Financial Analytics Services

Just when you thought AI was running out of industries to disrupt, Anthropic just released Claude for Financial Services… and suddenly, that $25K-a-year Bloomberg Terminal is looking a bit... vintage. Here's what makes it different: Claude can now combine external market data (from S&P/FactSet/Monginstar) with the firm's own internal data (Databricks/Snowflake) to answer questions like “How did our tech portfolio perform vs. the S&P 500 tech sector?” without analysts having to manually pull from multiple systems Need to run Monte Carlo simulations? Build proprietary trading models? Claude Code handles the heavy lifting, with expanded usage limits for those all-nighters before earnings calls. No more jerry-rigging APIs on your own. Claude comes with pre-built MCP connectors ready to plug into your existing financial data infrastructure. Expanded limits that bump analysts from regular Claude's 200K token limit (about 500 pages) to higher capacity thresholds and more queries per hour, so they can process massive 10-K filings and deal docs without hitting walls during market deadlines. Oh, and did we mention big names are already believers? Bridgewater estimates 20% productivity gains. Norway's sovereign wealth fund (NBIM) saved 213K hours. AIG compressed underwriting review time by 5x. Here’s one reason why: On the latest finance agent benchmark, Claude Sonnet 4 hit 44.5% accuracy on complex financial analyst tasks; neck and neck with OpenAI's o3. For context, most models scored below 30%. These aren't simple “What's Apple's stock price?” questions – we're talking multi-step SEC filing analysis that would make a first-year analyst sweat. Claude isn't alone in this financial AI arms race. Perplexity is another key player in the AI finance race, targeting individual analysts with three products: Perplexity Finance ($0): Free real-time stock prices, company analysis, and 13F comparisons…all wrapped in what they call a “delightful UI.” Enterprise for Financial Services ($40/month): FactSet integration and industry research. Teams save 10+ hours weekly per employee. One analyst said it summarized 48 hours of Q4 earnings work in 2 minutes. Perplexity Labs ($20/month): Custom dashboards and deep research reports in 10+ minutes. The best story? Stanley Druckenmiller used Perplexity to identify the top five Argentine ADRs, bought them all, and his positions have grown significantly And then there’s the crypto connection: Last week, Perplexity partnered with Coinbase to integrate real-time crypto data. Phase one launched with COIN50 index prices in Perplexity's Comet browser. Phase two connects queries directly to Coinbase trading. CEO Brian Armstrong's vision: crypto wallets fully integrated into AI models. He believes this will create “another 10x unlock” for AI.

Finance leaders today sit at the crossroads of complexity and opportunity. Reporting and analytics used to be about historical snapshots for closing the books, reconciling data, and delivering reports. But with automation and advanced analytics, the function is shifting from record-keeping to real-time decision enablement. Here’s where I see the biggest impact: Automated reporting means fewer manual reconciliations and fewer hours wasted on spreadsheet gymnastics. The result? Finance teams can close faster and redirect energy toward strategy. Real-time dashboards give CFOs and controllers the ability to monitor KPIs as they change, cash flow, margins, and working capital. Not weeks after the fact. AI-enabled analytics move beyond dashboards, spotting patterns, forecasting scenarios, and even prompting questions finance leaders should be asking. The key isn’t just adopting technology for the sake of it, it’s designing systems that give decision-makers confidence in the numbers. Automation reduces human error. Data integration eliminates silos. And analytics elevate finance from reporting on the past to actively shaping the future. In my experience, the finance teams that succeed with automation aren’t the ones that chase every new tool. They’re the ones that focus on building trustworthy, actionable insights that align with business strategy. #FinanceInnovation #Automation #DataAnalytics #CFOLeadership

AI just made its move into financial services. Anthropic announced a new tailored offering: Claude for Financial Services. Let’s break it down. • Claude connects directly to your internal data stack: Snowflake, Databricks, S&P, PitchBook, FactSet, and more. • It’s not a consumer chatbot. It’s a task-specific analyst, tuned for high-stakes environment. • It doesn’t train on your data. Privacy and compliance are foundational. • Oh yeah, and it can do Monte Carlo simulations. Where it creates value: • Investment teams can analyze portfolios, trends, and risk exposures in real time, without toggling across 12 dashboards or waiting on data prep. • Compliance and audit functions can use Claude to summarize regulatory updates, track adherence, and flag anomalies, before the next quarterly fire drill. • Client-facing teams can generate custom pitch decks, scenario models, and account insights on demand, without pulling an associate off a deliverable.    For CFOs • Increase visibility into financial drivers by asking natural-language questions across systems and models • Pressure-test scenarios in real time using up-to-date financial and macro inputs • Generate investor-ready insights faster and more consistently For FP&A Transformation leaders • Automate recurring analysis cycles such as forecast variance, budget rollups, and board package creation • Embed Claude into planning workflows to assist with driver modeling, commentary, and contextualization • Scale insight delivery without increasing headcount For GenAI Transformation leads • Operationalize AI within high-stakes workflows without reengineering existing systems • Launch proof-of-concepts with measurable productivity impact in under 90 days • Build a business case grounded in time saved, accuracy improved, and risk reduced Real results: • AIG accelerated underwriting by 80% while increasing data quality from 75% to 90% • Norway’s NBIM saved over 213,000 hours in a single deployment with a 20% productivity lift across finance teams If you’re leading a team inside a Fortune 500 and wondering where to start: Identify high-friction, high-repetition tasks in finance, ops, or risk. Don’t wait for a firm-wide transformation plan. Start small with one workflow Claude could automate or accelerate. Pilot. Measure. Expand. ----------------------- Follow me for GenAI Transformation, Training, and News.

CASE STUDY: A Finance Analytics Transformation by Paulo Jorge Ribeiro Summary: GlobalTech, a multinational electronics manufacturer, faced a financial management crisis due to slow reporting, data silos, and lack of predictive insight. The finance team was buried in spreadsheets, leading to errors and delays that hindered strategic decision-making. The company struggled with poor financial visibility, unexpected cost overruns, and missed revenue targets, forcing leadership to react rather than anticipate challenges. A turning point came when consecutive missed earnings triggered urgency for change. With a new CEO advocating for data-driven finance, GlobalTech embarked on a finance analytics transformation to modernize its approach, improve forecasting, and enhance decision-making capabilities. The company implemented a phased analytics strategy, integrating data systems, upgrading financial tools, and training staff. A pilot project in forecasting demonstrated immediate benefits, encouraging full-scale adoption of analytics across finance operations. The transformation led to faster reporting cycles, improved forecast accuracy, significant cost savings, and better cash flow management. By embedding analytics into decision-making and fostering a culture of data-driven insights, GlobalTech not only strengthened its financial position but also positioned itself for long-term competitiveness. Key Takeaways 📣 Leadership Buy-In is Critical: Senior executives must actively champion analytics initiatives to ensure alignment with strategic goals. 📣 Start with a Clear Problem and Quick Wins: Focus on specific financial pain points first to demonstrate early success and build momentum. 📣 Invest in Data, Tools, and People: Strong data infrastructure and skilled talent are essential for effective analytics implementation. 📣 Embed Analytics into Processes and Culture: Ensure analytics-driven decision-making becomes an integral part of business operations. 📣 Prepare for Challenges: Expect resistance to change, data integration hurdles, and technical glitches—proactive management is key. 📣 Measure and Communicate Success: Track improvements in efficiency, accuracy, and cost savings to validate the value of analytics. 📣 View Transformation as Ongoing: Continuously refine models, adopt new technologies, and adapt to evolving business needs. To read the entire article (lots more detail) click here: https://lnkd.in/d85F5XnD . Note, you must be a member to read it - AND - membership is free. Leave a comment and hit like/repost to share with your followers. --------------- ▪ Follow me 🚶♀️🚶🚶♀️🚶 for more insights ▪ Join 🤝 the PACE forum for discussion - (click the link in my profile) ▪ Click the 🔔 to get notified of new posts (top right of my profile) ▪ Subscribe 🖊 to my monthly newsletter #accountingandaccountants #article Awais Idris Lukas Sundahl, CMA, CSCA, MBA  Massimiliano Mauriello