Insurance Consulting Services

The problem of underinsurance among businesses, especially SMEs, is often framed as a simple cost-saving versus risk trade-off. However, this oversimplification ignores the intricate factors leading businesses to underestimate their vulnerabilities and the devastating ripple effects of being caught unprepared. A concerning report mentioned that 85% of MSMEs in India are uninsured! Moreover, many insured businesses have taken a policy only because it is mandated by a regulatory. Adding to this issue, I have witnessed multiple businesses that use insurance as a risk mitigation tool find their policy useless with inadequate coverage when facing a complex claim. Hidden liabilities are probably the most common reason behind such situations. Businesses are lulled into a false sense of security, only to discover the gaping holes in policy exclusions once a disaster strikes. The worst part is that such losses don't happen in a vacuum. Underinsured companies delay supplier payments, miss payroll obligations, and break contracts due to extended downtime. This sends tremors through the entire network they rely on. The true cost goes beyond immediate losses. It leads to stalled growth, lost opportunities while scrambling to recover, and a tarnished reputation that lingers long after the initial crisis. There’s a lot businesses can do to avoid such situations. The problem is not limited to saving costs on low premiums with inadequate coverage, or lack of awareness. The problem lies in bad strategic decisions. Many businesses, especially those with substantial tangible assets, underestimate the complexity of valuation in the modern economy. Outdated valuations often focus on physical assets – property, equipment. But what about lost revenue during downtime, the cost of data recovery after a cyber attack, or reputational damage that impacts future deals? Let’s unfold more layers. Businesses that depend on a network outside their direct control may have standard insurance coverage. But what do they do when their vendors are uninsured and suffer a major disruption? Managing risks in a volatile market isn't a simple accounting exercise. It needs to account for sector-specific risks and evolving threats to arrive at the true level of insurance protection required. Here's where a mindset shift is crucial. Treat your broker as a translator, not just a seller. Insist on plain language explanations of exclusions, and actively model how different policy options play out in 'worst-case' scenarios. Negotiate customisation to factor in that worst-case scenario, and be prepared to pay a premium for it. Use annual meetings to present changes in your business – new markets, technological shifts – and demand the insurance evolves in step. Indian businesses can't afford to view insurance as a sunk cost. It's an investment in securing the future. Take command of your risk profile and quantify the unknown to fill potential coverage gaps. Policybazaar For Business

Forecasting in Banking: Managing Uncertain Economic Environments Forecasting in the realm of banking is far from a straightforward process. Although the ultimate objective is to arrive at the most plausible predictions possible, the ever-changing economic landscape often presents challenges that make absolute precision impossible. However, that does not mean financial institutions should shy away from attempting to create reliable forecasts. When making forecasts, it is crucial to base these predictions on prudent and conservative assumptions. Banks often rely on historical data to project future trends; although this method has its merits, especially in stable economic conditions, it may not be the most advantageous approach when the economy is in flux. It is essential to factor in the realistic possibility of economic changes, such as interest rate fluctuations or market volatility, to arrive at more robust forecasts. Scenario analysis serves as an invaluable tool for generating realistic expectations about future financial conditions. It allows treasury professionals to examine various outcomes, assessing each for its likelihood and potential impact on the bank’s finances. Scenario analysis provides the advantage of preparedness, offering a range of plausible outcomes rather than fixating on a single, ideal projection. Modern technology, e.g. data analytics and algorithms, can offer increasingly sophisticated ways to improve the accuracy of forecasting models. While technology can significantly aid in making more accurate projections, it's crucial to remember that these tools should complement, not replace, human expertise. A balanced approach, incorporating both technological solutions and skilled professional judgement, tends to yield the most beneficial results. Regulatory frameworks often require banks to maintain a certain level of forecasting accuracy to ensure stability and to protect the interests of stakeholders. Consequently, a bank should always be aware of these requirements and incorporate them into their forecasting methodologies. Regulatory compliance, although often time consuming, provides an additional layer of scrutiny that helps to improve the forecasting process. It is important to understand that forecasting is not a one-off activity. Economic conditions change, sometimes in unpredictable ways, necessitating a revisit of previous forecasts. A best practice is to schedule regular review periods where assumptions can be reassessed, and forecasts updated, to reflect the most current and accurate information available. Overall, the approach to forecasting in uncertainty should be one of cautious optimism. The goal is not necessarily to predict the future with any accuracy, but to understand a range of plausible scenarios and prepare accordingly. By doing so, banks can make more informed decisions, better manage risks, and contribute to the long-term stability and success of their financial institutions.

Heat. Air Quality. Insurance Costs. An Indian Reality We Must Confront. Reflecting on a recent article I read around on how global heatwaves, air pollution, extreme weather are no longer distant threats. They’re having real, measurable impacts on homes, health, and financial risk. As an insurance broker, I believe it’s our duty to understand these changes, and help India stay resilient. Here’s what our sector should be really be thinking about:   What’s Changing, and Why It Matters 1. Rising temperatures and worsening air quality are more than environmental issues, they lead to greater health risks (respiratory, cardiovascular), increased mortality, and greater stress on medical systems. 2. Homes in many Indian cities are more exposed: ageing infrastructure, poor insulation or ventilation, and limited cooling systems magnify heat stress. 3. As insurers factoring in more frequent claims for heat damage, pollution-related losses, and weather disasters, premiums go up. That may make cover harder to access for many.   What the Insurance Industry Must Do 1. Embed Climate & Health Risk into Underwriting We need granular data: mapping risk zones for heat, pollution, flood etc., and using that to price fairly. Homes in “hot-spots” may need additional risk mitigation built into policies. 2. Design Products that Pay for Prevention Develop solutions that reward preventive measures, from cool roofing and air filtration to safer construction practices, where it is best to avoid the use of hazardous materials like asbestos. Parametric/trigger-based covers can also play a role, activating when thresholds such as heat index or AQI are breached. 3. Educate and Partner with Clients Many customers are unaware of how indoor heat or local air quality can damage property, health, and finances. Brokers must become educators, helping people assess risk, explore mitigation, reduce exposure. 4.Collaborate with Regulators & Local Governments Building codes, city planning, heat-mitigation infrastructure, pollution control, these are public goods that reduce risk for everyone. Working together can help reduce insurance risk, keep costs manageable, and make adaptation scalable. Why This Is a Leadership Opportunity India is uniquely placed. We have diverse climates, rapid urbanisation, and growing awareness. By acting now: Build trust: clients will value brokers who anticipate change, offer stable, forward-looking solutions. Drive innovation: those who develop climate-resilient products will lead, not lag, as regulation and customer expectations evolve. The realities of climate change are here and so are opportunities: to protect, to innovate, to lead. Insurance isn’t just about recovering losses, it’s about building resilience and enabling safer, healthier lives. #ClimateRisk #IndiaResilience #HealthAndClimate #RiskManagement https://lnkd.in/dYrveZd3 

Subscription fraud is often invisible - but its impact is significant. Fake free trials and recurring payment abuse rarely appear fraudulent at the start. They typically mimic legitimate user behavior, making detection challenging. Common fraud patterns in subscription businesses • Multiple accounts created by the same user • Use of temporary emails and shared or stolen cards • Abnormal usage during trial periods • Intentional chargebacks after extensive consumption Business impact • Revenue leakage • Increased chargeback ratios • Payment gateway penalties • Distorted growth and retention metrics • Higher customer acquisition costs How fraud is detected effectively • Device and IP intelligence • Behavioral signal analysis • Payment reuse and failure patterns • Usage anomalies during trials and renewals Prevention strategies that scale • Limit free trials per device and payment method • Apply step-up verification for high-risk users • Monitor usage prior to renewals • Block bots and high-risk IP ranges • Leverage AI models to identify evolving fraud patterns Outcomes of a strong fraud strategy • Reduced fake users • Lower chargebacks • Accurate business metrics • Protected recurring revenue • Improved trust with genuine customers Fraud prevention is not friction. It is a safeguard for legitimate users and sustainable growth.

𝗨𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗔𝗜 𝘁𝗼 𝗖𝗼𝗺𝗯𝗮𝘁 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝘂𝗱 The rise of instant payments has made AI-powered fraud detection a necessity. Unlike traditional rules-based systems, AI can spot subtle behavioral patterns across vast datasets in real time—vital for detecting complex, fast-moving fraud. Yet, as AI becomes central to fraud prevention, its responsible and transparent use is just as important. Consumers must be protected not only from fraud but also from the unintended harm of biased or opaque AI models. The stakes are high: an estimated 42.5% of fraud attempts now use AI, and nearly a third are successful. Criminals are evolving too, leveraging deepfakes and generative AI to bypass controls. The global market for deepfake detection is projected to grow 42% annually, from €4.73B in 2023 to €13.5B by 2026. Businesses are responding—three-quarters plan to adopt AI-driven fraud prevention tools—but fewer than a quarter have begun implementation, exposing a gap between awareness and action. At its core, AI’s strength lies in pattern recognition—automatically identifying relationships and anomalies in data. Just as a human analyst might, AI detects shifts such as unusual geolocation, new devices, or behavioral changes. In money-laundering cases, for example, mule accounts often move funds in chains; AI’s ability to view the network as a whole helps uncover these linked transactions. Fraud doesn’t appear in isolation—it often comes in waves and trends. Machine-learning models can evolve as new behaviors emerge, unlike static rules-based systems that require post-loss analysis to update their logic. This adaptability is especially crucial in an era of instant payments, where funds move within seconds. 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝘂𝗱 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗡𝗲𝗲𝗱 𝗳𝗼𝗿 𝗦𝗽𝗲𝗲𝗱 Speed is the main challenge. Instant payments typically settle within 10 seconds, leaving almost no time for manual fraud checks. While some transactions can be delayed if flagged as suspicious, decisions must be made instantly. Rules-based systems struggle here—they tend to generate too many false positives, draining resources and delaying legitimate payments. In contrast, AI-enhanced systems evaluate transactions in real time, combining models and rules to minimize friction. This enables fraud teams to focus their attention on the truly risky cases. Ultimately, AI doesn’t replace human judgment—it amplifies it. By providing real-time intelligence and adapting to new fraud patterns, AI helps businesses strike the balance between security and customer experience. As instant payments continue to expand globally, this balance will define the winners in the next phase of fraud prevention Source Visa #fintech #ai

Fraud wasn’t supposed to be a core product challenge. But for most businesses operating online today, it has staunchly become one. In 2024, Indian businesses lost ₹22,842 crore to cybercrime. That’s a 206% increase over the previous year. The first few months of 2025 have already added another ₹7,000 crore in losses. This isn't just a compliance or security concern anymore. It shows up as frozen accounts, locked working capital, rising chargebacks, and misuse through stolen cards, fake UPI payments, and promo abuse. What surprised us most was how quickly chargebacks became part of the everyday reality for merchants: 1. More than half involve deliberate abuse 2. Smaller businesses aren’t spared - around 30 percent of Indian SMEs now report direct losses from fraud, with revenue hits of up to 5 percent. The nature of fraud has changed. Attacks are faster, more coordinated, and more sophisticated. The usual playbook of reacting after the damage doesn't hold up anymore. We decided to rebuild our approach from first principles. RiskShield is what came out of it. It’s a fraud detection engine that runs within the payment flow. It scores every transaction in real time using machine learning, detects fraud rings using graph intelligence, syncs with government risk data like I4C, DoT blacklist, NCRB, and blocks bad actors mid-transaction. It also flags early signs of promo abuse, card testing, and UPI manipulation. So far, RiskShield has helped block over ₹1,700 crore in fraud attempts. It has flagged 2 crore high-risk signals and protected more than 6,600 merchants. The system operates quietly in the background, with an F1 score of 87 percent which is a measure that balances precision (how often fraud alerts are correct) and recall (how much fraud we actually catch) and recall close to 95 percent. Most issues are prevented before anyone files a complaint. There’s still more work to do, but one thing is clear to us now: Fraud cannot be treated as an after-effect. It has to be designed against from the beginning. PS. Here's the flow we have built ⬇️

𝗧𝗵𝗲 𝗡𝗚𝗙𝗦 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗯𝗶𝗴— for the first time, we now have 𝘴𝘩𝘰𝘳𝘵-𝘵𝘦𝘳𝘮 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰𝘴 tailored for 𝘀𝘁𝗿𝗲𝘀𝘀 𝘁𝗲𝘀𝘁𝗶𝗻𝗴, 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗻𝗲𝗮𝗿-𝘁𝗲𝗿𝗺 𝗺𝗮𝗰𝗿𝗼 𝗿𝗶𝘀𝗸. 🔸 This isn't about 2050. It's the next five years, i.e. 𝟮𝟬𝟮𝟱–𝟮𝟬𝟯𝟬. 🔸 This isn't abstract. It's 𝗚𝗗𝗣 𝘀𝗵𝗼𝗰𝗸𝘀, 𝗰𝗿𝗲𝗱𝗶𝘁 𝗿𝗶𝘀𝗸, 𝗶𝗻𝗳𝗹𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘂𝗻𝗲𝗺𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁. 𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀: 1.  A smooth transition ("Highway to Paris") 2.  A delayed, abrupt policy shift ("Sudden Wake-Up Call") 3.  Physical risk disasters without transition ("Disasters & Policy Stagnation") 4.  A fragmented world with climate chaos and policy misalignment ("Diverging Realities") These scenarios are a wake-up call for taking short-term climate risks seriously. ➤ Delaying climate action could increase global 𝗚𝗗𝗣 𝗹𝗼𝘀𝘀𝗲𝘀 𝗯𝘆 𝗼𝘃𝗲𝗿 𝟯𝘅, and unemployment spikes by 1.3 percentage points (Sudden Wake-Up Call vs Highway to Paris). ➤ Climate disasters aren’t just regional anymore. Floods, fires and droughts in Asia or Africa can cut European 𝗚𝗗𝗣 𝗯𝘆 𝟭.𝟳%, driven by supply chain exposure. ➤ Credit risk spreads explode in carbon-intensive sectors. In some cases, default probabilities jump by 20–30 percentage points, stressing banks and insurers alike. ➤ Green sectors could lose out if the transition is abrupt, fragmented, or disrupted by physical shocks. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝘆 𝘁𝗵𝗲𝘀𝗲 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 𝗮𝗿𝗲 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 ➤ For the first time, compound hazards—droughts, floods, wildfires—are modelled together, showing how climate risk can become systemic through trade, finance, and supply chains. ➤ Monetary policy is now integrated, so climate shocks affect interest rate paths, inflation dynamics, and macroeconomic volatility. ➤ Financial contagion is now factored in. Using advanced modelling, the framework maps how climate-related losses feed into default risk, cost of capital, and sectoral investment flows. ➤ Sector-by-sector and region-by-region outcomes now include asset-level exposure, probability of default, and sovereign bond repricing, offering tools fit for risk management. 𝗠𝘆 𝘁𝗮𝗸𝗲 This release is a step-change in how we understand and model climate risk. These scenarios are critical because they model economic and financial impacts on business over the next five years. A timeline relevant for senior management, boards and shareholders. Because these scenarios capture dynamic feedback loops, sector-specific capital costs, and second-round effects that ripple through the financial system, the risk science is taken to a whole new level. These real-world complexities have been missing from science to date, which is why these scenarios are so critical. #NGFS #NetZero #ClimateRisk _____________ For updates, follow me on LinkedIn: Scott Kelly

🔍 Large Language Models for Novel Financial Applications❓ 📈 Large language models (LLMs) are rapidly evolving within the financial sector. We see generative models (FinGPT, FinMA, InvestLM, AdaptLLM, InvestAR, FinABSA) and discriminative ones (FinBERT, FLANG) alongside benchmarks (FLUE, FPB, FiQA, FinRED). This progress promises impactful financial applications 💰 💡 However, current applications often focus on stock predictions, robo-advisors, or sentiment analysis. But market uncertainties also pose unique risks. We need to explore innovative LLM applications to unlock their full potential in finance 📊 💻 Here are my thoughts on new, untapped applications for LLMs in finance: 🔷 Market Risk: Multi-Task Volatility, VAR and Return Prediction 🔹 Volatility Forecasting - Earning Calls, CPI Releases, FOMC meetings 🔹 Multi-task Volatility, VAR, Return model with cross-attention & embeddings 🔹 Audio, News, Summary Analysis, Time Series encoders for multi-modality 🔷 Credit Risk: Enhancing Fundamental & Alternative Data Analysis 🔹 Analysis of Fundamentals (balance sheet, income, bank, loan statement) 🔹 Fundamentals Nowcasting using company info (EA, News, Sentiment) 🔹 Extracting Debt, Restructuring, Bankruptcy Filing, Profit Warning Signals 🔷 Climate Risk: Net Zero Investing, Net Zero Transition, Emissions 🔹 Tool-Augmented RAG for Data Extraction, Transform & Emission Estimation 🔹 Monitoring CleanTech Novelty (critical minerals usage, new "nuclear fusion") 🔹 Identifying Climate Targets in National Laws and Policies using LLM 🔹 Scope 3 Emission Estimation via transaction description of purchase goods 🔷 Supply Chain Risk: Monitoring Risk-Pooling Strategies 🔹 Monitoring Risk-Pooling Strategies (reshoring, nearshoring etc.) 🔹 Earning Calls Risk Extraction with suppliers, variety and industry position 🔹 Identifying Events, Triggers, Spillover (Supply Chain Disruptions, COVID-19) 🔷 Geopolitical Risk: Measuring Geopolitical Risk & Social Unrest 🔹 News & Congressional Transcripts summaries for geopolitical risk measure 🔹 Forecasting Social Unrest: Extracted Events (legislation, polarity, religion) 🔷 Valuation Risk: Evaluating Startups/Private Firms in VC  🔹 VC Screening with summaries of prospects, market, team and technology 🔹 Startup Recommendation Models via Company Relation Extraction 🔹 Startup Success Forecasting Framework (Market, Founder, Product) ⏬ See Explanation & Links to Python Notebooks in Comments: 👉 To Start: Python Code for Market, Credit, Climate, SC, Geo and Valuation 👉 To Practice: Summary for Market, Credit, Climate, SC, Geo and Valuation 👉 To Research: Advanced Research Papers & Case Studies #novelty #financialapplications #fintech #marketrisk #creditrisk #climaterisk #supplychainrisk #geopoliticalrisk #valuationrisk #climateinvesting #riskmanagement #dataanalysis #netzerostrategies #emissionstracking #economicuncertainty #financialdata #reshoring #alternativeanalysis #netzeroinvesting #financialrisks

Earlier today at Clouds Media Group, I had a chance to discuss about Takaful (Islamic Insurance) In the dynamic landscape of insurance, two models stand out: conventional insurance and Takaful, Islamic Insurance. While both offer financial protection, they differ significantly in principles, structure, and benefits, making it crucial to understand their distinctions. Principles and Structure: Conventional insurance operates on the principle of risk transfer, where the insurer assumes the risk in exchange for a premium. Takaful, however, is based on the principles of mutual cooperation and shared responsibility (Tabarru) among participants, who contribute to a common fund to support each other in times of need. This cooperative structure not only fosters a sense of community but also aligns with ethical and transparent practices. Transparency and Risk Management: Takaful emphasizes transparency in its operations, ensuring clear disclosure of fund management and distribution. Moreover, it promotes risk management and loss prevention measures among participants, directly impacting the collective fund. In contrast, conventional insurance may not always provide the same level of transparency or focus on risk management. Community Benefits and Ethical Considerations: One of the key benefits of Takaful is its promotion of shared responsibility and community welfare, aligning with Islamic principles. Any surplus in the Takaful fund is returned to participants, fostering a sense of ownership and cooperation. This stands in contrast to conventional insurance, which is often criticized for its profit-seeking nature and potential conflicts of interest. Inclusivity and Impact: While Takaful is rooted in Islamic principles, it is not limited to Muslims alone. Its ethical and inclusive nature appeals to individuals seeking a more transparent and community-driven approach to insurance. By emphasizing cooperation over competition, Takaful offers a model that transcends religious boundaries, providing financial security for all. Why? CRDB Bancassurance Members interested in Takaful are welcome at CRDB Bancassurance, where they can access Takaful products and services tailored to their needs. CRDB Bancassurance's commitment to providing ethical and inclusive insurance solutions aligns with the principles of Takaful, making it a trusted partner for those seeking financial security. In a world where ethical considerations and transparency are increasingly valued, Takaful stands out as a viable alternative to conventional insurance. Its principles of mutual cooperation and shared responsibility not only offer financial protection but also foster a sense of community and inclusivity. As we navigate the complexities of the insurance industry, let us explore and embrace models that prioritize ethics, transparency, and collective well-being. #TakafulInsurance #EthicalFinance #InclusiveCoverage #InsuranceIndustry #CommunityDrivenCoverage

📈 𝗔 𝗡𝗲𝘄 𝗘𝗿𝗮 𝗶𝗻 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗧𝗶𝗺𝗲𝘀𝗙𝗠 In finance, forecasting isn’t optional; it’s 𝘀𝘂𝗿𝘃𝗶𝘃𝗮𝗹. Whether it’s predicting trading, credit risk, or demand planning, predicting the future makes or breaks outcomes. Simple, transparent models still rule in regulated areas. But in high-stakes, unregulated spaces, accuracy is everything, and that’s where cutting-edge models take over. 𝗘𝗻𝘁𝗲𝗿 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗧𝗶𝗺𝗲𝘀𝗙𝗠 🚀 Google Research has introduced 𝗧𝗶𝗺𝗲𝘀𝗙𝗠, a foundation model for time series forecasting, trained on 𝟭𝟬𝟬 𝗯𝗶𝗹𝗹𝗶𝗼𝗻 real-world data points. 𝗧𝗵𝗶𝗻𝗸 𝗼𝗳 𝗶𝘁 𝗮𝘀 𝗚𝗣𝗧 𝗳𝗼𝗿 𝘁𝗶𝗺𝗲 𝘀𝗲𝗿𝗶𝗲𝘀. 🔹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲:  • Decoder-only transformer (like LLMs).  • Instead of words, it uses patches of time-points as tokens.  • Capable of flexible context (input) and horizon (forecast) lengths. 🔹 𝗜𝗻𝗽𝘂𝘁𝘀 & 𝗢𝘂𝘁𝗽𝘂𝘁𝘀:  • Input: raw time series patches/textual data.  • Output: future sequences — and it can generate longer forecasts in fewer steps (reducing error accumulation). 🔹 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲:  • Zero-shot forecasts (no retraining needed) that outperform ARIMA, ETS and even rival deep learning models like DeepAR & PatchTST.  • Evaluated on domains like retail, weather, traffic, and finance. 𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗳𝗶𝗻𝗮𝗻𝗰𝗲?  • 𝗧𝗿𝗮𝗱𝗶𝗻𝗴 & 𝗔𝘀𝘀𝗲𝘁 𝗣𝗿𝗶𝗰𝗶𝗻𝗴: Faster forecasts, no lengthy retraining.  • 𝗥𝗶𝘀𝗸 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀: Long-horizon macro + credit simulations with more nuance.  • 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Blending text (news, filings, sentiment) with historical series for richer signals. ✅ Out-of-the-box accuracy across domains. ✅ Foundation models aren’t just for language anymore; they’re coming for finance.  • 𝗟𝗶𝗻𝗸 𝘁𝗼 𝘁𝗵𝗲 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gc3PhT-S  • 𝗚𝗶𝘁𝗵𝘂𝗯: https://lnkd.in/gVUPJh9t  • 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲: https://lnkd.in/gZ-VksQ6 💬 Do you see multimodal foundation models like TimesFM reshaping forecasting in finance? 🔁 Repost to spread the word. 📌 Follow Puneet Khandelwal for more on quant, ML, and data science breakthroughs. #Finance #MachineLearning #DataScience #Forecasting #TimeSeries #Google #AI #Quant #Trading #ARIMA