment instead. Key insights: * Client dissatisfaction with CPAs is driven almost entirely by errors, poor communication, and a lack of proactive planning - not by fees. Yet advisory work, the service that fixes exactly those problems, remains priced lower than routine tax preparation. * Clients aren't paying firms to use AI; rather, they're paying to avoid being blindsided. Nearly 90% of clients say they expect AI-enabled quality improvements, but the real factor is whether the firm catches a problem before the client has to ask about it. * The freed-up time for accountants due to AI itself isn't billable. However, what a firm does with it is, and redirecting that time into proactive advice for clients could be crucial. Every tax season, firms bill their clients for hours spent preparing returns, checking figures, and filing paperwork. That has always been the backbone of the tax, audit & accounting business. However, there is a shift going on, and it will force firms to answer a harder question: If a machine can do the compliance work, what exactly are clients paying for? The data suggests the answer isn't technical skill; instead, it's presence, judgment, and trust - and unfortunately, most firms haven't figured out how to price for any of those. The work clients complain about isn't the work you'd expect. The recently released Year-Round Tax Advisor Report, published jointly by the Thomson Reuters Institute (TRI) and Jump, found that when clients are dissatisfied with their CPAs, fees are almost never the reason. Errors and missed items, poor communication, and a lack of proactive planning account for more than three-quarters of clients' complaints, while fees rank a distant fourth, cited by less than 10% of dissatisfied clients. Clients who expressed any tax-related fear were 3.6-times more likely to be unhappy with their CPA, and that pattern held across every fear type the study measured, from surprise tax bills to capital gains anxiety to RMD confusion. The issues clients are having, according to the data, isn't the return itself; instead, it's the sense that they hope someone catches a problem before it becomes a surprise. Tax strategy came up in at least 79% of advisor-client meetings every month of the year, not just during filing season, according to the Tax Advisor Report, and clients initiated those conversations more often than almost any other planning topic. Clearly, the demand for a year-round advisor relationship already exists. The question is whether the CPA meets it, or whether a financial advisor becomes the client's real tax resource instead. Compliance work is commoditizing, and firms know it. TRI's 2026 State of Tax Professionals Report shows the profession is aware of this shift, even if pricing hasn't caught up. More than 70% of firms already automate up to a quarter or more of their tax workflows, and much of the routine work is being absorbed faster than the pricing model is adjusting to it. Yet, tax firm profit margins show a different picture. Individual tax return preparation remains the highest-margin service that firms offer, with a 28% profit margin. Advisory services - such as business consulting, decision support, tax strategy, and financial planning - all land in the 20% to 24% range, despite almost three-quarters of survey respondents saying their clients strongly want a trusted advisor relationship rather than just someone to file their taxes. Indeed, tax firms are already sitting on the service that clients value most, yet they are pricing it lower than the routine work that AI is best positioned to take over. Hourly billing is still the dominant pricing model, used by 78% of firms, as they continue to tie pricing to time spent rather than value delivered. That model made sense when the return itself was the hard part; but now, it makes far less sense when what actually sets a firm apart in the client's mind is catching something on their return before they even know to ask about it. Clients are already grading firms on this. Today, clients are no longer just hoping their tax professional uses AI well, according to the Thomson Reuters Future of Professionals Report 2026. They're actively judging firms on it. Almost 90% of corporate tax clients say it's very important, or even essential, to receive AI-enabled quality improvements from the outside tax, audit & accounting firms with whom they work. In fact, nearly one-third of clients are already reconsidering, or planning to reconsider, their external firm relationships within the year. Clearly, clients aren't asking firms to automate for automation's sake. They're asking whether the firm is still catching problems as the routine work gets faster. None of this, of course, means that firms should panic about being replaced. However, it does mean they may need to focus on a strategic direction. They can go one of three paths: i) use automation to free up experienced people for advisory work; ii) use the extra capacity to serve more clients at the same headcount; or iii) rebuild the model around ongoing insight instead of once-a-year compliance. Not matter which path is chosen, what doesn't work is drifting between all three while still billing by the hour for work that AI tools can now finish in minutes. Tax firms are already sitting on the service that clients value most, yet they are pricing it lower than the routine work that AI is best positioned to take over. Those tax firms already succeeding at this transition share a pattern. They've stopped treating tax strategy as a once-a-year event and started treating it as a year-round relationship. They've moved toward value-based or retainer pricing that reflects judgment rather than time spent. And they've been deliberate about which client fears, which niches, and which moments of proactive outreach are worth building a service around. Already, it's showing up in the numbers: Firms with the highest profit margins use value-based pricing 61% of the time, compared with 54% among the lowest-margin tier, a gap that tracks the shift from billing for time to billing for judgment almost exactly. So if AI tools can now draft the return, flag the anomaly, and model the Roth conversion scenario in seconds, what are you charging for? The answer that it's no longer the mechanics of the work; rather, it's the relationship that catches the surprise before it happens, the advice that arrives before the client knows to ask for it, and the judgment that a tool simply cannot replaced. Tax firms that build their pricing around that reality will be better positioned as routine work stops being a billable item on its own. Follow Thomson Reuters on social.