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The case for an AI financial modeling agent: Cursor for Excel

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The software development world has been transformed by AI-powered coding assistants such as Cursor and Claude Code, which have changed how engineers write and debug code. 

These tools can generate contextually relevant code based on user prompts, accelerating the software development process and enabling engineers to be multiple times more productive. Is it time for a similar AI-powered financial modeling assistant in Excel?

Financial modeling, much like coding, involves numerous interconnected spreadsheet tabs, complex logical structures built through Excel formulas and links, and specialized domain knowledge. An ideal solution would be an AI modeling agent tool where it can understand the context of spreadsheet tabs and generate models while financial analyst prompts, much like a Cursor for Excel. However, the current general-purpose tools, like Excel Copilot, fall short when asked to generate financial models and we might need more specialized solutions.

The demand across financial disciplines

As a valuation analyst and former investment banking professional, I frequently use Excel to build valuation models such as Discounted Cash Flow and Option Pricing Models. As an Excel power user, I would eagerly subscribe to such an AI modeling tool. The potential application of an AI financial modeling agent spans across accounting firms, investment banks, and financial and risk consulting firms, etc. With a short prompt, finance professionals can generate detailed models and collaborate with AI financial modeling agents throughout the workflow.

How to build the AI Financial Modeling Tool

I haven’t been able to discover any similar tool currently available on the market. The most straightforward approach would be to develop a web application as an Excel add-in, similar to how Claude Code functions as an extension for VS Code. Alternatively, developers can choose to build an AI-native spreadsheet solution much like Cursor, where both the traditional spreadsheet and AI solution are integrated into a unified platform. 

The difference between coding files (e.g., .py) and Excel files (.xlsx) might be that there’s an extra visualization layer on top of the code. This means an AI modeling agent might need to generate and execute code live based on human prompts, effectively simulating the modeling behavior of humans in real-time. Fortunately, Excel provided some scripting language tools such as Script Lab where it can interact with Excel via APIs. Below is an experiment where I tried to create a mini discounted cash flow model through prompting a Generative AI model and Microsoft Script Lab.

DCF model with Claude and Script Lab

The general setup involved prompting Claude to create a mini discounted cash flow model in Excel, then transferring the generated TypeScript and HTML code into the Script Lab Excel add-in for execution. The process looks like this:

ai-flowchart.png

First, I prompted Claude to create a DCF model:

claude-dcf-model.png

Then, Claude generated TypeScript and HTML code containing the DCF model logic and structure.

claude-typescript.png

In Script Lab, I pasted over and executed the AI generated code, which automatically generated a draft DCF calculation in the spreadsheet.

script-lab-claude.png

Admittedly, debugging the Script Lab code with Claude’s assistance took longer than building this simple model from scratch. However, as AI systems learn and continuously self-train, I anticipate it would take way less effort to create a larger and complex model across multiple spreadsheet tabs. Imagine having a junior analyst who exists on a cloud server and can self-train financial modeling 24/7? The productivity gains would be substantial.

Financial modeling is overdue for its own specialized AI assistant, much like how engineers now rely on tools like Cursor. A dedicated AI modeling agent could dramatically streamline the workflow for analysts, auditors and bankers alike. The opportunity is clear: an AI-native financial modeling assistant will transform how finance professionals work, and I’m excited for the day when it becomes an indispensable part of every finance professional’s toolkit.

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Accounting

Continuous Auditing Transforms Corporate ERPs

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continuous auditing transforms corporate erps

As corporate accounting departments cross the threshold into late July 2026, the adoption of continuous, automated auditing systems has reached a definitive turning point. Driven by advances in artificial intelligence and deep integration with modern Enterprise Resource Planning (ERP) platforms, leading finance organizations are moving away from traditional, periodic post-hoc audits in favor of real-time, 100% transactional verification. This technological transition is redefining internal control environments, reducing compliance costs, and eliminating the structural delays inherent in legacy quarterly closing processes.

Unlike traditional auditing frameworks that rely on statistical sampling—a process that inevitably leaves operational blind spots—continuous auditing software monitors operational data feeds continuously. Every purchase order, electronic invoice, payroll disbursement, and cross-border wire transfer is automatically cross-referenced against established corporate governance parameters, regulatory tax schedules, and anti-fraud algorithms in real time. Anomalies or unauthorized ledger entries are flagged instantly, allowing internal audit teams to investigate and remediate compliance gaps immediately rather than months after the close of a financial period.

The implications for executive financial management are far-reaching. By embedding continuous verification directly into daily transaction workflows, chief financial officers gain uninterrupted visibility into the organization’s true financial standing. Real-time balance sheet auditing eliminates the severe operational bottlenecks associated with month-end and quarter-end financial reconciliations, freeing accounting professionals to focus on strategic financial modeling, tax planning, and capital allocation rather than manual data entry and spreadsheet consolidation.

However, implementing continuous auditing requires accounting leadership to invest heavily in data governance and technical upskilling. Internal audit teams must evolve from manual ledger reviewers into system architects capable of auditing complex algorithms and validating automated data pipelines. Accounting firms and corporate controllers that master continuous auditing will establish a resilient compliance framework capable of meeting stringent international regulatory standards with total transparency.

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Accounting

U.S. Imposes New 50% Tariffs on Canadian Imports Under Rare Legal Provision

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U.S. Imposes New 50% Tariffs on Canadian Imports Under Rare Legal Provision

WASHINGTON — In a major escalation of cross-border trade friction, U.S. President Donald Trump has signed executive orders imposing new 50% tariffs on a wide selection of Canadian exports, citing discriminatory practices by Ottawa targeting American auto, dairy, and beverage industries.

The new duties, announced Monday, will take effect in 30 days. They target a broad spectrum of consumer and industrial goods—ranging from wine, liquor, and milk products to commercial cement, furniture, clothing, and hockey equipment.

Untested Legal Mechanism

To enact the sweeping measures, the administration invoked Section 338 of the Tariff Act of 1930—a rarely used legal provision allowing the executive branch to levy additional tariffs of up to 50% on foreign nations deemed to discriminate against U.S. commerce.

White House officials noted that Section 338 addresses trade discrimination rather than national security or economic emergencies. The move comes months after prior global emergency tariffs faced legal challenges in domestic courts, signaling Washington’s pivot toward alternate statutory authorities to maintain import duties.

Senior administration officials briefed reporters that the measure directly responds to Canadian provincial bans on U.S. alcohol, restrictions on American vehicle exports, and import quota disparities affecting U.S. dairy and cheese producers relative to third-party trading partners.

“While the administration continues to secure reciprocal trade agreements globally, Canada retaliated against efforts to protect domestic industry,” U.S. Trade Representative Jamieson Greer stated.

USMCA Impact and Carve-Outs

Significantly, the newly ordered 50% duties will apply to designated items even if they otherwise comply with the United States-Mexico-Canada Agreement (USMCA).

However, the administration confirmed key targeted exemptions:

  • Energy products (including oil and natural gas)
  • Potash and critical minerals
  • Fish and seafood
  • Goods already governed by sector-specific duties (such as existing steel and aluminum tariffs)

Administration representatives emphasized that the tariffs do not stem from recent disputes concerning drifting Canadian wildfire smoke, noting that policy options regarding environmental spillover remain under separate review.

Canadian Response and Market Reaction

Following the White House announcement, the Canadian dollar experienced a sharp decline against the U.S. dollar, falling approximately 0.4% during evening trading.

Canadian Prime Minister Mark Carney issued a statement emphasizing that Canada’s earlier counter-duties had merely matched previous U.S. trade actions. “Canada stands ready to engage intensively to address outstanding issues with the U.S. to the mutual benefit of our citizens,” Carney stated, pointing to detailed proposals Ottawa submitted to modernize the USMCA framework.

Ontario Premier Doug Ford took a firmer stance, urging a “dollar-for-dollar” reciprocal response if the measures go into effect on August 19.

With a 30-day implementation window before the duties officially lock in, industry associations and trade groups on both sides of the border are calling for urgent bilateral negotiations to avert further supply chain disruption across North America.

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Accounting

Automated Continuous Auditing: Transforming Compliance and Real-Time Financial Oversight

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Transforming Compliance and Real-Time Financial Oversight

The traditional accounting paradigm—defined by periodic monthly closures and post-hoc annual audits—is rapidly giving way to continuous, automated financial oversight. As of July 2026, forward-thinking accounting practices and multinational corporate finance departments are leveraging continuous auditing systems powered by advanced machine learning models. These systems monitor operational transactions in real time, shifting audit methodologies from sample-based post-analysis to absolute, 100% transaction-level verification.

The operational advantages of continuous auditing are transformative. Standard auditing procedures historically relied on statistical sampling, which, despite rigorous methodology, inherently left gaps where anomalies or fraudulent transactions could go undetected for months. Modern continuous auditing platforms integrate directly with enterprise resource planning (ERP) databases, instantly cross-referencing purchase orders, invoices, bank feeds, and tax records. Any deviation from established control parameters or unusual transaction behavior triggers immediate flags for internal audit teams, dramatically reducing detection lag from quarters to seconds.

Beyond fraud prevention, continuous auditing fundamentally alters internal reporting and decision-making. Executive leadership no longer has to wait weeks after the close of a quarter to evaluate precise financial standing; real-time verified ledger data provides an uninterrupted view of operating margins, tax liabilities, and cash flow dynamics. This real-time visibility enables corporate controllers to adjust capital allocation strategies dynamically, mitigating liquidity constraints and capitalizing on emerging commercial opportunities far more efficiently than competitors bound to legacy reporting cycles.

However, implementing continuous auditing requires accounting professionals to acquire new analytical capabilities. The role of the auditor is evolving from manual data reconciliation toward system validation, algorithmic model governance, and strategic risk interpretation. Accounting firms and corporate finance departments must invest in continuous technical education, ensuring that audit staff possess the data engineering skills necessary to design, maintain, and evaluate complex automated compliance systems.

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