Bad actors, primarily distressed firms, have the incentive and desire to manipulate their financial reports to hide their distress and derive personal gains. As attackers, these firms are motivated by potentially millions of dollars and the availability of many publicly disclosed and used financial modeling frameworks. Existing attack methods do not work on this data due to anti-correlated objectives that must both be satisfied for the attacker to succeed. We introduce Maximum Violated Multi-Objective (MVMO) attacks that adapt the attacker's search direction to find $20\times$ more satisfying attacks compared to standard attacks. The result is that in $\approx50\%$ of cases, a company could inflate their earnings by 100-200%, while simultaneously reducing their fraud scores by 15%. By working with lawyers and professional accountants, we ensure our threat model is realistic to how such frauds are performed in practice.

Advanced intelligent systems, particularly Large Language Models (LLMs), are significantly reshaping financial practices through advancements in Natural Language Processing (NLP). However, the extent to which these models effectively capture and apply domain-specific financial knowledge remains uncertain. Addressing a critical gap in the expansive Indian financial context, this paper introduces CA-Ben, a Chartered Accountancy benchmark specifically designed to evaluate the financial, legal, and quantitative reasoning capabilities of LLMs. CA-Ben comprises structured question-answer datasets derived from the rigorous examinations conducted by the Institute of Chartered Accountants of India (ICAI), spanning foundational, intermediate, and advanced CA curriculum stages. Six prominent LLMs i.e. GPT 4o, LLAMA 3.3 70B, LLAMA 3.1 405B, MISTRAL Large, Claude 3.5 Sonnet, and Microsoft Phi 4 were evaluated using standardized protocols. Results indicate variations in performance, with Claude 3.5 Sonnet and GPT-4o outperforming others, especially in conceptual and legal reasoning. Notable challenges emerged in numerical computations and legal interpretations. The findings emphasize the strengths and limitations of current LLMs, suggesting future improvements through hybrid reasoning and retrieval-augmented generation methods, particularly for quantitative analysis and accurate legal interpretation.

This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common themes from these earlier studies. The first theme focuses on applications of ChatGPT and LLMs in various fields of accounting and finance. The second theme utilizes ChatGPT and LLMs as a new research tool by leveraging their capabilities such as classification, summarization, and text generation. The third theme investigates implications of LLM adoption for accounting and finance professionals, as well as for various organizations and sectors. While these earlier studies provide valuable insights, they leave many important questions unanswered or partially addressed. We propose venues for further exploration and provide technical guidance for researchers seeking to employ ChatGPT and related LLMs as a tool for their research.

Generative AI's incredible speed of sinking into our everyday lives came in like a tsunami. At a record-breaking, tech-pandemic'ish growth rate, OpenAI's ChatGPT registered users reached 1 million within 5 days and 100 million within 2 months to become the fastest adopted consumer internet app. This is even more impressive considering that OpenAI's ChatGPT, while reaching 162 countries, does not allow users from mainland China, Hong Kong, Iran, Russia and parts of Africa to sign up. Mainland China and Hong Kong make up a fifth of the world's population. This didn't stop the ChatGPT frenzy from reaching Chinese shores, as its wonder has topped almost every conversation, WeChat post and search results.

Last year, a move to remote working triggered a seismic shift in how businesses maintain operational productivity, particularly its finance segment. Digital processes became increasingly present with more finance departments leaning on AI's power to make data-driven decisions and gain a sustained advantage. Six months into 2021, digital transformation is still an ongoing, slow-moving process that hasn't been fully embraced by CFOs. Traditional financial reporting solutions like the ever-present Excel still play a significant role in a world where cloud-based systems efficiently and at scale fulfill demands for speed and transparency in financial reporting. In other words, businesses are lagging.

It has never been easier to measure and monitor business operations -- the amount of data available to organizations is staggering. Access to insight provides businesses with a clear competitive advantage, but many enterprises struggle to make sense of the seemingly endless reams of data at their disposal. To overcome hurdles with data literacy, smart businesses have embraced various business intelligence (BI) solutions to collect, aggregate, translate and present business information. An invaluable asset for enterprises worldwide, BI dashboards are data visualization tools that display the status of business analytics metrics, key performance indicators (KPIs) and other important data points on a single screen. To underscore the widespread adoption of BI, note that the global business intelligence market is projected to reach USD $147.19 billion by 2025.

SAN FRANCISCO, Nov. 07, 2017 (GLOBE NEWSWIRE) -- FinancialForce, the number one customer-centric ERP cloud vendor built on the Salesforce Platform, today announced it has experienced resounding success as a result of being all in on Salesforce Einstein. Since this revolutionary, integrated set of AI technologies was debuted by Salesforce at Dreamforce 2016, FinancialForce has successfully mastered its capabilities to not only run its company on the bleeding edge of Einstein, but also bring world-class analytics and predictive capabilities to its customers. The company is currently running its internal operations on an app it created, entitled Pulse. A product of Einstein Discovery technology, Pulse quickly and easily tracks massive amounts of FinancialForce data -- everything from customer support cases, usage metrics, opportunities, survey data, financial data, communities, sales invoices, transactional data and more. Leveraging Einstein Discovery across the collected data, FinancialForce is able to unearth critical insights that help deliver specific and specialized service to customers.

Regulatory compliance has always been and will always be one of the top priorities and concerns of every financial institution (FI). Regulatory reforms following the global financial crisis of 2008 compelled FIs to make substantial investments in risk and compliance – both in terms of technology and headcount – to prevent and remediate regulatory issues. Despite their best efforts, FIs often find themselves falling short of regulatory obligations owing to highly manual processes and silo-based solutions which hinder transparency, efficiency and availability of fast and meaningful data. Non-compliance means being slapped with hefty penalties not to mention consequent reputational damage. Compliance processes today need to be backed up like never before by automation, artificial intelligence and big data – to name a few crucial technologies – to keep up with increasing regulation and stricter enforcement.

After revolutionizing tax and accounting over the course of decades, technology finally looks poised to reshape the third major service of the traditional accounting practice: the audit. Machine learning, data analytics, ever-more-powerful and mobile computers, and new tools like blockchain will do more than just change the way auditors do their job -- increasingly, they'll change what that job is. To get a glimpse of what the audit (and auditor) of the future will look like, Accounting Today convened a virtual roundtable of experts in the field. Sharing their thoughts on the future of auditing here are: Mark Baer, managing partner of the audit services group at Top 10 Firm Crowe Horwath; Frank Casal, vice chair of audit at Big Four firm KPMG; Cindy Fornelli, the executive director of the Center for Audit Quality; Joel Shamon, the national audit leader at Top Five Firm RSM US; and Jimmy Thompson, an audit partner at Texas-based MaloneBailey. Which trends -- whether technological, regulatory, economic or otherwise -- should auditors be paying the most attention to over the next five years? Casal: Audit professionals' work is fundamentally about "trust."

Global leader in digitally-powered business process management and services Genpact has launched its Artificial Intelligence (AI) Reporting solution that harnesses the power of AI technologies to automate financial planning and analysis (FP&A) operations and drive more timely, insightful reporting. CFOs have fast, seamless access to both internal and external data sources like never before, driving more accurate forecasts for quicker, smarter business decisions. Based on multiple studies including a study done jointly by the Genpact Research Institute and HfS Research, CFOs across the board struggle with three challenges in providing actionable insights to their business leaders, including the ability to get relevant disparate data from internal and external sources; delay and the effort it takes for their teams to manage and do basic analysis on that data, reducing its relevance and leaving little scope for predictive analytics and business insight value adds; and lack of reporting mechanism that creates the user experience for business leaders to use that data to drive real time actions. Genpact's cloud-based AI Reporting solution uses advanced digital technologies to help enterprises reimagine the end-to-end financial reporting process. By integrating structured and unstructured data from internal and external sources, and automating reporting processes with predictive analytics, natural language processing and generation, and machine learning, the solution drives greater agility to adapt to new business requirements.