Financial Modeling Fundamentals

I've reviewed hundreds of financial models across 100+ clients. Most of them fail in the first 30 seconds. https://lnkd.in/eHYUr9Jc The numbers might be fine. But I open the file and see 47 tabs with names like "Sheet2_final_v3" and I already know what I'm dealing with. Assumptions buried in random cells. No flow. No structure. If I can't follow your model, nobody else will either. This is the same 9-part structure I use at my firm and teach to every fractional CFO I work with. → Drivers TabThis is the most important tab in your entire model. One place for every assumption. Revenue growth, headcount, tax rates. Change one input and the entire model updates. No hunting through tabs. → Source Data TabsRaw exports from QBO or your ERP. Keep them separate from your calculations. One formula pulls from here to populate everything else. → Error Check TabValidates that data made it from source to destination. Assets equal liabilities plus equity. Revenue ties across statements. Green means fine, red means stop. → Instructions TabMost people skip this. Don't. Which cells are editable, which tabs are read-only, what each color means. Your model will get passed around. Make it easy to audit. → Three Financial StatementsIncome statement, balance sheet, cash flow. All pulling from the drivers tab. Historicals and projections in one place. → Revenue TabYour most important forecast. Build it separately, link it back to drivers. Every business is different here, but the connection to the model stays the same. → Headcount TabYour largest expense needs its own schedule. Start dates, salaries, departments, prorated amounts. One mistake here and your cash forecast is off by six figures. → Balance Sheet SchedulesAR, AP, CapEx, debt. Waterfalls that show how balances move over time. These connect your P&L to your cash flow. → DashboardsThe view your board actually sees. KPIs, summary financials, budget vs actual. Everything else feeds into this. You can build your own following this structure, or grab a free template here: https://lnkd.in/eHYUr9Jc What does your model structure look like?

The Day I Mastered My First Financial Model: A Turning Point Every career has a “before” and “after” moment. For me, it was the day I mastered my first financial model. I still remember staring at my laptop late at night, frustrated that my model wouldn’t balance. What I didn’t realize then was that my approach needed a complete overhaul. Here are some mistakes I made—and how I fixed them: 1️⃣ Circular References in Interest Calculations - What I did: Ignored the circular dependency between debt and interest, leading to mismatched numbers. - What I changed: Used Excel’s iterative calculations and set up a clear interest schedule, dynamically linking debt and interest. 2️⃣ Hardcoding Assumptions - What I did: Hardcoded key inputs (like growth rates) directly into formulas, making updates a nightmare. - What I changed: Built a centralized Assumptions Tab, linking every input to one place. This made updates seamless and reduced errors. 3️⃣ Inconsistent Time Periods - What I did: Mixed monthly and annual data in the same projection, causing misaligned calculations. - What I changed: Standardized the timeline across all sheets, referencing a global timeline for consistency. 4️⃣ Lack of Error-Checking Mechanisms - What I did: Eyeballed outputs without proper checks, often missing small but critical errors. - What I changed: Added error checks like Balance Sheet balancing rows, conditional formatting for missing links, and flags for negative cash balances. The Turning Point: - When I fixed these issues and the model finally balanced, I realized financial modeling isn’t just about numbers—it’s about building a dynamic, error-proof story with financial data. That night, I found my rhythm. Today, modeling feels second nature, but I’ll always remember the struggle and the breakthroughs that got me here. 💬 What about you? Have you ever had a moment where fixing a key mistake transformed your approach to work? Share your story—I’d love to hear it! #FinancialModeling #CareerGrowth #InvestmentBanking #SkillsThatMatter #investmentbanking

𝗪𝗵𝘆 𝗶𝗻𝘀𝘂𝗿𝗲𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝘁𝗿𝘂𝘀𝘁𝗶𝗻𝗴 𝗰𝗮𝘁𝗮𝘀𝘁𝗿𝗼𝗽𝗵𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗲 𝘄𝗮𝘆 𝘁𝗵𝗲𝘆 𝘂𝘀𝗲𝗱 𝘁𝗼 Catastrophe models were never meant to answer one question that boards are asking today: “𝙄𝙨 𝙤𝙪𝙧 𝙚𝙭𝙥𝙤𝙨𝙪𝙧𝙚 𝙘𝙝𝙖𝙣𝙜𝙞𝙣𝙜 𝙛𝙖𝙨𝙩𝙚𝙧 𝙩𝙝𝙖𝙣 𝙤𝙪𝙧 𝙢𝙤𝙙𝙚𝙡𝙨?” In 2025, that question has become impossible to ignore. Here’s what many insurers and reinsurers are struggling with right now: • Return periods are less reliable for secondary perils. • Loss clustering is happening across regions that were once treated as independent. • Event frequency is shifting faster than annual model updates. • Exposure is accumulating silently through growth, not underwriting intent. • Post-event loss creep is consistently higher than modelled expectations. This is where AI is being used in efficient ways, alongside traditional CAT models. 𝗪𝗵𝗮𝘁 𝗶𝗻𝘀𝘂𝗿𝗲𝗿𝘀 𝗮𝗿𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼𝗶𝗻𝗴 𝘁𝗼𝗱𝗮𝘆 Instead of running catastrophe models once or twice a year, teams are now: • Using AI to monitor exposure drift weekly, so portfolio changes don’t go unnoticed between renewals. • Layering near-real-time climate signals on top of vendor models to detect when assumptions start to break down. • Running thousands of scenario variations, not to predict the next event, but to understand where loss amplification could occur. • Using AI to identify emerging accumulation risks, especially from secondary perils like convective storms, flooding, and wildfire spread. • Comparing modelled vs actual post-event development patterns to adjust expectations around loss creep and reserve adequacy. • Highlighting where diversification assumptions no longer hold, before those correlations fail during a real event. This is not about replacing RMS or AIR style models. It’s about challenging them earlier and more often. 𝗪𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝘁𝗵𝗶𝘀 𝘂𝗻𝗹𝗼𝗰𝗸 𝗻𝗲𝘅𝘁 The most important shift is not better prediction. It’s earlier 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴. • Reinsurance buying decisions that reflect current exposure, not last year’s view. • Capital buffers adjusted before volatility hits, not after. • Underwriting appetite changes informed by live accumulation signals. • Portfolio steering away from emerging hotspots months earlier. • Clearer conversations with boards about uncertainty, not false precision. Cat modelling is transitioning from a static output to a continuous risk conversation. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗻𝗼𝘄 In a stable climate, historical models were good enough. In a volatile one, delay becomes risk. The insurers who treat catastrophe risk as a living signal, not a periodic report, will make better capital, underwriting, and reinsurance decisions even when the models are wrong. In today’s environment, being early matters more than being precise. #InsuranceLeadership #CatastropheRisk #AIinInsurance #Reinsurance #RiskManagement

Treaty Pricing Explained (Reinsurance Lifecycle) Treaty pricing is the process by which reinsurers determine how much premium to charge for assuming a portion of an insurer’s risk portfolio. It is not arbitrary; it is a disciplined actuarial and risk-based exercise aimed at balancing expected loss, volatility, capital cost, and profitability. 1. Understanding the Underlying Portfolio Pricing starts with the cedant’s book of business, including: Class of business (motor, property, engineering, liability, etc.) Exposure values and limits Geographic spread Policy wordings and deductibles Historical underwriting practices A treaty is only as good as the portfolio it protects. 2. Analysis of Historical Loss Experience Reinsurers review: Loss ratios (ultimate, not just reported) Frequency vs severity trends Large losses and catastrophes Claims development over time Past losses are adjusted for inflation, exposure growth, and one-off events to estimate future performance. 3. Expected Loss Cost (Burning Cost) For non-proportional treaties, pricing often starts with burning cost, which represents: Average losses to the layer ÷ subject premium This shows how much the treaty has “burned” historically and provides a baseline for pricing. 4. Risk Load & Volatility Margin Beyond expected losses, reinsurers add a risk load to compensate for: Volatility of losses Tail risk and catastrophes Model uncertainty Correlated exposures Higher volatility = higher risk margin. 5. Expenses & Cost of Capital Pricing includes: Reinsurer operating expenses Brokerage Cost of capital tied up to support the risk Target return on equity Catastrophe and long-tail risks attract higher capital charges. 6. Reinsurance Structure & Terms Pricing varies depending on: Proportional vs non-proportional Retention and limit Reinstatements Event definitions Aggregates and sub-limits Better structure can materially reduce price. 7. Market Conditions Finally, market dynamics matter: Capacity availability Losses in recent years Hard vs soft market cycles Even a good portfolio will cost more in a hard market. In Summary Treaty pricing is the outcome of: Expected loss + risk margin + expenses + capital cost, adjusted for structure and market conditions. It is less about price negotiation and more about risk quality, data credibility, and transparency.

Insurance doesn’t collapse in chaos, it erodes in silence. A mispriced risk here. A thin capital buffer there. And one day, the surplus runs out. In this fragile equilibrium, reinsurance isn’t just a strategy. It’s a safeguard against disappearance. And few tools are as underappreciated and as powerful as stop loss reinsurance. A recent study offers a rare, full spectrum view of how stop loss contracts reshape an insurer’s solvency. It’s not just about transferring tail risk. It’s about redesigning survival itself. Here’s what makes it remarkable: even short duration contracts significantly reduce the chance of ruin. When structured well, they offer a ceiling on aggregate losses, allowing primary insurers to stay afloat even in adverse claim cycles. And the capital required to ensure regulatory solvency drops sharply. Yet this isn’t magic. Relationship between capital, retention level, and contract duration is not linear. As retention increases, extra capital needed to carry that risk doesn’t rise proportionally. And as initial capital grows, incentive to cede more risk actually diminishes. There’s a balancing act at play one that demands precision, not guesswork. Study also introduces a methodology to simulate how these factors interact over time. It reveals that ruin probabilities plateau beyond a certain contract length. And that solvency can be modeled and actively engineered. And that well calibrated stop loss treaties can meet even the strictest solvency directives, without overburdening the balance sheet. Still, this isn't a call for blind reliance. Unlimited protection is neither economical nor widely available. Beyond a point, what reinsurers offer must be matched with what insurers optimize internally: capital planning, risk appetite, and retention strategy must converge. Insurers must keep in mind that Reinsurance isn’t a cost. It’s a covenant with continuity. Because in this industry, solvency is never static. It’s something you reearn every single year. Refer attached report for detailed insights.⬇️ #Insurance #Reinsurance #Solvency #RiskManagement

Nobody taught me financial modeling. I learned Excel by trial and error. Built hundreds of models. Broke hundreds more. Eventually became good enough to teach it to others. And here's what irritates me most: The models that look the most sophisticated are usually the worst. 50 tabs. Color-coded everything. Formulas that stretch across the entire alphabet. Beautiful. Impressive. Completely f*cked. You try to trace precedents? Good luck. You try to understand the logic? It's buried under layers of links nobody documented. Hours later, you finally figure out what it's doing. And then you find the broken pieces. → The circular reference someone "fixed" by hardcoding. → The hidden tab that was supposed to update but doesn't. → The assumption that changed 6 months ago but lives on in cell P47. Here's what I learned after thousands of models: The best financial models are boring. → One clear logic flow (no treasure hunts) → Assumptions visible on page 1 (no mystery inputs) → Can explain it on a whiteboard (if you can't, rebuild it) → Someone else can audit it in under 30 minutes (the real test) Complexity isn't sophistication. It's technical debt waiting to blow up your forecast. The CFOs I respect most? They build models a summer intern could follow. Not because they lack skill. Because they understand the real risk isn't being wrong. It's being wrong and not knowing why. What's your biggest financial modeling pet peeve?

Most startup financial models are beautiful lies. I’ve reviewed hundreds of early-stage models. And the pattern is clear: → CAC magically drops over time → Churn is “estimated” but never tracked → LTV isn’t calculated or worse, inflated → Headcount costs are wildly optimistic → There’s a “Misc” tab with $1.2M in it Why does this happen? Because founders treat models like investor theatre. Built to impress. Not to operate. The cost? → You raise capital with zero visibility on runway → You overhire and miss your margin targets → You make roadmap bets you can't actually afford → And worst of all? You realize too late that the business model doesn’t work Your model isn’t a pitch prop. It’s your decision engine. A good one should answer: → What happens if CAC jumps 25% next quarter? → Can we delay the next hire and still hit targets? → What’s real runway after expansion churn? If you can’t get those answers, you don’t have a model. You have a spreadsheet in a blazer. Here’s how to build one that actually works: 1/ Start with a clear purpose → What decisions should this model help you make? Hiring plan, pricing strategy, runway clarity? Be specific from day one. 2/ Ground it in real systems → Pull actuals from your CRM, accounting, and payroll. Your model is only as useful as the data it’s built on. 3/ Link your core financials → P&L, Balance Sheet, and Cash Flow should speak to each other. If they don’t, your forecast can’t be trusted. 4/ Segment revenue realistically → Break revenue down by product, customer type, or geography. Model retention, expansion, and churn by cohort — not hope. 5/ Reflect costs with accuracy → Include real team ramp times, founder comp, tech debt, and overlooked ops costs. This is where most risk hides. 6/ Run scenarios, add sensitivity → Best case, worst case, base case. Play with CAC, churn, and pricing levers. Your model should answer “what if?” 7/ Use and update it regularly → If your model isn’t revisited monthly, it’s already outdated. It should evolve with your business — not collect dust post-fundraise. Bottom line? If your model looks polished but doesn’t drive decisions.. Rebuild it. Your business depends on it. PS: Curious, what’s the one metric you check first when you open your model? ——— Need help making the numbers make sense? I’m Mariya. Fractional CFO for SaaS startups. I help founders get clear on what the numbers are really saying. 📩 DM me if your model doesn’t match your reality.

The IMF’s climate risk work repeatedly highlights how climate hazards and adaptation choices can transmit into financial sector outcomes over time, including insurers’ exposure to weather related disaster risks and the role of reinsurance in net claims dynamics. 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 (𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐚𝐜𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐂𝐄𝐎𝐬, 𝐂𝐅𝐎𝐬, 𝐂𝐑𝐎𝐬, 𝐂𝐂𝐎𝐬) ↳ 𝐑𝐞𝐛𝐮𝐢𝐥𝐝 𝐭𝐡𝐞 𝐭𝐢𝐦𝐞 𝐡𝐨𝐫𝐢𝐳𝐨𝐧: Move beyond 12-month pricing plus a historical cat view. Operationalize 5-10 year “repricing realism” assumptions by line and geography (how quickly can you actually reprice, exit, or re-underwrite). ↳ 𝐓𝐫𝐞𝐚𝐭 𝐦𝐨𝐝𝐞𝐥 𝐮𝐧𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲 𝐚𝐬 𝐚 𝐩𝐫𝐢𝐜𝐞𝐝 𝐫𝐢𝐬𝐤 𝐟𝐚𝐜𝐭𝐨𝐫: Add explicit loadings or capital buffers for model drift, demand surge, litigation inflation, and correlated perils. Make it visible in portfolio steering, not buried in actuary notes. ↳ 𝐋𝐢𝐧𝐤 𝐎𝐑𝐒𝐀, 𝐫𝐞𝐢𝐧𝐬𝐮𝐫𝐚𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐮𝐧𝐝𝐞𝐫𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐚𝐩𝐩𝐞𝐭𝐢𝐭𝐞 𝐢𝐧 𝐨𝐧𝐞 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐥𝐨𝐨𝐩: If the ORSA scenario says volatility rises, the reinsurance tower, attachment points, reinstatements, and aggregate protections should show the same story, and underwriting authority should follow it. IAIS expectations make this integration harder to avoid. ↳ 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐫𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞 𝐮𝐧𝐝𝐞𝐫𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐫𝐢𝐬𝐤 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐬 𝐠𝐫𝐨𝐰𝐭𝐡 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Close the protection gap by underwriting mitigation, not just loss. Where physical risk is rising, resilience services and parametric structures can be the difference between staying in market versus withdrawing. Long-term climate and catastrophe modelling is no longer an “innovation project.” It is becoming a solvency, conduct, and competitiveness requirement, with global standard setters reinforcing supervisory focus on climate risk within core insurance governance and risk management. If your board asked today, “Which assumptions in our cat and climate framework are most likely to be wrong, and what is our plan when they are,” what would you point to first? #Reinsurance #ORSA #Catastrophe #Climate #Insurance — ♻ Repost to help others in your network. 💾 Save this post for future reference. ➕ Follow me ( Kirill Patyrykin ) for more

What Nobody Told Me About Financial Models (Until I Found One That Actually Worked) I spent years building financial models that looked impressive...And were completely useless. Here's what nobody told me: 1. Pretty ≠ Useful My first model had: • 47 tabs • Color-coded sections • Beautiful charts • Complex formulas linking everything Looked amazing in board meetings. Couldn't answer: "What if we hire 5 people next quarter?" What I learned: The best models are boring. They answer questions. That's it. 2. You're Building the Wrong Thing I kept building models that showed me the past. Revenue last month. Expenses last quarter. What we spent last year. What I needed: Where will we be in 90 days? What happens if revenue slows? Can we afford this decision? Models should predict, not report. 3. Generic Templates Don't Work Downloaded a "startup financial model" from Google. Spent 20 hours trying to make my business fit into someone else's structure. Never worked. What I learned: SaaS models don't work for ecommerce. Ecommerce models don't work for marketplaces. You need the template built for YOUR business model. 4. Speed Matters More Than Accuracy I'd spend 6 hours building perfect projections. By the time I finished, the assumptions had changed. What I learned: Better to have an 80% accurate answer in 5 minutes than a 95% accurate answer in 5 hours. Decisions wait for speed, not perfection. 5. If You Can't Update It Yourself, You Don't Own It Hired a consultant. Paid $10K for a custom model, beautiful work. Every time I needed to change something, I had to pay them again. What I learned: A model you can't update yourself isn't a tool. It's a dependency. 6. Scenarios > Single Projections My models always showed one future. The "expected case." Never built best case or worst case. The problem: Real life doesn't follow your expected case. What I learned: Every model should answer: • What if things go better than planned? • What if they go worse? • Where's the danger zone? 7. Trust Is Everything If you don't trust your model's numbers, you won't use it and if you don't use it, why did you build it? What I learned: A simple model you trust beats a complex model you question. What finally worked: A model that: 1. Matched my actual business model 2. Showed forward visibility (90 days out) 3. Could answer scenario questions instantly 4. I could update myself 5. I actually trusted That's when financial models stopped being decorative and started being useful. Want a model that actually works for your business? We built 12 templates, each one for a specific business model. Not generic, not one-size-fits-all. Pick yours. Use it. Trust it. Free download here: https://lnkd.in/eyDWHcau P.S. I wasted so much time with models that looked good but didn't work. Don't make the same mistake.

Reinsurance Consulting Series – Day 16 Why Burning Cost and Exposure Rating Often Tell Different Stories As an experienced Reinsurance Consultant, one of the most common questions I encounter during treaty renewals is: “Should we trust the Burning Cost or the Exposure Rating?” The answer is usually: Both. But understand what each is telling you. Many pricing disagreements between cedants and reinsurers arise because one party focuses on historical experience while the other focuses on future risk. ⸻ What Is Burning Cost? Burning Cost pricing relies on: * Historical loss experience * Past treaty performance * Actual claims activity In simple terms: “What losses has this layer experienced historically?” This method works well when historical experience is credible and representative. ⸻ What Is Exposure Rating? Exposure Rating focuses on: * Current portfolio exposure * Risk profile * Sum insured values * Expected future losses In simple terms: “What losses could this layer experience in the future?” This method is especially useful when historical loss data is limited or changing. A Property Cat Example Consider a Property Cat XoL treaty. The cedant has experienced no major catastrophe losses in the last five years. Burning Cost may indicate: Very low expected loss cost. However, catastrophe modeling may show: * Increased coastal exposure * Higher insured values * Greater hurricane concentration Exposure Rating may indicate a significantly higher technical price. Why the Results Differ Burning Cost looks backward. Exposure Rating looks forward. One measures experience. The other measures exposure. Both perspectives are important. Example from My Experience In one renewal, a treaty showed an extremely favorable Burning Cost. The cedant argued for flat renewal pricing. However, portfolio analysis revealed: * Significant growth in insured values * New geographic accumulations * Higher catastrophe exposure Exposure Rating suggested materially higher risk than historical losses indicated. The final pricing decision considered both perspectives. What Experienced Reinsurers Do Rather than relying exclusively on one method, they evaluate: * Burning Cost * Exposure Rating * Catastrophe Model Results * Market Conditions * Portfolio Changes * Risk Appetite The goal is to understand both historical performance and future risk potential. ⸻ Key Insight A treaty with a low Burning Cost is not automatically underpriced. A treaty with a high Exposure Rating is not automatically overpriced. The real value comes from understanding why the two methods differ. Key Message In reinsurance pricing, Burning Cost tells you where the portfolio has been. Exposure Rating tells you where it could go. The best underwriting decisions consider both. Next in the Series (Day 17): Why Social Inflation Is Reshaping Casualty Reinsurance Pricing #Reinsurance #TreatyPricing #BurningCost #ExposureRating #ExcessOfLoss #TechnicalPricing