AI in Financial Services

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

If I'd need to do an AI for FP&A and Finance Roadmap in 2025 this is what I'd do: 1: Know the Possibilities of AI in Finance AI is not just ChatGPT — and it’s not just about answering questions. It’s about transforming how Finance operates. But it’s hard to know what you don’t know. That’s why the first step is awareness. Get familiar with what’s actually possible. Here are just a few examples of how AI can be used in Finance: ✅ Automated variance analysis – AI detects anomalies, highlights drivers, and explains them in seconds. ✅Forecasting & scenario planning – Build predictive models that adapt in real-time. ✅Expense & invoice classification – Automate tedious reconciliations and improve audit readiness. Before building your roadmap, open the window to what’s possible. 2. Choose an implementation partner and tool I have this resource with 30+ AI tools for Finance below But if you want to keep it simple, My top suggestion is OpenAI and ChatGPT If your company just uses Microsoft products, then explore Copilot If your company just uses Google products, then explore Gemini 3. Get your team trained on that tool No matter what LLM and AI company you choose to partner with, I think this is one of the most important steps. Every tool has its features The more you know about them, the more you can do with AI for Finance Some examples: GPTs from OpenAI: A game changer, you can add your policies, files and data in minutes and you can create a chatbot for your entire company Colab AI Agent from Google: Have an AI finance data scientist at your disposal to explore how to find the main drivers of profitability or do scenario modeling Copilot in Excel with Python from Microsoft: This can unlock data insights in seconds. My point is that every tool has its secrets. And you can spend hours and hours learning them. But AI changes every day. So instead of trying to keep up, choose a learning partner and get your team trained on use cases of AI in Finance. If you need help with that, let me know and I can give you suggestions Some options: AI Finance Club Self Paced Courses LinkedIn Learning Courses 4. Prioritise use cases Use my framework in the pdf Focus on Quick Wins and Major Projects Keep some Fill Ins ready Avoid Thankless Tasks 5. Create a Governance & Compliance Plan AI is powerful—but it needs guardrails. Define what data can and can’t be used Set standards for review and oversight This ensures your AI efforts are safe, ethical, and scalable. 6: Track and Share Wins Start small, but celebrate results. Did an AI tool reduce reporting time by 50%? Did automation save your analysts 10 hours a week? Share it with the team and leadership. Build an Internal prompt Library and start documenting every AI idea or request that comes up—big or small. Momentum builds when people see the value. If you need the full version of this guide (20+ pages), comment and I'll send!

𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝗼𝘂𝘁𝗹𝗶𝗻𝗲𝗱 𝟲 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗙𝗣&𝗔 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗯𝗲𝘁𝘁𝗲𝗿 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴. Most finance teams know them. Few actually implement them consistently. Why? Because doing it right has always been painfully manual. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘀𝘁𝗿𝘂𝗰𝗸 𝗺𝗲: AI is changing this. Fast. The six practices McKinsey recommends are now achievable at scale: • 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘁𝘆-𝘄𝗲𝗶𝗴𝗵𝘁𝗲𝗱 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 – AI can run hundreds of scenarios and assign P values automatically, not just the three you had time to build manually. • 𝗧𝗿𝘂𝗲 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺 𝗰𝗮𝘀𝗲𝘀 – AI separates baseline trends from management initiatives without the spreadsheet gymnastics. • 𝗕𝗲𝗮𝗿 𝗰𝗮𝘀𝗲 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 – AI identifies downside risks and models them before you're blindsided. • 𝗖𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗺𝗮𝗰𝗿𝗼 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀 – AI flags when one business unit uses different GDP assumptions than another. • 𝗗𝗶𝘀𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲𝗱 𝗶𝗻𝗳𝗹𝗮𝘁𝗶𝗼𝗻 – AI tracks the specific components that actually affect your business, not just CPI averages. • 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗯𝗮𝗰𝗸 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 – AI compares forecasts to actuals weekly and learns from variances automatically. 𝗧𝗵𝗲 𝗯𝗿𝘂𝘁𝗮𝗹 𝘁𝗿𝘂𝘁𝗵: Human bias has always been the weak link in forecasting. Optimism creeps in. Assumptions go unchallenged. P-values are applied inconsistently across business units. AI doesn't have a political agenda. It doesn't inflate projections to look good in front of the board. It just processes data. The result? Faster forecasts. More accurate projections. And decisions based on reality, not hope. 𝗠𝘆 𝗮𝗱𝘃𝗶𝗰𝗲? 𝟭. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗯𝗮𝗰𝗸 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 Use AI to compare your forecasts over the last 12 months with actuals. Find where bias lives in your models. 𝟮. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 Stop building three scenarios manually. Let AI generate probability-weighted ranges based on actual data patterns. 𝟯. 𝗘𝗻𝗳𝗼𝗿𝗰𝗲 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝘆 Use AI to flag when macro assumptions differ across business units. Inconsistency kills forecast accuracy. Because here's what separates finance teams that drive decisions from those that just report numbers: They use AI to remove bias and deliver forecasts that leadership can actually trust. 𝗦𝗼 𝗯𝗲 𝗵𝗼𝗻𝗲𝘀𝘁: Which of these six practices is your biggest gap right now? ---------- 🧑💼 I'm a partner at Business Partnering Institute 🤝 We help increase the influence of your finance team 🔔 To see more content, hit the bell on my profile 📘 Order our new book now: https://bit.ly/4h2P9AA 🧑🎓 Enroll in our LinkedIn course: https://bit.ly/4a5fB9l 📻 #FinanceMaster podcast: https://bit.ly/3NLSt73 📺 Follow us on YouTube: https://bit.ly/4bSBut6 📢 Join our WhatsApp channel: https://bit.ly/3WWGOrc 📄 Check out all our templates and cheat sheets here: https://lnkd.in/eC_zuCU4

AI Risk Is Becoming Uninsurable. Contracts Are Taking the Hit. Insurance has been quietly stepping away from meaningful AI coverage. Exclusions are expanding, sublimits are shrinking, and underwriting is getting tighter. Companies are still deploying AI at full speed, and the gap has to land somewhere. It is landing in contracts. Read the full article: https://lnkd.in/gRHtVEmp I wrote about this for Corporate Counsel because the shift is real and accelerating. We are watching contracts absorb functions that insurance used to perform. That change reshapes how indemnities work, how governance is drafted, and how responsibility is allocated across the AI lifecycle. Indemnities are narrowing. Broad, catch-all promises are being replaced by precise and limited obligations. The protection that many clients think they are getting often does not exist anymore. Governance obligations are expanding. They are moving upstream into how the system is built, validated, monitored, and supervised. Documentation and controls now influence liability in a way many teams have not expected. And, shared responsibility frameworks are becoming the norm because AI risk sits at the intersection of model behavior and human decisions. This is a structural shift. Contracts are functioning as underwriting instruments because the traditional backstop is pulling away. When the safety net is gone, the contract becomes the risk architecture. If you support procurement, sales, data partnerships, or AI deployments, this matters. Boilerplate AI language is no longer neutral. Internal processes now influence exposure. Many executives still assume their insurance covers AI-related risk when it does not. That disconnect shows up in negotiations every day. The article goes deeper into how these trends are playing out in real agreements and what in-house teams can do to respond with clarity and control. For more insights, check out the Contract Trust Report: https://lnkd.in/gJdXkUpJ — Olga V. Mack I build legal systems for real life.

Tech Stack of an AI Research Agent: The complete architecture that powers intelligent research automation. Building effective AI research agents requires more than just selecting a good LLM. The real challenge is coordinating multiple specialized components that work together smoothly to deliver accurate and thorough research results. Here's the essential tech stack breakdown: 🔹 1. LLM Backbone drives the core intelligence : GPT-4o excels at multimodal tasks and summarization. Claude 3 handles long-context document analysis very well. Mistral or Llama 3 offer open-source flexibility when you need full control over your deployment. 🔹 2. Memory and Context Management prevent information loss : LangChain or LlamaIndex manage context and effectively handle document chunks. Vector databases like Pinecone, Weaviate, or Chroma store embeddings and allow for semantic search across large document collections. 🔹 3. Web Browsing and Retrieval capabilities gather live information : Search APIs such as Serper, Brave Search, and Bing fetch reliable real-time results. Browser automation tools like Selenium or Playwright scrape dynamic content when static APIs fall short. 🔹 4. Tool Abstractions and Agents coordinate complex workflows : AutoGen enables collaboration among multiple agents. CrewAI provides role-based organization for task-specific responsibilities. LangGraph manages stateful workflows between agents. 🔹 5. Task Routing and Planning handle smart decision-making : Function calling via OpenAI or Claude APIs manages tool selection. ReAct or AutoGPT-style planners support iterative search, analysis, and synthesis processes. 🔹 6. Document Understanding extracts structured information : PDF parsers like Unstructured.io handle content extraction. OCR tools like Tesseract process scanned documents and images. 🔹 7. Output Generation creates professional deliverables : Notion API or Google Docs API generate formatted reports. Whimsical API and Mermaid.js create diagrams and visual summaries. The sample flow showcases the complete cycle: query processing, task breakdown, web search, document parsing, vector storage, summarization, source citation, and final output generation. Success comes from choosing components that integrate well, not just relying on individual tool capabilities. #aiagent

Multi-agent AI systems can produce superior results in financial investment research and decision-making. New research uncovers a number of specific insights on the best configurations. A study introduces a multi-agent system with flexible group sizes and diverse collaboration structures that adapts to market conditions and investment scenarios. This identifies optimal architectures for different sub-tasks. Insights from the study include: 🌟 Multi-Agent vs. Single-Agent Performance: The study highlights that while single agents handle straightforward tasks like fundamental analysis well, multi-agent systems excel in complex tasks like risk analysis, underscoring the need to align agent configurations with task complexity. 🤝 Flexible Collaboration Structures: The research explores horizontal (peer), vertical (leader-led), and hybrid collaboration models. Horizontal and hybrid structures perform best in data-gathering tasks, while vertical structures enhance complex, judgment-heavy tasks. 🔄 Ensemble Structure for Superior Predictions: An ensemble approach combining different agent types improves investment decision-making accuracy, achieving a notable 66.7% accuracy in buy/sell predictions and more precise target stock prices. 📊 Task-Specific Agent Configuration: In fundamentals analysis, single agents show strong results by using straightforward metrics, while multi-agent groups handle market sentiment's variable insights and excel in the layered complexity of risk analysis through diverse agent perspectives. 🧑🏫 Insights on Leadership Roles: Leader-led (vertical) structures are essential in complex, analytical tasks like risk assessment, as leaders synthesize diverse inputs and maintain coherence, producing more focused, reliable reports by filtering and integrating subordinate contributions. An important next step in this research is to generate Humans + AI multi-agent architectures, identifying the optimal roles for humans and AI agents in the system, as well as the configurations that best suit specific financial investment tasks. Link to paper in comments.

90% of finance teams still think AI means “just ChatGPT.” So why is that your only priority? AI tools already determine speed, accuracy, and cost savings in finance. Yet most businesses ignore Pigment, Ramp, and Causal... That's why I mapped this AI stack for finance pros to stay ahead. Steal it to maximize leverage 👇 🔎 Generative AI ↳ Summarises reports + policies. Use cases: • Policy documentation • Answering finance queries • Drafting accounting memos    KPIs: • Reduced analyst workload • Hours saved per report • Faster reporting cycles    📊 AI for Forecasting ↳ Predicts revenue, cash flow, and costs. Use cases: • Scenario planning • Expansion modeling • Real-time cash tracking    KPIs: • Forecast accuracy % • Variance vs. actuals • Cash visibility    📈 AI for FP&A ↳ Scenario + driver-based models. Use cases: • Budget vs. actuals • Sensitivity analysis • Driver-based forecasting    KPIs: • Accuracy of NRR / CAC • Forecast cycle speed • Scenario coverage    💳 AP & AR Automation ↳ Invoice capture + payments. Use cases: • Vendor payments • Bank reconciliation • Automated reminders    KPIs: • DSO (days sales outstanding) • Payment errors reduced • Collections speed    💼 Expense Management ↳ Policy enforcement + parsing. Use cases: • Fraud prevention • Receipt scanning • Category automation    KPIs: • Reimbursement time • Expense accuracy • Policy adherence Adopting AI isn’t optional. But it doesn’t have to be overwhelming. That’s why I'm helping SaaS founders & operators. Join 5k folks at: thestartupfinance.com PS. Are you already using AI in your finance stack?

5 steps: how to use AI for personal finance? (A structured, economics-aligned, data-informed approach) AI can provide clarity, forecasting, and discipline for your finances, as long as you don't compromise your data safety. ONE Rule: Do NOT use free versions of any AI tool for these. Step 1. Upload 3 Months of Statements to AI tool: - Checking, savings, credit cards.  - AI captures your current financial patterns from these. Step 2. Ask AI to categorize your spending & income to classify: - recurring vs. one‑off expenses   - fixed vs. variable costs you incur  - income sources and volatility   - consistent or irregular patterns   This part usually feels tedious to do manually, but AI removes the friction. Step 3. Ask AI to generate a Predictive Spreadsheet to: - project next month’s spending   - estimate cash flow over next month  - show best‑case, worst‑case, and normal scenarios - include formulas to allow adjustments   This turns your raw data and patterns into a living financial model. Step 4. Ask AI to identify and classify recurring costs: - subscriptions you forgot about, overlapping services (e.g., Netflix, Hulu)   - recurring charges that add up (e.g., credit card interest) - multiple food delivery services, duplicate software tools - repeated impulse categories (e.g. "treat yourself splurges)  This is where most people leak money without noticing. Step 5. As AI to build you a One‑Year Plan: - extrapolate your current habits   - calculate probability of hitting a savings target   - show what changes are required to reach Point X   - quantify tradeoffs, adjustments, and goals  - simulate alternative scenarios  You will end up with a plan that is data‑informed, personalized, probabilistic, and grounded in your actual behavior. Not a generic “budgeting tip list.” However, AI is not a financial advisor. There are risks and opportunities. The risks: - sharing sensitive data with free models   - assuming AI’s projections are guarantees   - relying on AI to make your decisions (hint: Don't!)  - treating AI as a shortcut instead of a tool for clarity The opportunities: - instant categorization and analysis   - faster forecasting than most humans can manually do   - visibility into spending patterns you might overlook   - the ability to model financial outcomes without guesswork  AI collapses the cost of analysis, not the need for discipline or judgment. Two BONUS uses for AI in Personal Finance: 1️⃣ Use AI as a Behavioral Mirror: AI can surface behavioral patterns like impulse buys, emotional spending, end‑of‑month spikes. Check if they are derailing your goals. 2️⃣ Use AI for “What‑If” Scenarios:    - “What if I move to a cheaper apartment?”   - “What if I pay off this debt first?”   - “What if I save 10% instead of 5%?”   This turns abstract financial advice into concrete, personalized math. - Dr. Kruti Lehenbauer of Analytics TX, LLC P.S.: Hope this #FinancialLiteracyMonth #datascience #economics #AI insight helps you!

The Canaries are Fleeing the Coal Mine. While Silicon Valley evangelists are busy hyping the trillion-dollar productivity boom of Generative AI, a much quieter, more pragmatic group of people is heading for the exit - the insurers. The Financial Times revealed this week that several major insurers are beginning to retreat from offering cover for certain AI-related risks. They are spooked by the potential for very large, even multibillion-dollar claims: from IP and copyright disputes to discrimination cases and damages caused by AI “hallucinations”. We should pay very close attention to this. Why? Because actuaries are the ultimate realists. They don’t deal in “vision” or “disruption” or the utopian promises of a post-work future. They deal in cold, hard, quantifiable risk. And right now, they are looking at the Generative AI landscape and, in effect, saying: we cannot yet model this with confidence. The insurance industry’s hesitation signals three critical things we must not ignore: 1. The “Black Box” Problem is Systemic If even professional risk-takers struggle to quantify the chances of an AI hallucinating and causing reputational, financial or physical damage, that suggests these systems are not yet reliable enough to be deeply embedded in critical infrastructure and essential services. 2. The Litigation Wave is Building The retreat from cover suggests that the industry is bracing for a wave of complex litigation – from IP and copyright disputes to defamation and fraud cases – with potentially very large, systemic exposures. We don’t yet know whether AI liability will become this decade’s asbestos or tobacco in terms of scale and complexity, but the direction of travel is clear enough for insurers to start limiting their own exposure. 3. Efficiency vs. Resilience We are rushing towards efficiency without properly pricing in the cost of resilience. The drive to automate, optimise and cut costs too often ignores a simple question: who absorbs the shock when highly networked AI systems fail at scale? We have seen this movie before. In the run-up to the 2008 financial crisis, complex derivatives were sold as miracle products that dispersed risk – until they suddenly amplified it. When the people whose entire business model is based on calculating risk start treating parts of a technology as too risky to insure for now, it is time to pause the hype cycle and take a hard look at the safety rails. Innovation is essential. But “move fast and break things” is a dangerous strategy, especially when we still don’t know who is going to pay for the breakage. What’s your take? Is this a temporary blip as the market adjusts – or an early warning sign of a systemic bubble? #AI #RiskManagement #Economics #TechPolicy #FutureOfWork

2026 is quietly being called the “year of Agentic AI” in banking and finance. Until now, AI in banks mostly meant chatbots, dashboards, or automation projects stuck in pilot mode. Now the conversation is changing.  Large banks and consulting firms are actively working on 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬, 𝐭𝐫𝐢𝐠𝐠𝐞𝐫 𝐚𝐜𝐭𝐢𝐨𝐧𝐬, 𝐚𝐧𝐝 𝐚𝐬𝐬𝐢𝐬𝐭 𝐭𝐞𝐚𝐦𝐬 𝐜𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬𝐥𝐲, rather than just generating responses. Oracle launched new agentic AI capabilities for banking platforms in early February. It enables banks to automate workflows across onboarding, service operations, compliance checks, and process monitoring. At the same time, global consulting firms like 𝐀𝐜𝐜𝐞𝐧𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐂𝐨𝐠𝐧𝐢𝐳𝐚𝐧𝐭 are pushing enterprise-wide adoption programs where AI agents support operations, customer servicing, and internal workflows. The interesting shift? Finance and FP&A are next in line. Because, changes are visible across: 1. Variance analysis that runs automatically every day instead of after month close. 2. Forecasts which adjust continuously as sales or cost signals change. 3. Scenario models which run automatically when assumptions move. 4. Liquidity or working-capital risks that get flagged early, not after reporting. Finance teams spend huge time assembling numbers.  Agentic AI changes the timing by changing outcomes. However, finance leaders need discipline here. They need to ask 3 important questions: 𝟏. 𝐃𝐨𝐞𝐬 𝐀𝐈 𝐢𝐦𝐩𝐫𝐨𝐯𝐞 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐨𝐫 𝐣𝐮𝐬𝐭 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐭𝐚𝐬𝐤𝐬? Headcount savings alone won’t justify investment. Better capital allocation and faster decisions will. 𝟐. 𝐖𝐡𝐨 𝐨𝐰𝐧𝐬 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 𝐰𝐡𝐞𝐧 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐜𝐭? Governance, auditability, and human oversight must be built in from day one. 𝟑. 𝐂𝐚𝐧 𝐀𝐈 𝐜𝐨𝐬𝐭𝐬 𝐬𝐩𝐢𝐫𝐚𝐥 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐜𝐨𝐧𝐭𝐫𝐨𝐥𝐬? Cloud AI usage without discipline can quietly inflate tech budgets. Agentic AI won’t replace finance teams. But finance teams using agentic AI may replace those who don’t. #AgenticAI #AIinBanking #FinancialServices #DigitalTransformation