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Forbes
5 days ago
- Business
- Forbes
Why The Traditional 10-K Is Ripe For An AI Overhaul
William Tarr is the co-founder and CEO of Tergle, an audit-focused AI software startup in San Francisco. The final night of a year-end close feels like what I imagine air traffic control would be like if it ran on paper. Bank confirmations, leasing schedules and goodwill tests crowd a war room-like table while someone refreshed the SEC's EDGAR uploader every 10 minutes. Even in the age of AI, most of that choreography still looks the same, only the spreadsheets have become denser and the filing clock less forgiving. The modern 10-K, an annual report most public companies are required to submit to the U.S. Securities and Exchange Commission (SEC), often runs hundreds of pages, embeds thousands of XBRL tags and must track business lines that range from China to the cloud. Yet, some firms continue to assemble it with the digital equivalent of masking tape. An Area Poised For Disruption Regulators have begun to signal that this status quo cannot last. On March 27, 2025, the SEC devoted an entire public roundtable to AI in finance to discuss how smarter tools may support automation, investor communication, risk management and more. In my view, the timing is no accident: The number of public companies forced to restate or withdraw financial statements climbed to a nine-year peak, with 140 restatements in just the first 10 months of 2024. That's more than double the figure four years earlier, the Financial Times reported (registration required). High-profile missteps, like Macy's $151 million in false bookkeeping entries, have made headlines and can erode investor confidence. Behind those headlines lies a structural problem. Financial reporting has outgrown the batch-process mindset of the 20th-century ledger. Transactions now stream in real time, yet accountants reconcile them weeks later; investors consume numbers in seconds, yet issuers take up to 60 days to publish a comprehensive story. That mismatch creates a widening gap where errors or, worse, manipulations, can hide. AI is not a silver bullet, but it offers a fundamentally different posture: continuous, data-first scrutiny instead of episodic, paper-trail inspection. Consider how a filing might evolve if AI became native rather than ornamental. Journal entries could flow through a language-model pipeline as they are booked, which would allow statistical and semantic engines to flag outliers the moment they appear, like an accrual growing 10 times faster than revenue or a related-party vendor with a residential address. During drafting, another model could cross-reference every XBRL tag with the surrounding prose, catching the mislabeled lease liability before it summons an SEC comment letter. Even the narrative sections would change: Transformers trained on years of peer filings could alert preparers that their risk-factor discussion omits a topic the market now expects, such as generative AI cybersecurity. Caution Is Warranted Skepticism remains healthy, however. Language models can hallucinate, and finance leaders justifiably fear black-box decisions that lack an audit trail. The answer is not to bury AI but to govern it. Every inference must be logged; role-based controls should separate data ingestion, model tuning and approval; and audit committees ought to receive AI exception reports alongside traditional control matrices. With those guardrails, I believe the technology looks less like a threat to professional judgment and more like a tireless junior who never sleeps and never tires of ticking and tying. Firms can also explore best-practice playbooks that are beginning to crystallize, such as the Public Company Accounting Oversight Board's July 2024 generative AI "Spotlight,' which highlights the need to keep a human reviewer accountable for each AI-assisted step, among other strategies. The SEC's 2023 cybersecurity disclosure rules likewise require boards to describe how they govern technology risk—obligations that extend to any AI system touching financial statements. Companies operating across the Atlantic must map those workflows to the EU AI Act, which imposes explicit data governance, transparency and human oversight duties. In practical terms, that means tightening access controls so sensitive ledgers never leak into public large language models (LLMs), documenting prompt engineering as rigorously as journal-entry support and training staff to recognize—and correct—model hallucinations with the same skepticism they apply to a junior analyst's shaky reconciliation. Looking Ahead Early adopters are already discovering practical entry points. Revenue-recognition waterfalls and inventory roll-forwards lend themselves to anomaly detection because the data is structured and the business logic is explicit. Optical character recognition systems that once balked at faint fax copies can now parse documents with high accuracy, opening a path to automate footnote support files long trapped in PDF purgatory. Once confidence builds, firms can expand into narrative coherence checks and real-time benchmarking, turning what used to be a frantic year-end scramble into a measured, incremental close. For finance chiefs weighing the leap, the calculus is shifting from 'if' to 'when.' KPMG's "2024 Global AI in Finance" report (download required) found that 95% of CFOs expect to be using generative AI in some phase of reporting within three years, up from less than 40% at the time of the survey. In my view, the first movers will not only shorten close cycles, they will also accumulate proprietary training data that becomes a moat that's harder for latecomers to replicate than any single piece of software. In the 1930s, the newly created SEC demanded annual transparency and, over time, the 10-K was born. Today, the form still serves, but I believe its preparation often doesn't match the velocity or complexity of modern commerce. Whether prompted by rising restatements, investor impatience or regulatory prodding, the shift toward using AI for disclosures feels inevitable. The firms willing to re-code their reporting DNA now can position themselves to greet that future with confidence. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Forbes
21-03-2025
- Business
- Forbes
The Changing Nature Of Auditing: Considerations For Incorporating AI
William Tarr is the co-founder and CEO of Tergle, an audit-focused AI software startup in San Francisco. getty Seemingly unknown in the world of auditing, the age of AI is dawning. AI-driven financial ecosystems demand new levels of scrutiny and expertise, something that cannot be done with outdated manual methods. Auditing firms must look toward new technologies or risk being dragged into irrelevance. A century ago, a ledger book, journal, account sheet and slide rule would be sufficient to audit a national public firm. The economy was profoundly more explicable. Steel production saw the processing of a few simple inputs and returned an easily quantifiable output: tonnage of processed metal. Chemicals, similarly, took in industrial scales of unprocessed materials and returned literage of product, while early automobile supply chains could be simply understood and largely remained within national borders. In the decades after World War II, an era of globalization saw supply chains and monetary transfers become more complex, and new technologies proliferated; the so-called 'Information Age' began. After some adjustment, the tools deployed for the auditing of infant technology companies changed largely in lockstep, with new data processing that arrived in the mid-1980s (Microsoft Excel launched in 1985). The internet and mobile technologies sparked further innovation in the financial services space at the turn of the century. However, as the co-founder of an AI-powered auditing software, it is soberingly clear to me that this is where any meaningful progression stalled. In my view, failure to adapt has limited the reliability of audits. Even absent the $25 million fine the Public Company Accounting Oversight Board (PCAOB) gave KPMG Netherlands over internal exam cheating, 2024 saw the highest yearly cumulative levels of sanction at $12.4 million in fines to audit firms and individuals. This follows record years in 2023 and 2022 of $11.85 million and $11.02 million respectively, according to the Financial Times (paywall). Many of these fines have fallen on the 'Big Four' firms, and they highlight broader areas where AI-driven processes can help. For example, in auditing Aegean Marine Petroleum Network, PwC 'ignored red flags' such as a nonexistent address and client businesses reporting addresses in residential blocks, the Financial Times article also said. Such formulaic practices as checking addresses and verifying clients are ripe for automation. Outside the U.S., the Financial Reporting Council's investigation into PwC and EY in their dealings with London Capital & Finance concluded they had 'failed to understand the business and raised the possibility that there could be 'material misstatements' in the company's accounts,' the Guardian reported. Assurance practices are another area where there's an opportunity for technology innovation. More egregiously, and something I find perplexing, many audits still rely on data sampling, which can risk sampling errors and incomplete analysis of financial documents. Instead, utilizing LLMs that can collate large sources of information, including learning from peer entities to eliminate any failures to understand business models and conduct whole-scale testing, should be encouraged as a preventative measure. Those who remain skeptical about the introduction of AI into a field that fundamentally risks liability for material failures abound. It is no surprise, however, given the many years of education required for the field. In response, I want to help allay those fears: In no way do I anticipate the role of auditing to be automated away in some Isaac Asimov-esque future. It would be as if there were 10 very intelligent interns sitting on your shoulder completing repetitive tasks that require little qualitative intervention and flagging discrepancies as and when they arise. Venture capital-driven bullishness in agentic AI (registration required) has contributed to the proliferation of companies seeking to automate back-office processes. I've noticed more companies (my own included) aiming to bring AI into the auditing profession. AI and data analytics advancements are contributing to the $313 billion market value the auditing services market is expected to achieve by 2031. Of course, some challenges remain in the implementation of new software, such as the need for high-quality training data. This can be difficult given both the intensity of regulatory control over financial data and the dearth of publicly available audit materials not sourced from large, public corporations. However, I expect these concerns to dissolve as model training becomes more effective and can be bootstrapped through synthetic data generation. In addition, the past few years have seen AI progress at an unprecedented rate. For instance, optical readers have become up to 99% accurate. There is no reason to say that the current challenges render AI's implementation impossible. The excuses of the past are becoming increasingly hard to defend; the time for AI adoption is now. Firms seeking to integrate AI should first identify tasks best suited for automation, such as anomaly detection, document processing and risk assessment. If exploring external AI solutions, it's important to consider whether they can integrate with existing accounting systems, enhance precision and ensure compliance. It's also critical to select AI providers that prioritize transparency, security and explainability—those that enhance, rather than obscure, the auditing process. The firms that move first and modernize workflows while keeping auditors in control will define the profession's future. There is increasing optimism surrounding the opportunities that exist in agentic AI, and considering the state of auditing, it's easy to understand why. The status quo is untenable. Audit failures, rising fines and outdated methods indicate that traditional auditing can no longer keep pace. Firms that fail to embrace AI could be left behind. Those who cling to the old ways will likely be swept away; in the age of AI, it's adapt or die. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
