Financial Modeling Fundamentals

Understanding common FP&A Financial Models with the help of an example. Let's understand five common financial models using a simple example: a coffee cart business. 1. Budgeting Model You estimate that you'll sell 3,000 cups this month and make a profit of ₹30,000. This is your plan before the month begins. Question it answers: What do I expect to happen? 2. Forecasting Model Halfway through the month, you realize sales are lower than expected. You revise your estimates and project a lower profit. Unlike budgets, forecasts evolve with reality. Question it answers: Given current trends, what is likely to happen? 3. Sensitivity Analysis What happens if you increase the selling price from ₹30 to ₹35? Or if milk prices rise? Here, you change one variable at a time to understand its impact. Question it answers: Which factor affects my results the most? 4. Scenario Analysis Now consider three situations: Best Case: Higher footfall and strong sales Base Case: Business performs as expected Worst Case: Heavy rains reduce customer traffic and costs increase This helps prepare for multiple possible futures. Question it answers: What if the overall business environment changes? 5. Break-Even Analysis You calculate the minimum number of cups you must sell to cover all costs. Everything beyond that point contributes to profit. Question it answers: What's the minimum I need to achieve to avoid losses? The models may differ, but the objective remains the same: Making better decisions with numbers. Which financial model do you use most often in your work?

Plenty of teams build models for investors. Fewer build ones they can actually use. This guide from the Financial Modeling World Cup shows how solid modelling can clarify your business, expose weak assumptions, and flag risks early, before they get expensive. Here are my key takeaways: 🔶 A clear model is built around logic: inputs grouped, flows easy to follow, and every output tied back to the assumptions that drive it. 🔶 It’s not enough to connect the income statement, balance sheet, and cash flow. You need to model how things like payables, capex, and loan repayments affect your ability to operate and grow. 🔶 The income statement tells you how it looks, but the cash flow statement tells you if it works. 🔶 A lot of founders underestimate how much modelling forces better thinking. When you’re forced to define every cost, timeline, and return, it sharpens the whole plan. 🔶 Excel tricks won’t save a bad model, but strong logic, clean formatting, and realistic assumptions will. 🔶 Valuation models should be built to break. If you’re not testing downside scenarios, you’re not actually doing risk planning. 🔶 Models are decision tools. They should answer simple questions: What happens if revenue drops? How much cash do we burn? When do we run out? A solid model won’t predict the future, but it can help you prepare for it. #financialmodelling #FPandA #Excel #valuation #couchonomics #payments #fintech #embeddedfinance #digitalassets #futureofmoney #futureoffinance NORBr Onalytica Favikon ⁠Global Finance & Technology Network Thinkers360 - ⁠- - - - - - - - - - - - - - - - - - - - - - - - - - - 👍 Hit like ♻️ Share it with your network 📢 Drop a comment 🎙️ Check out my podcast Couchonomics with Arjun on YouTube 📖 Get my weekly newsletter on LinkedIn: Couchonomics Crunch 🕺💃 In the MENA region? Join our Fintech Tuesdays community. 🤝 Let's connect! - ⁠- - - - - - - - - - - - - - - - - - - - - - - - - - -

Spotting Logical Inconsistencies in Financial Models - Post 2 Growth vs. Return on Capital – The Scaling Illusion 📈 15% revenue growth... with no reinvestment? Not so fast. A common mistake in financial models is projecting aggressive growth without an increase in reinvestment (CapEx, R&D, or acquisitions). Where is that growth coming from? If the company isn’t reinvesting, ROC (Return on Capital) would have to skyrocket unrealistically to sustain that growth. Here’s the issue: 📌 Growth = Reinvestment Rate × ROC. If your model assumes 15% growth but only a 5% reinvestment rate, you’re implying the company suddenly generates 3x its usual return on capital—unlikely in the real world. 🔍 The Fix: ✅ Reality-check the reinvestment rate: Calculate: Reinvestment Rate = (CapEx – Depreciation + Δ Working Capital) / NOPAT ✅ If growth exceeds what ROC can sustain, adjust: Option 1: Increase reinvestment (CapEx, R&D) in the model to make growth realistic. Option 2: If reinvestment is limited, lower the growth assumption. ✅ Run sensitivity tests—what happens if ROC doesn’t improve? If the company can’t sustain growth at those levels, your projections need adjusting. Growth isn’t magic—it must be fueled by reinvestment. Always test the implied ROC and ensure your model doesn’t overpromise what capital can deliver.

I’ve been teaching financial modeling since 2008. In that time, I’ve worked with thousands of finance professionals. Here are three keys lessons learned over the years: (1) Modeling is a thinking exercise, not a spreadsheet exercise. Where I went wrong early in my career was thinking that I was a great modeler because I knew how to build models that wowed others. But what I got wrong was not realizing that modeling is not about mesmerizing audiences. It's rarely about the tools. It's about understanding the business first and how the dynamics within the company impact the future. Then it's about being able to depict the business accurately and plan effectively within the tool. (2) Structure and simplicity win almost every time. Simplicity doesn't mean a lack of sophistication. And it doesn't mean using basic formulas and functions. It means that others can follow, use, and trust the model to make their decisions. The best models are robust without being complex. Those two terms don't have to be mutually exclusive. (3) Technical skills are just the starting point. Learning accounting equations and how the financial statement connect is important. But those are table stakes and are to be expected. Anyone can learn them from textbooks. But textbook learning doesn't support a career. It might just help land a first job in finance or accounting. The real value in financial modeling comes from interpretation and anticipation. It's knowing what questions to ask and what inquiries are on others' minds before they make them. It's about knowing implications across a massive range of areas: operations, capital, people, morale, reputation, and more. The technical skills are a given. If I could give one piece of advice to anyone learning financial modeling today, it would be to focus as much on understanding the business problems as you do on formulas. It would be to focus as much on critical thinking as you do on accounting equations. Ask what others value from you and what you can do to increase their confidence and make their lives easier. And for experience modelers: teach others. Explaining your logic and rationale for what you do forces you to refine your own thinking. And it passes on know-how that most young people aren't learning in school.

Most FP&A pros know ROI and Payback. That's not enough. If you're only comfortable with a couple of investment models, you're limiting your ability to guide strategic decisions. After years of evaluating projects at P&G, Unilever, and Squarespace, I've learned that finance professionals need to master 5 distinct model types: 📌 𝗥𝗲𝘁𝘂𝗿𝗻 𝗼𝗻 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 (𝗥𝗢𝗜) Divides revenue from an investment by its cost. Use it when: You need a quick profitability snapshot. The formula is simple: (Revenue / Cost of Investment) x 100 📌 𝗣𝗮𝘆𝗯𝗮𝗰𝗸 𝗠𝗼𝗱𝗲𝗹 Measures how long it takes until an investment is paid back in full. Use it when: Cash flow timing matters more than total return. Calculate it as: 12 / ROI = months to pay back the funds. 📌 𝗗𝗶𝘀𝗰𝗼𝘂𝗻𝘁𝗲𝗱 𝗖𝗮𝘀𝗵 𝗙𝗹𝗼𝘄 (𝗗𝗖𝗙) Accounts for the time value of money by discounting future cash flows. Use it when: Comparing projects with different timelines or risk profiles and the timeframe is > 1 year. An NPV of zero means the present value of investments and returns are equal. 📌 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗥𝗮𝘁𝗲 𝗼𝗳 𝗥𝗲𝘁𝘂𝗿𝗻 (𝗜𝗥𝗥) The discount rate that would result in an NPV of zero. Use it when: You need to assess risk relative to returns. The higher the IRR, the higher the economic rate of return of your project. 📌 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗟𝗶𝗳𝗲𝘁𝗶𝗺𝗲 𝗩𝗮𝗹𝘂𝗲 (𝗖𝗟𝗩) Estimates how much net profit we expect to receive from customers over the period of time they spend money on our products or services. Use it when: Optimizing customer acquisition spending. SaaS companies use CLV as a benchmark to determine how much to invest in acquiring new customers. The difference between average and elite FP&A professionals? The average FP&A Analyst knows the formulas. The elite FP&A pro knows when to use each model and why it matters for the decision at hand. -Christian WattigP.S. Want my complete 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝟰𝟲 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 built in? It's the same framework I teach at Wharton Online. Free for my subscribers: https://lnkd.in/eBAmSF_6

The best financial model I ever built was useless. It had everything. Dynamic assumptions. Scenario planning. Monte Carlo simulations. I had poured weeks into it, convinced it would be the tool that shaped the next big decision. But when I presented it to the board, the reaction was clear: no one cared about the complexity. The model was technically brilliant but strategically irrelevant. The assumptions were too optimistic. The questions it answered weren’t the ones directors were actually asking. And by the end of the meeting, it sat untouched, another document filed away. That experience was humbling. It taught me that a CFO’s job isn’t to impress with spreadsheets. It’s to bring clarity to decisions. To take the complexity and distil it into something leaders can use with confidence. Now, when I build models, I start with one question: what decision needs to be made? Everything else flows from that. Because the truth is, a model can be perfect in design and still useless in practice. What matters isn’t the elegance of the formula; it’s the clarity of the outcome.

20 years of Financial Modeling Learnings in One single post... SAVE this I have been building financial models for the past 20 years. I have also been learning something new about this every day over the past 20 years! Here are my top learnings! 1) Always understand the business before approaching valuation modeling. Without understanding the business, the model is meaningless. - What does the company do? - How does it make money - What is the value chain? - Are their any competitive advantages that it has? 2) Complex is NOT equal to better Make granular models, but don't make them unnecessarily complicated. 80% of the business value will come from 20% of the key drivers. Focus on them. Too much granularity on every component does not help. 3) Revenue projections and business projections are to be based on your understanding of the business, and not on history. If we use history, companies that are growing will keep growing, and those that haven't grown, will never grow 4) Conceptual clarity on corporate finance concepts is key - Cost of Debt has to be lower than Cost of Equity - Cost of Debt cannot be lower than risk free rate - How to project growth? - How to work with terminal value? 5) Ensure consistency in your assumptions For example, revenue cannot grow without consistent capex assumptions, or working capital assumptions. 6) Always make the models READABLE Your financial models are to be used by teams in organizations. Make them readable. If you follow steps 1 and 2, the model will automatically tell a story. But help others understand the model. Keep decimals consistent. Use color coding where needed. Arrange data neatly. 7) ALWAYS project a balance sheet, and a 3 statement model This ensures consistency, and the fact that the business model can be evaluated across the 3 statements in the future. A model without a projected balance sheet is half done. 8) Build in scenarios, or sensitivity analysis A model includes various inputs, and they can be wrong. So this helps us understand the range of probable outcomes. 9) Last, but not the least, don't take your model too seriously. The model depends on inputs, so if inputs are not correct, the output will also be not correct. The financial model is a tool to help you as an analyst. It is not the other way round. Focus on the business, and points 1 and 2. Use these the next time you build a financial model! And do not forget to SAVE and SHARE the post! ----- Peeyush Chitlangia, CFA I help you build better valuation models Do reach out if you are looking to learn the practical aspects of valuation!

𝗥𝗲𝗮𝗹 𝗦𝗮𝗹𝗲𝘀 𝘃𝘀. 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝗶𝗼𝗻𝘀 – 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 In business, projections are helpful, but real sales are what truly drive success. Projections paint a promising picture, but they can also mask potential pitfalls that could arise if not grounded in reality. Transparency about your numbers and challenges is key not only does it build trust with stakeholders, but it also sets a strong foundation for long-term growth. Here’s why real sales matter over projections and how to avoid pitfalls: 𝗖𝗮𝘀𝗵 𝗙𝗹𝗼𝘄 𝗥𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀: Projections often miss the mark on real-time cash flow issues. Focus on accurate sales data to maintain healthy cash flow and prepare for fluctuations in revenue. Keeping an eye on actual sales helps you understand when revenue gaps may occur, avoiding unexpected shortages. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗕𝘂𝗶𝗹𝗱𝘀 𝗧𝗿𝘂𝘀𝘁: Overstating results or hiding struggles may lead to short-term confidence but will damage long-term credibility. Being honest about real sales figures and challenges ensures investors, partners, and teams are aligned and can address issues together. 𝗔𝗰𝗰𝗼𝘂𝗻𝘁 𝗳𝗼𝗿 𝗠𝗮𝗿𝗸𝗲𝘁 𝗩𝗼𝗹𝗮𝘁𝗶𝗹𝗶𝘁𝘆: Projections are often optimistic, but markets can change quickly. Always be cautious about external factors like economic shifts or new competitors that can impact real sales. Stay flexible and adjust expectations as conditions evolve. 𝗣𝗹𝗮𝗻 𝗳𝗼𝗿 𝗦𝗵𝗼𝗿𝘁𝗳𝗮𝗹𝗹𝘀: Projections rarely consider unexpected obstacles. Having a contingency plan for when actual sales fall short helps maintain operational momentum. Whether it’s securing additional funding, diversifying income streams, or cutting costs, prepare for unforeseen challenges. 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲, 𝗡𝗼𝘁 𝗔𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Set realistic targets based on actual sales performance. Projections serve as a guide, but track real-time performance metrics to better assess where you stand. This will help you make data-driven decisions to stay on course. In the long run, focusing on real sales, transparency, and adaptability will ensure sustainable growth—far more than relying on optimistic forecasts alone. #RealSales #TransparencyInBusiness #SalesStrategy #BusinessGrowth #SustainableGrowth #CashFlowManagement #MarketRealities #TrustAndCredibility #SalesPerformance #BusinessStrategy

Travelers announced TravelersLLM, a proprietary large language model trained on millions of internal documents. The headline will be that it beat commercial AI models on insurance questions. The line worth your attention is quieter: it works alongside leading frontier models. That one line reframes it. Travelers is not trying to out-build the AI labs. It is building the one layer no lab can replicate: decades of curated underwriting, claims, and distribution knowledge, structured so machines can use it. Rent the intelligence. Own the context. One caveat: the benchmark claim is Travelers' own internal testing, not independent evaluation. Confidence, not a scorecard. Travelers calls the model the foundation for agentic applications and names distribution partners as direct beneficiaries. When institutional knowledge is instantly accessible at the point of sale, the service gap between carriers becomes a data readiness gap, not a headcount gap. The uncomfortable question for other carriers: is your institutional knowledge in a state any model could learn from? For most, the answer is no. It lives in retiring underwriters and unstructured file notes. Takeaway: The moat is not the model. It is the curated data underneath it. Carriers that treat knowledge capture as an AI project rather than an HR afterthought will own the next decade of distribution service.

🔎 From Access to Context in Insurance AI TravelersLLM is a useful case for understanding where insurance AI is heading after the first wave of internal GPT platforms. In 2023, the focus was access: giving employees safe environments to use general-purpose LLMs. AXA Secure GPT and AllianzGPT are examples of that phase. That solved one problem, but access alone is not enough. The harder challenge for insurers is context. Useful AI in P&C insurance needs to work with proprietary knowledge: underwriting judgment, claims experience, policy wording, risk classification, operational history, expert knowledge, and risk appetite. This is where TravelersLLM becomes interesting. Travelers describes it as a proprietary LLM tailored to its property casualty business. Public information suggests that the aim is not to build the largest model, but to make insurer-specific knowledge more usable at scale. Still, context is only the current milestone. The next challenge is operationalization. As insurers move toward future agentic applications, insurance AI cannot simply mean giving more autonomy to a model. It requires controls, escalation rules, documentation, and clear accountability. The real challenge is not building the largest model. It is turning proprietary insurance knowledge into a consistent, cost-efficient, and governed AI infrastructure. #Insurance #GenAI #InsurTech