Financial Modeling Fundamentals

Here’s a paradox of modern AI that many organizations deploying Large Language Models (LLMs) are discovering: a model can ace a university-level math exam but still struggle with seemingly simple arithmetic. As LLMs become more integrated into daily operations, we've observed that users are naturally attempting to leverage them for complex calculations, like figuring out payment schedules or financial projections. The potential for instant, helpful answers is immense. However, we know that LLMs, for all their conceptual brilliance, can not perform calculations in a fully reliable way. This inherent limitation presents a significant challenge for any financial institution where accuracy is paramount. At Scotiabank, we put this to the test. Through experimentation with hundreds of real-world financial calculation questions, we observed that: • Quite often, the AI's answers were remarkably close, within a dollar. • Crucially, in other cases, the answers were off by tens of dollars. For a bank, "close enough" is never good enough when it impacts a client's financial situation. A minor discrepancy, while seemingly small, can erode the most important thing we build: trust. This led us to a crucial decision regarding our internal AI tools. Rather than risk providing potentially inaccurate financial calculations, we've implemented safeguards. Our LLMs are now prompted to decline direct calculation requests, instead guiding users to established formulas. This story highlights a core principle of our AI strategy, and what we believe should be an industry standard: trust and accuracy above all else. It's not about limiting technology; it’s about deploying it responsibly, with a clear understanding of its current capabilities and inherent risks. The long-term solution for such challenges is already in sight: augmenting LLMs with dedicated, reliable tools for precise calculations, moving us toward a more robust, agentic model where AI can seamlessly leverage external, verified systems. This work is underway across the industry, and we will only roll it out when it’s proven to be precise and reliable.

Capital Modeling for Parametric Insurance As parametric insurance gains traction, insurers face specific challenges in capital modeling and regulatory capital navigation. 1. **Regulatory Uncertainty**: The treatment of parametric insurance under frameworks like Solvency II and SAM remains ambiguous. Insurers must engage proactively with regulators to establish appropriate methodologies. Regulators have the challenge of how to shoe-horn parametric insurance into a regulatory framework that was not designed with this in mind. 2. **Line of Business Allocation**: Fitting parametric products into traditional lines of business is complex. Many parametric products resemble inwards non-proportional reinsurance more than direct insurance, with payouts triggered by specific events. Even then, there is no guarantee that the standard premium volatility factors are appropriate. Insurers may need to explore Undertaking/Insurer Specific Parameters (USP / ISP) or transition to partial internal models. 3. **Portfolio Size and Trigger Remoteness**: The risk profile changes significantly with smaller portfolio sizes and trigger remoteness. As triggers become more remote, the capital required relative to premium increases. At a certain point, the 99.5th VaR can fall well outside the 3-sigma range, challenging standard deviation-based approaches. 4. **Diversification Effects**: Understanding correlation between parametric triggers, and at different levels of triggers, means approaches like copula modeling might be necessary. Student t copulas are a likely candidate. 5. **Attritional vs. Catastrophic Losses**: The binary nature of parametric triggers blurs the line between attritional and catastrophic losses. 6. **Time Series vs. One-Year Capital View**: While sensor data forms a time series that could be modeled using techniques like SARIMAX or GARCH-X, the one-year capital view required by regulations doesn't necessarily need to incorporate this time series structure. 7. **Climate risk and trends**: An advantage of parametric insurance is the typical clean time-series sensor records (necessary for pricing and risk management). However, the continued relevance of historical records is at risk given climate change for many key parametric coverages. 8. **Demonstrating Appropriateness**: The Head of Actuarial Function (HAF) faces the challenge of demonstrating that the chosen capital approach appropriately reflects the risk profile of parametric products. The approach needs to work within the regulatory framework, but the result must still be reasonable. As the parametric insurance market evolves, so too must our approach to capital modeling. The challenges are significant, but so are the opportunities for innovation and more accurate risk assessment. #ParametricInsurance #CapitalModeling #InsurTech #RiskManagement #Solvency2 #SAM #ISP #USP #HAF #InternalModel #RiskAppetite

Indian Motor Insurance Market: A 2025 Deep Dive into Trends, Profitability, and the OD-TP Balancing Act. The motor insurance industry saw a mixed bag in January 2025—some insurers surged ahead while others faltered. Top Performers: The Growth Champions These insurers are setting the benchmark for market leadership and profitability: • ICICI Lombard General Insurance is the market leader, collecting ₹8,888.49 crore with 15% growth—showcasing strong underwriting discipline. • The New India Assurance Co Ltd, the leading PSU, holds ₹8,552.87 crore, growing at 9% YoY, but its high TP dependence could impact long-term profits. • Tata AIG General Insurance displayed stellar growth of 22%, reaching ₹7,394.53 crore, largely due to a balanced OD-TP mix and digital innovation. • SBI General Insurance was the fastest-growing insurer at 36%, signaling an aggressive expansion strategy in motor insurance. Struggling Insurers: The Red Flags While the industry grew, some insurers saw sharp declines: • HDFC Ergo (-39%) suffered significant premium loss, potentially due to restructuring or selective underwriting. • Navi General (-54%) seems to be exiting or significantly scaling down operations. • IFFCO-Tokio (-9%) indicates potential distribution or pricing challenges. 2. OD vs. TP: The Profitability Puzzle Industry-Wide OD-TP Split: 41:59 Currently, 41% of total motor premiums come from OD, while 59% come from TP. This suggests that most insurers rely more on TP, which is less profitable due to fixed pricing and high claims. Who Has the Most Profitable OD-TP Mix? • Balanced Mix (Ideal for Profitability): • ICICI Lombard (51:49) • Tata AIG (45:55) • Bajaj Allianz (52:48) • These insurers maintain high OD premiums, ensuring better pricing flexibility and profit margins. • TP-Heavy (Lower Profitability, High Claim Risks): • The New India Assurance (37:63) • National Insurance (30:70) • SBI General (44:56) • Heavily dependent on TP, these insurers face lower profitability and limited pricing control. Ideal OD-TP Ratio for Profitability: 50:50 or OD-Heavy (55:45) ✅ Higher OD proportion → More pricing flexibility, higher profit margins. ✅ Balanced TP share → Stable revenue but without over-reliance on regulated pricing. ⚠️ Over-dependence on TP (>60%) → Lower profits, regulatory limitations, high claims volatility. ✅ Optimize OD-TP Mix → The ideal ratio is 50:50 or OD-heavy (55:45) for sustainable profits. ✅ Leverage Digital & AI ✅ Expand into Underserved Markets → Tier 2 & 3 cities hold immense growth potential. ✅ Differentiate via Product Innovation → Pay-as-you-drive models, EV insurance, and customer-centric add-ons ✅ Improve Claims Efficiency #Insurance #MotorInsurance #Underwriting #India #RiskManagement #DigitalInsurance #InsurTech

Have you ever wondered how lenders decide if a borrower is worth the risk? Let me take you behind the scenes of a process that’s as much about precision as it is about trust. Imagine: You’re sitting with a financial model that’s more than just numbers on a spreadsheet it’s a living story about a company’s future. This story guides decisions that could mean millions in funding or none at all. In lending, The stakes are high. Unlike equity investors, lenders don’t benefit if a company does exceptionally well. Our focus is simple yet critical: Will we get the principal and interest back? To figure this out, We don’t just look at one possible future. We create several. Here’s how: -Base Case: This is the borrower’s forecast. Optimistic, but plausible. -Downside 1 and 2: These are the “what ifs. What if sales drop? What if margins shrink? These are the scenarios that keep lenders up at night. But it’s not just about imagining worst-case scenarios it’s about preparing for them. Let’s say a company forecasts 5% sales growth annually. What happens if that growth dips to 2%? Or zero? Will they still have the cash flow to pay down their debt? Why does this matter? Because working capital, taxes, and capital expenses don’t stop just because sales do. The model accounts for it all: -Receivable days. How long does it take for customers to pay up? -Inventory cycles. Are products sitting on shelves too long? -Debt terms. What happens when interest rates rise? One particularly powerful tool in this analysis is a toggle feature. With it, we can flip between scenarios in seconds, testing the model’s resilience to real-world shocks. It’s like a stress test for the future. Imagine you’re planning a road trip. Your Base Case assumes clear skies, smooth roads, and perfect gas mileage. But then, You hit traffic, the weather turns, and you need a pit stop. A good financial model doesn’t just get you there in perfect conditions it ensures you’ll make it even when things go wrong. As I worked through this example, it hit me how much lenders act like navigators. We’re not here to control the company’s journey but to make sure their ship doesn’t sink. And that requires more than formulas it requires empathy, foresight, and a deep understanding of the businesses we partner with. What do you think? If you’ve built or used financial models like this, I’d love to hear your insights. How do you balance optimism with caution in your projections? Let’s dive into this in the comments.

The Alan Turing Institute: The Impact of Large Language Models #LLMs in #Finance: Towards Trustworthy Adoption As large language models (LLMs) evolve AI capabilities for interpreting complex, and often unstructured linguistic text and for their ability to generate engaging human-like language responses, The Alan Turing Institute’s Fair Prosperity Partnership is exploring emerging opportunities for safe, trustworthy adoption within the financial services sector. The Impact of large language Models in Finance: Towards Trustworthy Adoption captures collective insights revealed within a study conducted with the support of colleagues from HSBC, Accenture and the UK’s Financial Conduct Authority (FCA). The work included an extensive literature survey on the impact of LLMs in banking and insights shared by 43 participants who attended a face-to-face workshop examining questions about the likelihood, significance, and timing of the integration of LLMs into financial services. Workshop participants were from major high street banks, regulators, investment banks, insurers, consultancies, payment service providers, and other stakeholders, with the majority revealing that they have begun to employ LLMs for varied internal processes, and to actively assess their potential for market-facing activity. They illustrated a granularity of understanding that is inherently emerging with these deployments which could lead to the development of purpose-specific, auditable models that mitigate many risks stemming from the current lack of ability to predict or explain, and thereby rely on LLM outcomes. Overall, discussions delved into significant potential to tackle persistent concerns and develop strategies that could advance safe adoption of LLMs generally. They also elevated global considerations to be navigated, for example, unfair concentration of services in large organisations with the data to support LLM development or competitive advantage in countries with a favourable regulatory landscape. Recommendations included support for a sector-wide and cross-sector analysis of current use that can elevate best practices, and exploration into opportunities emerging with open-source models specialised in financial tasks, such as FinMA and FinGPT. Link: https://lnkd.in/eynqFm7E

Can Indian Insurance Be Warren Buffett’s Next Big Bet? The Indian insurance sector is at an inflection point. With penetration at 4.2% of GDP (Life: 3.2%, Non-Life: 1%)—far below the global average of 7%—the potential for explosive growth is undeniable. But would Warren Buffett, the world’s greatest value investor, put his money here? Let’s analyze through his investment lens. Performance Snapshot: The Big Picture 📈 Life Insurance Premium Growth: ~10-12% CAGR (FY18-FY23) 🚗 General Insurance Growth: ~16-18% CAGR (FY18-FY23) 💰 Claim Ratios: Life (~95%), Health (~100%+), Motor (~85%) 🏆 Market Leaders: LIC (Life: 58% share), ICICI Lombard, Bajaj Allianz(General) 🏦 Insurance Penetration: Below global standards, indicating untapped potential Does Indian Insurance Pass the Buffett Test? One of Buffett’s core investment principles is to seek businesses with strong competitive moats. Indian insurance companies benefit from high regulatory barriers, making it difficult for new players to enter the market. Additionally, insurers accumulate large investment float, similar to Buffett’s insurance holdings in Berkshire Hathaway, which allows them to generate significant returns from capital before claims are paid. However, the sector struggles with high underwriting losses due to inflated claim ratios, particularly in health and motor insurance. LIC, the market giant, still operates with legacy inefficiencies that impact its profitability and agility. On the upside, India’s expanding middle class and increasing financial awareness offer massive growth potential. Fintech-driven models like AI-led underwriting and embedded insurance could revolutionize risk assessment and improve profitability. However, insurers must contend with rising fraudulent claims, intensifying digital competition, and evolving regulatory challenges. The Buffett Playbook: Steps to Make Indian Insurance a Profit Machine 📊 1️⃣ AI-Driven Risk Assessment: Reduce fraudulent claims and optimize pricing using big data and predictive modeling. 📈 2️⃣ Smarter Float Investments: Make insurance capital work harder by strategically investing reserves, following Buffett’s approach. 🤖 3️⃣ Cost Efficiency via Automation: Leverage AI, blockchain, and automated claim processing to reduce costs and improve operational efficiency. 🛡 4️⃣ Insurance for the Future: Offer innovative, tech-enabled products like pay-as-you-go, parametric insurance, and AI-driven pricing models. 🎯 5️⃣ Financial Literacy & Market Expansion: Increase awareness and accessibility, ensuring insurance reaches a broader audience. Final Thought: A Buffett-Style Success Story in the Making? The Indian insurance sector has all the ingredients to become a Buffett-style powerhouse—strong moats, smart capital allocation, and long-term profitability. However, success depends on embracing technology, optimizing underwriting, and innovating with customer-centric solutions. #Insurance #Investing #Fintech

Let’s understand a very important insurance concept that is CAT Modeling - Catastrophe (Cat) Modeling in insurance is a method used to assess and quantify the potential financial risks associated with catastrophic events such as natural disasters (e.g. earthquakes, hurricanes, floods) or large-scale man-made events (e.g. terrorism). The goal is to estimate the probability and severity of such events and to predict the potential impact on insurance portfolios, helping insurers better manage risk, set premiums, and plan for reinsurance. Key Components of Cat Modeling: Hazard Models: These models simulate the occurrence and intensity of catastrophic events. For example: Earthquake Hazard Models: Predict the likelihood and intensity of earthquakes in a region. Exposure Data: This refers to the data about the assets or properties that could be affected by a catastrophe, including: Location of buildings. Vulnerability Models: These models assess how likely different assets are to be damaged by a specific type of catastrophe. For instance: Flood Vulnerability: How susceptible various structures are to flooding depending on factors like elevation, construction type etc. Financial Impact Models: These estimate the potential financial losses caused by catastrophic events, such as damage to property, business interruption costs, and claims payouts. Loss Estimation: After applying hazard, exposure, and vulnerability data, cat models calculate the probable loss, which helps in determining insurance premiums and reinsurance needs. Earthquake Cat Modeling: Scenario: An insurance company insures properties in California, which is highly prone to earthquakes. Cat Model Use: The company applies earthquake cat models that use geological data to predict the likelihood of earthquakes in various areas. The model considers factors like fault lines, soil types, and historical earthquake patterns. Outcome: The insurer can assess the probable losses from potential earthquake events, allowing them to set premiums that reflect the actual risk and adequately prepare for possible claims. Hope this helps! #catmodeling #insuranceconsultant #reinsuranceconsultant #p&cbusinessanalyst #insuranceanalyst #insurancesme #insuranceconsulting

An insightful article on the growth of cyber insurance despite challenges like data breaches and ransomware. Effective risk management through adjustments and data collection has led to positive outlooks. However, next to rising ransomware incidents and evolving privacy regulations: “Another driver that should not be lost sight of is catastrophe risk. Despite the tail and development issues discussed above, cyber does not behave like a pure casualty line. Over the long term, its dynamics, capital demands and driving concerns are likely to turn on the scale of the catastrophes seen, more akin to property insurance. When considering the normalised cost of accumulation losses, we must rely on models due to the lack of historic data. However, over the past 24 months, the industry has seen an increasing number of events which so far have fallen short of "the big one", such as, Crowdstrike, MoveIT, Kaseya, or Change Healthcare. If these events are to be treated as part of the normalised accumulation loss, it should be possible to calculate how much of the normalised accumulation loss load remains to fund the more extreme tail events. If instead it is decided that these events are frequent enough to be part of the attritional losses that are expected most years, insurers/Munich Re must ensure it remains sustainably profitable despite these events and not succumb to the temptation to consider profitability on an “as-if” they didn’t happen basis. Cyber is neither property nor casualty but carries the challenges of both – accumulation as well as the long-tail risks. The fact that the cyber threat landscape is the most dynamic of all insurance lines makes these challenges even more complex. However, as the line grows, the tolerance for failures and missteps decreases. The good but still young years, 2022 and 2023, should not let us forget or lose respect for the profitability challenges. Minimising uncertainty and refining cat load requirements therefore remain priorities for Munich Re and the insurance market, to ensure the viability and sustainability of its cyber business in the long term.”

The Capital Adequacy Ratio (CAR), also known as the Solvency Ratio, for general insurers reflects the financial stability and ability of an insurer to meet its long-term liabilities and obligations, especially in the event of large claims. What Capital Adequacy Ratio Reflects Financial Health: A measure of how well an insurer can handle unexpected losses. Indicates the insurer’s capability to absorb shocks from claims or market downturns. Risk Coverage: Demonstrates whether the insurer has sufficient capital to cover underwriting, operational, and investment risks. Regulatory Compliance: Regulatory bodies like IRDAI in India mandate a minimum solvency ratio (usually 1.5 or 150%) to ensure insurers maintain a financial buffer. Confidence for Stakeholders: A higher ratio signifies a safer and more reliable insurer, inspiring confidence among policyholders, investors, and regulators. What a Negative Capital Adequacy Ratio Means A negative solvency ratio indicates a significant financial crisis within the insurer. Key implications include: Deficient Capital: The insurer lacks sufficient capital to cover its liabilities. Indicates that liabilities exceed the value of assets, resulting in a negative net worth. Inability to Settle Claims: The insurer might struggle to settle large or catastrophic claims, jeopardizing policyholder interests. Regulatory Non-Compliance: Failing to meet the minimum solvency margin could lead to regulatory interventions such as restrictions on issuing new policies or conducting specific operations. Risk of Insolvency: A negative CAR raises concerns about the insurer’s long-term survival and could lead to liquidation if not addressed. Loss of Credibility: Policyholders, investors, and stakeholders may lose confidence, leading to reputational damage and possible policy cancellations. Steps to Address Negative Solvency Capital Infusion: Raising funds through equity, debt, or external investments to restore solvency levels. For 3 PSUs- Government Intervention is must now. Reinsurance Support: Offloading risk through reinsurance agreements to reduce liabilities. Cost Optimization: Streamlining operations and reducing expenses to improve profitability. Product Portfolio Rebalancing: Focusing on profitable and low-risk insurance products to stabilize finances. Having better claim ratio always does not mean efficiency, whole claim management needed to be assessed.

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