Financial Modeling for Analysts to Build Clear, Structured Models for Better Decisions with Shreya

Why It Matters

Adopting structured modeling standards and disciplined practices reduces errors, builds stakeholder confidence, and enables faster, more reliable investment decisions in complex, high‑impact projects.

Key Takeaways

• Structured standards like FAST boost model clarity and stakeholder trust.
• Starting from scratch avoids inherited errors in legacy financial models.
• Project finance models excel in scenario analysis for renewable energy assets.
• Consistent commercial, technical, and financial assumptions are critical in portfolio roll‑ups.
• AI can assist but cannot replace disciplined model building practices.

Summary

The Financial Modelers Corner podcast featured Sharia Bot, a seasoned financial modeler at Cross‑Boundary Group, discussing how disciplined modeling practices drive better investment decisions. She highlighted the growing role of AI, noting it can augment but not replace the need for clear, structured models. Key insights included the value of standards such as FAST, which enforce consistent sheet architecture, input shading, and formula discipline. Sharia emphasized that models are never truly finished and that rebuilding from scratch often prevents inherited errors seen in poorly documented legacy workbooks. She recounted a "horror" model riddled with tangled formulas and missing input labels, contrasted it with a successful 11‑country project‑finance template that balanced flexibility and readability, and explained how portfolio roll‑ups for solar assets require tight alignment of commercial, technical, and financing assumptions. For practitioners, adopting robust standards, leveraging AI for routine checks, and treating models as living decision‑tools can improve confidence, accelerate deal execution, and ultimately enhance returns for investors in frontier and renewable‑energy markets.

Original Description

In this episode of Financial Modeler’s Corner, Paul Barnhurst speaks with Shreya Bhatt about her journey in financial modeling and her experience working across infrastructure and project finance. Shreya shares real challenges she has faced, including rebuilding poorly structured models, and explains what makes a model reliable and easy to use. The conversation also covers modeling standards, practical use cases, and how financial models support real-world decisions.

Shreya Bhatt is a Manager at CrossBoundary Group with over eight years of experience in financial modeling and infrastructure finance. She works on project finance, portfolio modeling, and investment analysis, supporting decision-making across sectors like renewable energy and infrastructure.

Expect to Learn

What makes a financial model reliable and easy to use

Common mistakes that make models difficult to work with

How project finance models connect to real-world decisions

When to use different modeling standards like FAST and SMART

How AI fits into financial modeling today

Here are a few quotes from the episode:

A model can either be the most powerful tool or the most frustrating one.” – Shreya Bhatt

“AI can help us, but judgement and interpretation will always remain important.” – Shreya Bhatt

Shreya explains that strong financial models are built with clarity, structure, and consistency. She highlights that while tools and technologies like AI can speed up parts of the process, the real value comes from understanding the business, making sound assumptions, and delivering insights that support decision-making.

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In today’s episode:

[00:00] – Trailer

[02:48] – Worst Modeling Experience

[05:44] – Role at CrossBoundary

[07:35] – Favourite Model Types

[11:14] – Portfolio & Solar Models

[14:06] – FAST Certification Journey

[16:15] – FAST vs SMART Standards

[19:56] – Modeling Best Practices

[23:36] – AI in Financial Modeling

[28:52] – Career Lessons

[33:04] – Rapid Fire Insights

[36:56] – Final Advice