Exemples de lead magnets LinkedIn en finance

Highlights from a recent video I made in the trading niche 📈 Simple visuals. Clear explanations. Engaging storytelling. 💬 Comment “TRADING” and I’ll share the project file. #TradingContent #VideoEditing #YouTubeAutomation #FinanceContent #ContentCreation

The Free Claude AI Finance Course (15 resources to master Claude in 2026) 👉 Comment "CLAUDE COURSE" and I will send it to you I am limiting access to the first 100 comments so make sure to be quick This is the most complete free Claude resource I have ever put together And it is worth $1,000+ in saved training time Most finance pros are using ChatGPT or Copilot But Claude is quietly becoming the tool of choice for serious financial work Cell-level citations in Excel Projects that keep your context clean per client Cowork mode for long-running tasks Extended thinking for complex scenario planning That is why I built this free course A curated path from beginner to advanced with 15 of my best Claude resources for finance Inside the course, you will find: 1. Top 100 Claude Tips 2. Claude Sonnet 4.6 deep-dive 3. Claude Opus 4.6 deep-dive 4. Claude in Excel: Build Financial Models 5. Claude for Finance: 4 Power Moves 6. Claude Voice Mode 7. 5 BEST Ways to Use AI in Excel (Video) 8. How to Master Claude in Excel 9. Claude Finance Playbook 10. Claude is in PowerPoint 11. AI Blueprint 2026 ft. Shawn Kanungo (Video) 12. How to Use Claude as a Finance Pro (Video) 13. Claude Cowork 14. How This CFO Saves 2+ Hours/Day (Video) 15. Claude's Free-Tier Features I have seen finance teams spend weeks trying to figure out Claude on their own Watching random tutorials Testing prompts that go nowhere Getting mediocre results That is because no one gave them a structured starting point This course is the shortcut I wish every finance team had on day one 👉 Comment "CLAUDE COURSE" if you want the link to access this free course Bonus: you will also get a free seat to my AI Finance Masterclass ♻️ Repost to help a finance pro who wants to get serious about Claude

Everyone's giving AI advice. Most of it is garbage. "Fire your team and replace them with AI." "Automate everything." "Let AI handle all your work." Sounds smart. Will set you back years. I've implemented AI across every department in my companies. Here's what actually works vs what just sounds good on LinkedIn: Fire your team and replace them with AI → Use AI to empower your team, not replace them. Work is more fun with people. Automate everything immediately → Fix the process first, then automate. Automating broken processes just makes them broken faster. Let AI make your business decisions → Use AI for insights, not final judgement. AI still hallucinates. Replace all brainstorming with AI → AI supports creativity, but humans innovate. AI can only reference what existed before. Feed AI as much data as possible → Better prompts need better context, not more. Too much data = context rot. Copy-paste other people's prompts → Build prompts around your own needs. Generic prompts create generic output. AI automatically gives you an advantage → Your advantage comes from how you use it, not just using it. -DM P.S. I put together a complete guide on implementing AI across every department in your business - sales, marketing, ops, finance, all of it. Message me "AIBUSINESS" and I'll send it over 👊 --- Enjoyed this? Join 300,000+ entrepreneurs receiving my weekly insights: https://bit.ly/3RQoZ9U

Finance has spent 40+ years defending a theory this chart seriously damages. In 1981, Robert Shiller asked a simple question: If stock prices reflect future dividends, why are they so much more volatile than the dividends themselves? 📌 Save this post. This is post #2 of 8 in the "Excess Volatility Puzzle" series. The full series is worth following start to finish. He tested it with one of the most important charts in finance. Shiller plotted two lines from 1871 to 1979: The first was actual stock prices. The second was what prices should have looked like if they rationally reflected the dividends that eventually came. The result was the problem. Actual prices swung wildly. Rational prices barely moved. Stocks were 5 to 13 times more volatile than the fundamentals justified. Not 10% more volatile. Not 50% more volatile. 5 to 13 times. That is not a small flaw in the theory. It is the flaw. If markets were truly efficient, those two lines should move closely together. They don’t. And they never did. That chart matters because it forces a much bigger question: If price is supposed to be a rational reflection of value, why has it behaved so differently from value for so long? This is one of the clearest reasons markets are driven by more than information. They are driven by narrative, psychology, and waves of belief that fundamentals alone cannot explain. I mentioned in my last post that markets are wrong more often than they are right. Shiller’s chart is the first 108 years of evidence. And the puzzle he identified in 1981 has only become more interesting since. In the next post, I’ll show what happened when researchers extended the data beyond 1979 and why the case against market efficiency got even harder to ignore. Comment "1981" plus make sure we're connected: And I will DM you the Robert Shiller 1981 Report🤝 To not miss a series post:  ➕ Follow Chris Murphy, CFA Murphy and ring the bell 🔔 Post #1: https://bit.ly/4dhj1dr Post #2: This post. Post #3 is coming Sunday. --- Repost to help educate your network ♻️ Substack and Newsletter Launching soon, subscribe for day one access. https://longrace.com https://chrismurphyhq.com

I just built a 35+ page complete guide on LLM Fine-Tuning and it’s finally ready! After months of research, experiments, failures, breakthroughs, and dozens of model runs… I’m excited to share my most comprehensive work so far: 📘 LLM Fine-Tuning Techniques With Real Code, Math & Case Studies This isn’t a basic overview. It’s a full, end-to-end handbook designed for: ✔ ML Engineers ✔ Data Scientists ✔ AI Researchers ✔ GenAI Builders ✔ Anyone serious about mastering LLMs What’s Inside the Guide? 📖 Core Fine-Tuning Techniques Full Fine-Tuning PEFT Methods LoRA & QLoRA (with math derivations) Instruction Tuning RLHF (Reward Models + PPO) 💻 Hands-On Code BERT Sentiment Fine-Tuning LoRA with Hugging Face QLoRA with 4-bit quantization Instruction dataset formatting Memory-efficient training tricks 🏢 Real-World Case Studies Healthcare → Clinical summarization (92% accuracy) Legal → Contract risk extraction Finance → Fraud detection saving $50M E-commerce → 300% faster product descriptions 📊 Advanced Concepts Hyperparameter search Evaluation beyond accuracy Continual & multi-task learning Deployment best practices Future directions: MoE, Constitutional AI ⚡ Why This Matters Fine-tuning is no longer optional. It’s how industries convert LLMs into domain-specific, high-ROI systems. And this guide aims to make that journey easier for everyone building in AI. If you’re working with AI, GenAI, or LLMs this guide will be a valuable resource. 👍 Follow Reshma Sriraman & #ReshmawithAI for more AI • ML • GenAI • Agentic AI projects If you’re working on fine-tuning, LoRA, QLoRA or GenAI projects, 💬 comment "resource" I’ll share additional resources, notebooks & datasets with you! 🔂 Repost for AI learners LLM Fine-Tuning explained with real examples and case studies. Join the learning community -https://lnkd.in/gyhqiuPG

100+ real examples across 10 industries. All using different types of AI to drive business outcomes…take action and operate autonomously. It’s already happening inside the world’s biggest enterprises. From AI diagnosing cancer to autonomous fraud detection to supply chains that self-optimize in real time… The question isn’t “Can AI do it?” It’s 👇 Where are you falling behind? If you’re:  • Exploring AI but don’t know where to start  • Unsure what to automate first  • Worried about wasting budget on experiments  • Trying to make a real business case internally This will open your eyes. Because once you see 100+ real use cases across: -Healthcare. -Finance. -Retail. -Marketing. -Manufacturing. -HR. -Cybersecurity. -Legal. -Logistics. You stop thinking “AI tool.” And start thinking about autonomous enterprise. 📌 Comment “AI” and I’ll send it to you.

I will likely never use Excel again. As a former banker, I genuinely believed one thing: My Excel skills would never get disrupted. This weekend, I had the same moment many developers had with Claude Code. Macros. Nested IFs. Circular models. That was my moat. I tested a new AI tool on a messy financial model: • 8 interlinked sheets • Broken references • Hard-coded numbers everywhere • Inconsistent formulas In minutes, it: – cleaned the structure – flagged inconsistencies – standardized formulas – explained what was wrong – and suggested a clearer architecture A real co-analyst. Now, let’s be clear: 1) You still need to understand what’s happening underneath. 2) You still need to sense-check. 3) You still need judgment. But something fundamental is shifting. For years, your value was partially tied to your technical execution skills: “How fast can you build the model?” “How well do you master the tool?” That constraint is dissolving. The bottleneck is no longer: 👉 “Can you technically do it?” It’s becoming: 👉 “Do you know what should be built?” 👉 “Can you structure the problem?” 👉 “Can you detect when the AI is wrong?” 👉 “Can you orchestrate the right agents?” We’re moving from software operators to system designers. From spreadsheet technicians to critical thinkers + agent managers. And this is going to hit way beyond Excel: Finance. Ops. Marketing. Legal. Strategy. The people who win won’t be the most technical. They’ll be the ones who: • Understand the fundamentals • Think clearly under uncertainty • And know how to collaborate with AI without outsourcing their brain If you’re still optimizing for “being the best at the tool,” you might be optimizing for yesterday. The real question now is: Are you training yourself to think at the level above the tool? Nathan Z. - thanks for the tool 😉 Comment below "Shortcut" if you want to know the tool!

We generated $10.6M for clients across 10 industries. So I'm giving away all 10 playbooks. Live. In one class. Here's what's inside: → Finance — where $85K/year roles get replaced → Insurance — the $6.3T market most AI builders ignore → Healthcare — how to sell into a $4.5T spend → Legal — intake systems that replace $150K in staff → Real Estate — lead qualification at scale → Private Equity — deal flow and portfolio automation → Accounting — workflow builds that close at $30K+ → Recruiting — AI that sources, screens, and books → Dental / DSO — the most underserved vertical in AI → Hospitality — loyalty and ops infrastructure 10 industries. 10 playbooks. One live session. Every attendee also gets the full LinkedIn Go-To-Market Guide. The exact system behind 94% of our revenue. April 19th. 2 PM. Only 500 spots. No replay. 1. Comment CLASS and I'll send you the registration link directly. 2. Repost to skip the line 🔁

Does legal AI actually reduce human error, or does it concentrate it? A lawyer/attorney who doesn't think will misuse AI. But would they have done meaningfully better before? The difference: before, an error stayed local. With an AI playbook deployed across thousands of contracts (including with leading solutions like Harvey, Legora or Spellbook for instance), a design flaw no longer hits one deal. It hits your entire legal infrastructure at once. Cybersecurity has documented this mechanism for years. So has finance (ISDA, 2008). Law is not immune. I published a full analysis of this in The Legal Technologist including concrete conditions for standardisation to remain governed, not just inherited. Link in the comments. Would love to hear your take — especially from in-house counsel, outside counsels and CTOs.

Most CFOs talk about resilience. Far fewer build the liquidity structures that actually 𝘤𝘳𝘦𝘢𝘵𝘦 it. Agree? On 𝟭𝟰 𝗔𝗽𝗿𝗶𝗹 𝟮𝟬𝟮𝟲 𝗮𝘁 𝟭𝟭:𝟬𝟬 𝗖𝗘𝗧, I’ll join Alexander Mutter from cflox GmbH to explore how Nordic CFOs and Treasurers are redefining working capital as a strategic resilience engine, not just a balance‑sheet metric. We’ll dig into three core themes shaping 2026: • How working capital moves beyond short‑term liquidity management toward long‑term resilience • How liquidity can stabilize supply chains and payment flows in volatile markets • How CFOs can gain planning certainty and flexibility in an increasingly unpredictable environment 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘄𝗲𝗯𝗶𝗻𝗮𝗿 𝗵𝗲𝗿𝗲: https://bit.ly/4sso8fN To support the discussion, I’m sharing this 𝗖𝗙𝗢 𝗖𝗮𝘀𝗵 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗖𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁, because a strong working‑capital strategy becomes far more powerful when paired with disciplined, day‑to‑day cash management. Say, "Cash Management" below, and I'll share the PDF. Together, they form the foundation for real resilience. P.S. If you had to strengthen one part of your cash cycle before the next shock hits, where would you start?

"We don’t need AI." The 3 dangerous assumptions hiding behind this phrase. (And why your business is already falling behind.) Behind this seemingly simple statement lie three very different realities → each with its own risks, blind spots, and costly consequences. And if you’re in finance, healthcare, legal, or any industry handling sensitive data ? Ignoring them could mean falling behind competitors, exposing your business to compliance nightmares, or even losing millions in inefficiencies. Let’s break it down. Reality #1: "We don’t see the value." (Translation: "We don’t understand AI well enough to know where it fits.") ❌ Myth: "AI is just hype. It’s for tech giants, not us."  ✅ Truth: Your competitors are using AI to detect fraud in real-time, automate compliance checks, and predict risks before they happen. Risk ? You’re leaving money on the table (while they take it). Fix : Start with one high-impact, low-risk use case (e.g., document classification, anomaly detection). Not to "do AI," but to solve a real problem better. Reality #2: "We already have something." (Translation: "We have a legacy system, a few scripts, or a vendor who says they ‘do AI.’") ❌ Myth: "We’re covered—we have tools."  ✅ Truth: Most "AI" in enterprises is : Rule-based automation (not real AI). A black-box vendor solution (you don’t know how it works or if it’s secure). A single-use model (trained on outdated data). Risk ? You’re paying for AI but getting a liability. Fix ? Ask your vendor : ✔ Is this real AI (can it learn from new data) ? ✔ Where is our data stored ? ✔ What’s the fallback plan when it fails ? If they can’t answer, you don’t have AI... you have a problem. Reality #3: "We don’t want to be sold AI." (Translation: "We’re tired of vendors pushing solutions we don’t understand.") ❌ Myth: "AI is just another buzzword to scare us into buying."  ✅ Truth: This isn’t about fear, t’s about responsibility. The real question isn’t "Do we need AI ?" but : Are we okay with missing fraud that AI could have caught ? Are we comfortable with manual processes costing us time and money ? Are we willing to let competitors out-innovate us ? Fix : Treat AI like cybersecurity or compliance, a non-negotiable part of modern business. Want AI that actually works for your business ? I help finance, legal, and data-sensitive industries build production-grade AI systems that are : ✅ Secure (built for compliance) ✅ Scalable (not just a one-off experiment) ✅ Explainable (no black-box solutions) Let’s talk. If you’re ready to move from "We don’t need AI" to "How do we do this right?" DM me or comment "AI." (No fluff. No hype. Just real AI for real businesses.)

Two agencies failed this finance client... Then we took them from 0 → 650 leads a month. Comment: "650" for the full breakdown. The first agency threw budget at broad targeting. The second kept "testing new creatives." Both missed the REAL problem. → It wasn't the ads → It wasn't the budget →It wasn't the platform The real problem was this... WHO was clicking. See, this client sells business loans. Specifically to restaurants. But their previous agencies? They were running generic "business funding" ads trying to talk to restaurants, real estate agents, and medical clinics all at once. Which means the ads spoke to nobody. Because a restaurant owner and a real estate agent have completely different language, pain points and capital needs. When you try to talk to everyone in one ad — everyone ignores you. So before we wrote a single ad, we asked five questions. Not about the creative. Not about the audience settings. About the BUSINESS. → What does a qualified lead look like? → Which industry has the biggest market? → Which one do they enjoy working with? → Which is most profitable? → Which has the highest close rate? That's what gave us the filter. One niche. One message. One funnel. Then we built ads that used THEIR language. Their visuals. Their world. Chef's outfits. Restaurant floors. The exact questions running through their head: "How much can I get? How fast? What are the rates?" We didn't try to be clever. We just entered the conversation already happening in their mind. And did the work that was required for exceptional results. 0 → 650 qualified leads a month. Same platform the last two agencies "couldn't make work." I break down the full 3-step case study... Just comment "650" and I'll send it to you 👇 Alisha Conlin-Hurd 👆 Building Inbound Marketing Machines for finance & investment companies.

Revenue teams love dashboards. But dashboards don’t fix broken strategy. The first thing we build isn’t reporting. It’s the GTM plan. -> Clear positioning -> Defined ICP -> Offer architecture -> Revenue sequencing If the strategy is unclear, measuring it just amplifies noise. Once the GTM plan is locked, we execute the plan, then we build the dashboards across: -> Marketing -> Sales -> Customer success -> Finance Connected. Not to track activity. To measure constraint. Profitability isn’t a reporting exercise. It’s the output of aligned execution across the entire revenue lifecycle. When strategy and measurement connect, decision quality improves. And decision quality compounds. If you’d like a sneak peek at how we implement the Go-To-Market Insights, comment “GTM” and I’ll share access.

𝗖̧𝗮 𝘆 𝗲𝘀𝘁, 𝘁𝗼𝘂𝘀 𝗻𝗼𝘀 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲𝘀 𝗪𝗘𝗣 𝘀𝗼𝗻𝘁 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲𝘀 𝘀𝘂𝗿 𝗻𝗼𝘁𝗿𝗲 𝘀𝗶𝘁𝗲 𝗲𝗻 𝗹𝗶𝗴𝗻𝗲. Dans le prolongement du lancement du site 𝗪𝗶𝗻𝘃𝗲𝘀𝘁𝘆, les 𝟱 𝗽𝗿𝗲𝗺𝗶𝗲̀𝗿𝗲𝘀 𝘀𝗼𝗰𝗶𝗲́𝘁𝗲́𝘀 qui commenteront « 𝗪𝗘𝗣 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 » sous ce post bénéficieront d’une 𝗿𝗲́𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗱𝗲 𝟳𝟬𝟬 € 𝘀𝘂𝗿 𝗹𝗲 𝗳𝗼𝗿𝗳𝗮𝗶𝘁 𝗪𝗘𝗣 𝗣𝗿𝗲𝗺𝗶𝘂𝗺. WEP Premium s’adresse aux sociétés qui préparent une 𝗹𝗲𝘃𝗲́𝗲 𝗱𝗲 𝗳𝗼𝗻𝗱𝘀 et qui recherchent plus qu’un accompagnement classique. Avec Winvesty, nous avons construit un programme qui 𝗮𝘀𝘀𝗼𝗰𝗶𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝘁𝗶𝗼𝗻 et 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗲́, pour augmenter concrètement les chances d’une entreprise d’être 𝘃𝘂𝗲, 𝗶𝗱𝗲𝗻𝘁𝗶𝗳𝗶𝗲́𝗲 𝗲𝘁 𝗿𝗲𝘁𝗲𝗻𝘂𝗲 𝗽𝗮𝗿 𝗱𝗲𝘀 𝗶𝗻𝘃𝗲𝘀𝘁𝗶𝘀𝘀𝗲𝘂𝗿𝘀. Cette dynamique s’appuie notamment sur le 𝗹𝗮𝗯𝗲𝗹 𝗪𝗘𝗣, relayé sur 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻, afin de renforcer la présence, la lisibilité et la différenciation des sociétés accompagnées. 𝗦𝗶 𝘃𝗼𝘁𝗿𝗲 𝘀𝗼𝗰𝗶𝗲́𝘁𝗲́ 𝗲𝘀𝘁 𝗰𝗼𝗻𝗰𝗲𝗿𝗻𝗲́𝗲, 𝗹𝗮 𝗺𝗮𝗿𝗰𝗵𝗲 𝗮̀ 𝘀𝘂𝗶𝘃𝗿𝗲 𝗲𝘀𝘁 𝗹𝗮 𝘀𝘂𝗶𝘃𝗮𝗻𝘁𝗲 : – 𝗹𝗶𝗸𝗲𝘇 𝗹𝗲 𝗽𝗼𝘀𝘁 – 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝗲𝘇 𝗪𝗘𝗣 𝗣𝗿𝗲𝗺𝗶𝘂𝗺 – 𝗿𝗲𝗽𝘂𝗯𝗹𝗶𝗲𝘇 𝗹𝗲 𝗽𝗼𝘀𝘁 – 𝗿𝗲𝗺𝗽𝗹𝗶𝘀𝘀𝗲𝘇 𝗹𝗲 𝗳𝗼𝗿𝗺𝘂𝗹𝗮𝗶𝗿𝗲 𝗱’𝗶𝗻𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝘀𝘂𝗿 𝗹𝗲 𝘀𝗶𝘁𝗲 𝗪𝗶𝗻𝘃𝗲𝘀𝘁𝘆 𝗩𝗼𝘂𝘀 𝘀𝗲𝗿𝗲𝘇 𝗿𝗲𝗰𝗼𝗻𝘁𝗮𝗰𝘁𝗲́ 𝗽𝗮𝗿 𝘂𝗻 𝗺𝗲𝗺𝗯𝗿𝗲 𝗱𝗲 𝗻𝗼𝘁𝗿𝗲 𝗲́𝗾𝘂𝗶𝗽𝗲. Plus d’informations sur : www.winvesty.com #startup #leveedefonds #fundraising #venturecapital #finance #croissance #Winvesty

Say hello to Eleanor — EVA's new AI teammate who lives and breathes #Talent, #WorkforcePlanning, #Ops and #Finance. Her only job? Get you from “Hmm, interesting…” to “OMG, EVA.AI | Powering HR 4.0 is fantastic to save time and money” as fast as humanly (or artificially) possible 😃! In minutes she’ll: ➡️ Listen to your real-world headaches (slow hiring, messy mobility, runaway costs, headcount chaos) ➡️ Pinpoint the actual bottlenecks ➡️ Show you — clearly and honestly — which parts of EVA.ai will fix them ➡️ Hand you a next step that makes sense for your company, not some generic demo script She works hand-in-hand with our sales and customer success crew, so you’re never talking to a bot in a vacuum. Curious? Come meet Eleanor. 5 minutes, zero sales pressure, all upside. 👉 Chat with her here: https://lnkd.in/d7kMBXed P.S. She’s scarily good at this. You’ve been warned. P.P.S. While you’re chatting with Eleanor, you’ll quickly realise we’ve one of the most natural AI voice agents on the market right now. Want to beta-test this exact tech inside your own recruitment, HR business partnering, HR support, shared services or employee experience flows? DM me "I want in on the voice beta”.