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