In this episode of The ModSquad, hosts Paul Barnhurst, Ian Schnoor, and Giles Male are joined by Tea Kuseva, Community Manager at the Financial Modeling Institute, for a detailed discussion on the state of AI tools in financial modeling. The group continues its hands-on testing of seven tools, including TabAI, Excel Agent, Shortcut, and TrufflePig, evaluating how these platforms perform on real-world financial modeling tasks

Tea Kuseva is the Community Manager at the Financial Modeling Institute (FMI), the only global accreditation body dedicated to financial modeling. With her deep involvement in the modeling community and her role supporting professionals worldwide, Tea Kuseva brings thoughtful questions and provides structure to the discussion, helping translate technical insights into practical takeaways for finance professionals.

Expect to Learn

1. How leading AI tools perform on real financial modeling tasks
2. Common issues like unbalanced sheets and flawed formulas
3. Key differences between Excel-based and standalone tools
4. Practical ways AI can assist with analysis and reporting
5. Why Excel and modeling expertise still matter in an AI-driven workflow

Here are a few quotes from the episode:

1. “Even five years from now, you’ll still need to understand every cell if you're handing in a model.” – Ian Schnoor
2. “Fast, consistent outputs are still better achieved by experienced humans than by today’s AI tools.” – Giles Male

AI tools show promise in assisting with financial modeling, but they are not yet reliable enough to replace human expertise. Strong Excel skills and sound judgment remain essential. Used wisely, AI can enhance productivity, but it should complement, not replace, technical understanding. The future of modeling is human-led, AI-assisted.

Follow Ian:

LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=ca

Follow Giles Male:

LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/

Follow Tea:

LinkedIn: https://www.linkedin.com/in/tkuseva/

In today’s episode:

[01:16] - Guest Intro

[06:07] - Tools Under the Microscope

[07:59] - The Testing Framework

[13:43] - Lessons from the Esports Challenges

[19:33] - Real Examples from the Tools

[25:54] - Practical Use Cases for AI Today

[33:56] - Variability in AI Outputs

[39:40] - Looking Ahead: The Next Five Years

[44:58] - Final Comments

[46:13] - Final Thoughts and Key Takeaways

In this episode of The Mod Squad, hosts Paul Barnhurst, Ian Schnoor, and Giles Male continue their hands-on testing of AI tools for financial modeling. This time, they put Subset, an AI-powered spreadsheet tool still in beta, through its paces. The hosts explore whether Subset can realistically handle core financial modeling tasks, including importing Excel files, building three-statement models, and applying basic accounting logic. Along the way, they uncover significant limitations, bugs, and logical errors that highlight the risks of relying on unsupported or immature tools.

Expect to Learn

• What Subset promises to do and how it performs in real-world testing
• The challenges of importing Excel files into non-Excel environments
• Why basic accounting logic still breaks many AI modeling tools
• The risks of using outdated or unsupported AI tools found online
• What it would actually take for professionals to move away from Excel

Here are a few quotes from the episode:

• “There’s no AI on the planet that should tell you gross profit is revenue plus costs.” – Ian Schnoor
• “It’s clever, but massively flawed and unreliable in lots of areas right now.” – Giles Male

Subset shows ambition in trying to act as a full AI spreadsheet, but the testing reveals serious issues, from incorrect formulas to flawed financial logic and unstable performance. While the tool demonstrates how far AI experimentation has come, it also serves as a cautionary example of why finance professionals must validate outputs and maintain strong technical foundations.

Follow Ian:

LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=ca

Follow Giles Male:

LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/

In today’s episode:

[02:40] – Welcome back to The Mod Squad

[05:04] – Introducing Subset and its promises

[08:38] – Importing Excel files into Subset

[11:27] – Errors, bugs, and beta limitations

[13:50] – Building a three-statement model from scratch

[19:25] – A Basic Revenue Reality Check

[22:37] – Why Excel Is Hard to Replace

[27:10] – Lessons learned from testing multiple tools

[30:01] – Why Structured Data Matters

In this episode of The Mod Squad, hosts Paul Barnhurst, Ian Schnoor, and Giles Male continue their exploration of tools for financial modeling. This time, they test Melder, a tool designed to streamline financial modeling tasks in Excel. The hosts evaluate how it handles various financial exercises, such as creating formulas and generating a deferred revenue schedule. While the tool shows promise, the hosts identify areas where Melder has room to improve, particularly with bugs and user experience quirks. This episode also highlights the challenges of using tools still in beta.

Expect to Learn

• A detailed review of Melder’s features for Excel-based financial modeling.
• How Melder compares to other tools previously tested by the team.
• Challenges faced when using Melder for tasks like building formulas and financial schedules.
• The pros and cons of using Melder, especially when it comes to its unique features and limitations.
• Insights into tools’ development process, especially when still in beta.

Here are a few quotes from the episode:

• "I appreciate the confidence behind the bold statements, but at the end of the day, tools need to make sure they’re doing the job correctly." – Ian Schnoor
• "When tools go wrong, it’s not just about fixing the error; it’s about understanding what went wrong so we can avoid future issues." – Giles Male

Melder offers some useful features for financial modeling, such as custom formulas and file handling, but it still faces challenges like data overwriting and slow performance. While it shows potential, especially in automating tasks, it needs further refinement to become a reliable tool for complex financial tasks. As it continues to evolve, we look forward to seeing how it improves and addresses these issues.

Follow Ian:

LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=ca

Follow Giles Male:

LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/

In today’s episode:

[00:31] - What is Melder?

[03:30] - Melder’s Website and Features

[08:40] - Testing Melder on Financial Modeling Tasks

[12:00] - Exploring Melder’s Formula Creation Capabilities

[14:30] - Overview of the LLM Model and Google Gemini Models

[19:43] - Testing the Trial Balance and Tool's Thought Process

[24:08] - Understanding Overengineered Formulas

[32:05] - Testing the PVM Use Case and Encountering Errors

[41:51] - Final Thoughts and Melder’s Future Potential

In this episode of Financial Modeler’s Corner, hosts Paul Barnhurst and Ian Schnoor continue their exploration of AI tools for financial modeling. This time, they test Trufflepig, a tool designed to help financial analysts automate spreadsheet tasks while still allowing them to focus on the insights. The hosts test Trufflepig on various financial modeling tasks, discussing its performance and how it compares to other tools they've used. They cover tasks such as building a DCF model for Nvidia, generating executive summaries, and creating a financial forecast. While Trufflepig performs well in some areas, there are still challenges that need to be addressed, particularly with certain financial concepts like working capital and net income.

Expect to Learn

• A review of Trufflepig, an AI-powered spreadsheet tool.
• How Trufflepig performs on real-world financial tasks.
• The benefits and limitations of AI tools in financial modeling.
• Insights into how Trufflepig compares with other financial modeling tools.

Here are a few quotes from the episode:

• “The biggest advantage of using Trufflepig is that it helps you with the repetitive tasks, so you can focus on higher-level analysis.” - Ian Schnoor
• “Trufflepig is an interesting tool, but as with any new software, there’s a learning curve. But if it delivers value, it’s worth it.” - Ian Schnoor

Trufflepig is a promising tool for financial professionals, particularly those looking to automate repetitive spreadsheet tasks. While it performs well on basic tasks like building DCF models and creating executive summaries, there are areas for improvement, especially around financial concepts like working capital and the handling of complex formulas.

Follow Ian:

LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=ca

Trufflepig: https://Trufflepig.ai/

In today’s episode:

[01:40] – Review of Previously Tested AI Tools

[05:15] – Trufflepig’s Positioning and Messaging

[12:00] – Trufflepig Attempts the eSports Modeling Case

[22:00] – Challenges with TEXTSPLIT and Modern Excel Functions

[30:50] – Executive Summary Generation

[40:01] – Data Sourcing and Web Pulling Behavior

[49:26] – Reasons for DCF and Market Price Differences

[59:45] – Exporting to Excel and Formatting Issues

[1:12:26] – Final Review and Closing Thoughts

In this episode of The ModSquad on Financial Modeler’s Corner, Giles Male and Ian Schnoor put Elkar to the test, a financial modeling tool that's been getting attention for its speed and slick design. From solving structured Excel challenges to building full forecast models, they push the tool to its limits. What follows is a revealing look at how Elkar performs when accuracy, logic, and professional modeling standards are on the line. Along the way, they uncover surprising strengths, critical flaws, and even moments of unexpected comedy. Whether you’re curious about automation or cautious about AI in finance, this episode offers plenty to think about.

Expect to Learn

• What Elkar gets right: speed, formatting, and a sleek interface
• Where it breaks down: logic errors, disconnected assumptions, and unreliable outputs
• How Elkar stacks up against other AI tools like TabAI and Agent
• Why using AI without understanding modeling fundamentals can be dangerous
• What it takes to turn a promising AI output into a reliable financial model

Here are a few quotes from the episode:

• "Right now, Elkar is like a junior analyst, you see potential, but you can't let them run unsupervised." - Giles Male
• "AI tools like this might build something that looks like a model, but without logic, it’s a house of cards." - Ian Schnoor

In this episode, Elkar proves to be a fast and visually polished AI tool with clear potential, especially in formatting and task execution speed. However, when it comes to financial logic, assumption structuring, and balance sheet integrity, it consistently misses the mark. The tool even resorts to shortcuts like hardcoding values and plugging imbalances.

Follow Giles Male:

LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/

Follow Ian:

LinkedIn - https://www.linkedin.com/in/ianschnoor/?originalSubdomain=ca

Elkar: https://elkar.co

In today’s episode:

[06:48] - Exploring Elkar: Website, Pricing, and Features

[10:34] - Elkar Takes on the Esports Excel Challenge

[20:14] - Elkar Gets Caught Cheating

[24:18] - Elkar Struggles with Complex Logic

[35:45] - Cash Flow Logic & Balance Sheet Errors

[46:38] - From Hardcoding to Dynamic Assumptions

[53:45] - Balance Sheet Plugging and Logical Failure

[57:34] - Reviewing Elkar’s Working Capital Assumptions

[1:04:20] - Wrap up & Final Thoughts

In this episode of The ModSquad on Financial Modeler’s Corner, Paul Barnhurst and Giles Male put Shortcut under the AI microscope, testing one of the most hyped AI tools in the financial modeling world. With claims like “the most accurate Excel agent in the world” and the ability to outperform human champions in modeling tasks, Shortcut has made a big splash, but does it live up to its own bold promises? Paul and Giles run it through a rigorous series of real-world modeling challenges, from esports cases and financial forecasts to dashboard analysis and deferred revenue schedules. What they find is a tool with clear potential, and some serious red flags.

Expect to Learn

• Where Shortcut impresses with formatting, speed, and usability
• Where it fails, especially with modeling logic
• How Shortcut compares to Excel Agent and TabAI across key modeling tasks
• Why reversing formatting in a model is a huge red flag
• What to consider when investing in premium AI tools for modeling

Here are a few quotes from the episode:

• “Shortcut has potential, but right now it’s flash over fundamentals.” - Giles Male
• “Could you imagine an analyst reversing formatting to make a number look negative? They’d be out the door.” - Giles Male
• 
  

Despite the hype, Shortcut proved to be a solid tool with promise. It delivered impressive formatting and UI, yet had some serious issues like incorrect logic, hardcoded values, and non-balancing models, which held it back. A promising AI assistant, just not a replacement for real modeling expertise.

Follow Giles Male:

LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/

In today’s episode:

[01:15] - Intro & Where the AI Modeling Journey Stands

[05:11] - Shortcut: First Impressions & Bold Claims

[14:28] - Viral Demo Video Breakdown

[23:12] - Esports Challenge: Basic Excel Tasks

[31:19] - Intermediate Case: Modeling Accuracy

[36:10] - Building a 3-Statement Forecast

[44:24] - Red Flags: Formatting & Balance Sheet Errors

[50:27] - Deferred Revenue Test

[56:32] - Trial Balance Dashboard: Visuals vs. Substance

[1:07:22] - Final Thoughts & Shortcut's Ranking

In this episode of The Mod Squad on Financial Modeler’s Corner, Paul Barnhurst, Ian Schnoor, and Giles Male take a close look at TabAI, a tool designed to simplify and speed up Excel tasks using automation and intelligent suggestions. With more tools dropping out of the market and Excel’s own Agent feature gaining ground, the question is simple: Does TabAI offer something worth switching to? From cleaning data and building dashboards to attempting a full five-year forecast, the team puts TabAI through a series of real-world modeling challenges to see what it gets right and where it still falls short.

Expect to Learn

• Where TabAI shines in helping analysts and where it needs improvement.
• How does it compare to Excel Agent in terms of speed, usability, and accuracy?
• Why finance pros still need to understand what’s going on under the hood.
• What to watch for when relying on tools that promise “done-for-you” modeling.

Here are a few quotes from the episode:

• “Agent was faster, but TabAI handled more advanced stuff better.” - Ian Schnoor
• “AI is great at building things fast, but one small mistake can make the whole model unusable.” - Giles Male

TabAI turned out to be one of the more impressive tools we’ve tested so far, especially when it comes to everyday Excel tasks and building dashboards. It’s not perfect, especially with full-scale models, but it’s definitely a step in the right direction. For now, it’s a great helper, but you’ll still need your own modeling skills to get the job done right.

Follow Ian Schnoor:

LinkedIn - https://www.linkedin.com/in/ianschnoor/

Follow Giles Male:

LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/

In today’s episode:

[02:28] - TabAI Leaves Retail

[05:17] - Competing with Excel Agent

[06:50] - TabAI Feature Overview

[10:30] - The “Iron Man Suit” Claim

[14:28] - eSports Case Test

[23:12] - Dancing Fur Coat Model

[29:14] - Trial Balance Dashboard

[33:56] - Deferred Revenue Test

[38:36] - Full Forecast Model Build

[51:10] - Final Thoughts

In Episode 5 of The ModSquad on Financial Modeler’s Corner, Paul Barnhurst, Ian Schnoor, and Giles Male take a hard look at the changing landscape of financial modeling in the wake of Microsoft’s release of Excel Agent. Since launching at the end of September to coincide with Excel’s 40th birthday, Excel Agent has quickly changed the competitive dynamics for AI-powered modeling tools. The team explores the implications: how Excel Agent’s capabilities compare to other tools, why third-party platforms are shutting down, and what all this means for the future of work in modeling-heavy industries like investment banking.

Expect to Learn

• Why Excel Agent is pushing competing modeling tools like Rosie AI out of the market.
• What makes Excel Agent a “magnifier” of both modeling skill and error.
• How fast AI is evolving inside Excel and what that means for modelers today.
• Why AI won’t reduce hours in finance, despite speeding up modeling work.
• What OpenAI’s Project Mercury reveals about the next phase of automation in investment banking.

Here are a few quotes from the episode:

• “You can't hit a prompt, go get a coffee, and expect a working model.” – Giles Male
• “If you don’t understand what the AI just built, you’re in trouble.” – Ian Schnoor

This episode makes it clear: AI is not a replacement for skill; it’s a multiplier. Excel Agent may be setting the new standard, but success still comes down to human understanding, judgment, and accountability. As the modeling world evolves rapidly, professionals who stay informed and upskill will thrive. The Mod Squad isn’t slowing down either; more tool reviews and sharp conversations are coming.

Follow Ian Schnoor:

LinkedIn - https://www.linkedin.com/in/ianschnoor/

Follow Giles Male:

LinkedIn - https://www.linkedin.com/in/giles-male-30643b15/

In today’s episode:

[05:29] - AI Tools Recap

[07:26] - AI Hype and Hidden Risks

[10:23] - AI as a Skill Magnifier

[13:48] - Microsoft’s Impact on AI Startups

[16:15] - Rapid Evolution of Excel AI

[21:29] - OpenAI’s Role in Financial Modeling

[29:17] - Understanding Assumptions and Calculations

[31:53] - Final Thought