financial modeling
financial modeling on Beyond Market Intelligence: a running collection of 199 stories we have gathered and hand-picked because they are worth your time. Every post here touches on financial modeling in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around financial modeling, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Testing autonomous agents (Or: how I learned to stop worrying and embrace chaos)
In the rapidly evolving landscape of autonomous agents, the stakes have never been higher. After 18 months of building production AI systems, we’ve learned that the challenge isn’t just about creating agents that respond accurately; it’s about ensuring they operate reliably and safely. A misstep—like an agent mistakenly approving a major vendor contract—can have serious consequences. This exploration delves into the complexities of engineering reliable autonomous agents, emphasizing the importance of guardrails, layered reliability, and the balance between innovation and caution in this transformative field.
Ai2 releases MolmoWeb, an open-weight visual web agent with 30K human task trajectories and a full training stack
Ai2 proudly announces the launch of MolmoWeb, an innovative open-weight visual web agent designed to empower engineers in building browser agents with unparalleled transparency. Featuring 30,000 human task trajectories, MolmoWeb offers a complete training stack that includes the largest publicly released dataset of human web-task execution to date. Unlike existing solutions, which either rely on closed APIs or lack comprehensive training models, MolmoWeb combines extensive human and synthetic data to deliver a powerful, browser-agnostic tool that transforms how users interact with web environments.
The three disciplines separating AI agent demos from real-world deployment
Deploying AI agents effectively in real-world scenarios presents significant challenges that go beyond successful demonstrations. Issues such as fragmented data, unclear workflows, and high escalation rates hinder enterprises' progress in adopting these technologies. As Sanchit Vir Gogia highlights, the true test lies in navigating the complexities of organizational environments.
[D] risks of using XGB in credit risk models
In the realm of credit risk modeling, utilizing XGBoost within ensemble models presents notable risks that warrant careful examination. While the aggregation of outputs from weak predictors may seem acceptable, it can obscure underlying issues that compromise model integrity and reliability. Insignificant variables can lead to misleading conclusions, undermining the model's effectiveness in predicting defaults.
[D] Risk of using XGB models
In the evolving landscape of data science, particularly within risk auditing, understanding the limitations of complex models like XGBoost is crucial. While ensemble models aggregate weak predictors, relying on statistically insignificant variables can undermine model reliability. Your observations about the Information Value of certain features are valid and warrant further scrutiny. It's important to challenge the notion that aggregation alone mitigates risk.
Switching out of Data Strategy to Technical work
Transitioning from a data strategy role to a more technical position can be challenging, especially in a large consulting environment like the Big Four. While your current focus on data governance and architecture may not align with your interests, it's possible to pivot toward technical work in the next 6-12 months. To position yourself effectively, consider seeking opportunities to collaborate on technical projects, pursuing relevant training in AI and model development, and networking with professionals in roles you aspire to.
15 Power BI Project Ideas to Build Your Portfolio in 2026
Building a robust Power BI portfolio is essential for aspiring business or data analysts in 2026. Employers seek candidates who can transform messy data into clean models and create impactful dashboards that drive informed decision-making. This article presents 15 compelling Power BI project ideas that will not only showcase your technical prowess but also demonstrate your ability to derive actionable insights from data. These projects serve as tangible evidence of your skills, empowering you to stand out in a competitive job market.
FMI - Advanced Financial Modeller
Have you taken the FMI - Advanced Financial Modeller course? This discussion invites you to share your experiences and insights on how the course may have impacted your career. Whether you found new skills, enhanced your understanding of financial modeling, or gained a competitive edge in the job market, your feedback could be invaluable to others considering this opportunity. Join the conversation and explore the potential benefits this course might offer for professional growth in the finance sector. Your insights matter!
Gallagher& Mohan excel test
Congratulations on advancing to the second round of the Gallagher & Mohan financial analyst interview! As you prepare for your Excel test, it's understandable to feel anxious, especially with the DCF model and specific real estate focus. To excel in this test, familiarize yourself with key formulas such as NPV, IRR, and various financial functions that apply to real estate valuation. Engaging with resources or forums where others have shared their experiences can also provide valuable insights.
30 Data Science Projects for Beginners to Advanced (with Source Code)
Building a strong data science portfolio is essential for showcasing your skills and standing out to potential employers. "30 Data Science Projects for Beginners to Advanced" provides an invaluable resource, offering a curated list of projects that span various skill levels. Each project includes source code, real datasets, and step-by-step instructions to guide you through completion.
This Week's /r/Excel Recap for the week of March 07 - March 13, 2026
Welcome to this week's recap of /r/Excel for March 07 - March 13, 2026. This week featured engaging discussions and insightful problem-solving, with the top posts highlighting topics like VLOOKUP with curly brackets and the complexity of user-created spreadsheets. Join the conversation on unsolved challenges, including refining Excel to PowerPoint workflows and sharing Office Scripts. Dive into the community's insights and expertise to enhance your spreadsheet skills and discover innovative solutions for your Excel journey. Explore the top comments for added inspiration!
14 Machine Learning Projects for Beginners to Advanced (2026)
Embarking on a journey into machine learning can be both exciting and overwhelming. This article presents 14 carefully curated projects, organized by difficulty, that cater to everyone from beginners to advanced practitioners. Each project includes free datasets, starter code, time estimates, and valuable insights on how to enhance your work into a standout portfolio piece. By exploring these projects, you'll not only develop transferable skills but also create tangible results to impress hiring managers, empowering your career in this dynamic field.
Anyone using Excel professionally on Mac via Parallels? (VBA, Power BI, large financial models)
Hello everyone, I’m a financial controller navigating complex Excel models and large datasets on a MacBook Air M4 with 24GB RAM. However, I’ve encountered significant limitations with Excel for Mac, particularly with features like VBA, pivot tables, and Power BI. To enhance my workflow, I’m considering running Windows through Parallels. Before making this investment, I’d love to hear from others who use Mac and Parallels for professional Excel work. How does Excel perform with large financial models?
Who in your org actually owns the macro assumptions in financial models?
In many organizations, the ownership of macro assumptions in financial models varies widely, leading to confusion and inconsistency. While some firms designate the CFO as the final decision-maker, others rely on treasury analysts or even the individuals who constructed the Excel models. This raises important questions: Who ultimately determines the numbers tied to macro events, such as rate or commodity price assumptions? Additionally, is there a documentation process explaining these choices, or are they often based on intuition?
Built a multi-scenario financial stress test model in Excel — feedback welcome
I'm excited to share my latest creation: a comprehensive multi-scenario financial stress test model built in Excel. This model features a robust baseline projection engine, stress testing capabilities, risk scoring, and conditional verdict logic, all organized across multiple sheets. With locked input sections to ensure data integrity, this is the most structured file I've developed to date. If you're passionate about complex Excel modeling or need assistance with financial analysis, I’d be happy to provide access to this model for a small fee.
Does anyone in this sub know of a good online excel course to learn financial analysis? ?
As you prepare to embark on your new role as a financial analyst at a university, it's essential to equip yourself with the right skills to ensure a smooth transition. Mastering tools like VLOOKUP and basic financial modeling will not only enhance your confidence but also empower your data analysis capabilities. In this community, you can discover valuable recommendations for online Excel courses tailored to financial analysis. Your eagerness to learn is commendable, and I encourage you to explore the insights shared by fellow members.
Other than setting up CAGR, what’s the LAMBDA function really good for?
If you primarily use Excel for data visualizations and financial modeling, you might be underestimating the versatility of the LAMBDA function. Beyond calculating CAGR, LAMBDA allows you to streamline complex calculations and reduce clutter by encapsulating repetitive functions into a single formula. This means you can create reusable, dynamic functions tailored to your enterprise needs. Discover how LAMBDA can transform your workflow, enhance readability, and empower your dashboards with sophisticated calculations that previously required multiple helper functions.
Project Tutorial: Predicting Employee Productivity with Decision Trees and Random Forests
Are you grappling with the challenge of predicting employee productivity in your factory? In this project tutorial, we’ll guide you through using decision trees and random forests to create a machine learning model that forecasts whether a workday will be productive. By analyzing team-level productivity data, you’ll uncover the key conditions that drive performance on the factory floor. Join us as we transform complex data into actionable insights that empower you to optimize productivity and enhance team outcomes. Let’s dive in!
A Designer’s Guide To Eco-Friendly Interfaces
In a world where every high-resolution image and complex animation comes with a hidden cost, the need for sustainable UX has never been more pressing. Designers are now challenged to rethink their approach, prioritizing responsibility over excess. As we look ahead to 2026, the hallmark of sophisticated design will be its ability to minimize impact while maximizing user experience. This guide will explore eco-friendly interface strategies that not only enhance performance but also align with the values of a more sustainable future.
Built an open-source Excel structural risk analyzer - looking for feedback from model builders
Are hidden structural issues in your Excel models causing silent failures? Meet XLChek, an open-source structural risk analyzer that scans .xlsx files offline, pinpointing risks like circular references and orphan calculations. Designed for advanced Excel users and model builders, this read-only tool generates clear HTML reports, ensuring your spreadsheets maintain structural integrity without the need for data uploads. I invite your critical feedback on its utility and potential risks it may overlook. Join me in refining this tool for better financial modeling and operational accuracy.
How do you handle audit trails in Excel financial models?
Managing audit trails in Excel financial models can be daunting, especially when one small error can lead to significant repercussions. How do you ensure accuracy in your spreadsheets? Whether you rely on manual checks, versioning, internal processes, or specialized tools, understanding effective tracking methods is crucial. This exploration not only highlights best practices but also uncovers real workflows that can transform how you handle sensitive financial data. Join us as we navigate these strategies to enhance accountability and precision in your financial reporting.
Financial Model Help
Navigating financial models can be challenging, especially when legacy spreadsheets limit your analysis. If you're seeking help with your financial model, you're not alone. Many professionals find themselves overwhelmed by complex calculations and outdated tools. This guide will connect you with effective strategies and AI-driven techniques to enhance your financial modeling skills. Continue reading to discover practical solutions that streamline your workflow and empower you to make data-driven decisions with confidence. Embrace the transformation that modern technology can bring to your financial analysis.
I have a financial model that occasionally has a large revision, where entire rows may be deleted. Natural this breaks any formulas referencing those rows. What are the easiest ways to prevent this?
Managing financial models can quickly become overwhelming, especially when entire rows are deleted, breaking essential formulas. This common challenge not only disrupts your calculations but also leads to tedious manual corrections across hundreds of rows. Fortunately, there are efficient strategies to safeguard your formulas against these disruptions. In this guide, we’ll explore practical solutions that simplify your cash flow model maintenance and minimize the risk of errors. Discover how to streamline your workflow and regain control over your financial analysis with our actionable insights.
Designing For Agentic AI: Practical UX Patterns For Control, Consent, And Accountability
In a world where technology can either empower or overwhelm, designing for agentic AI is crucial. Users crave autonomy, but they also need control, consent, and accountability in their interactions with intelligent systems. This guide presents practical UX patterns and operational frameworks that prioritize user trust and transparency. By integrating these design principles, organizations can build powerful systems that not only deliver results but also enhance user confidence.