AI in Financial Services

Really happy I had the opportunity to train around 100 of our #finance leaders at Kraft Heinz on how to use #AI for Finance! It was a fantastic opportunity to showcase how Generative AI can transform the way we work — from automation to analysis. We had representatives from functions like FP&A, supply chain finance, internal audit, tax, controllership and more! Some of the key topics we covered: ✅ Prompt engineering techniques ✅ Why context is everything when working with LLMs ✅ Practical tips like generating Python code for finance automation and deeper financial insights I truly believe AI can be a game-changer for Finance. Our function is uniquely positioned to champion this technology and lead its adoption across the organization. The potential benefits? 1) Faster analysis – Spend less time crunching numbers and more time interpreting them. AI accelerates data processing and helps surface key trends in seconds. 2) Smarter automation – Automate repetitive, manual tasks like reconciliations, reporting, and forecasting so teams can focus on strategic work. 3) Sharper insights – Use AI to dig deeper into financial data, spot anomalies, and uncover insights that might otherwise be missed. 4) Better decision-making – With real-time insights and predictive capabilities, Finance can help drive smarter, faster business decisions. 5) Increased efficiency – Free up valuable time and resources by integrating AI tools directly into daily workflows. Can’t wait to see how my colleagues continue to explore and innovate with these powerful technology!

Thinking about applying AI within your finance function? Heres three high-return areas to consider: ⚙ Automating Financial Close: AI can accelerate month-end and year-end closing by automating data extraction, reconciliation, and anomaly detection. This can halve closing timelines and reduce manual workloads by up to 70%, allowing teams to focus on strategic analysis rather than repetitive tasks. 📈 Enhancing Forecast Accuracy: Predictive AI models use historical and external data to provide more precise scenario analyses. They improve over time, boosting forecast accuracy by up to 60% and supporting faster, data-driven decisions 💡 Generating Strategic Insights: AI can analyse large volumes of structured and unstructured data, from social media to IoT sources, to reveal hidden risks and opportunities, aiding strategic planning Providers like BlackLine, Anaplan 'IBM Watson' deliver these solutions. However, beware - lots of AI features are rebranded versions of old tech. Automated reconciliations and advanced analytics, for example, are now marketed as “AI-powered” despite existing for years. True AI brings adaptive, real-time learning capabilities, so it’s crucial to assess whether solutions offer genuine innovation or just marketing hype What AI strategies have worked for your team? Please share your thoughts!

More than 400 US-listed companies valued over $1B disclosed AI-related risks in their SEC filings this year — a 46% jump from 2024. That’s not a trend line. That’s a warning signal. As AI becomes core to operations, decision-making, and customer engagement, regulators and investors expect documented, explainable, and accurate risk disclosures. Companies are realizing they cannot treat AI as an experimental add-on anymore — it's now a material business risk. 𝐖𝐡𝐚𝐭’𝐬 𝐃𝐫𝐢𝐯𝐢𝐧𝐠 𝐭𝐡𝐢𝐬 𝐒𝐩𝐢𝐤𝐞? AI is creating new, complex, and sometimes poorly understood sources of risk: ➡️ Bias or discriminatory outcomes ➡️ Hallucinated results that mislead decisions ➡️ Data-security vulnerabilities within AI pipelines ➡️ Opaque vendor models with unknown training data ➡️ Regulatory convergence (SEC + FTC + emerging state AI laws) Boards and executives are feeling pressure from all sides: regulators, shareholders, customers, and auditors — all asking the same question: 𝐋𝐞𝐠𝐚𝐥 𝐄𝐱𝐩𝐨𝐬𝐮𝐫𝐞 𝐢𝐬 𝐑𝐢𝐬𝐢𝐧𝐠 — 𝐅𝐚𝐬𝐭 𝘋𝘪𝘴𝘤𝘭𝘰𝘴𝘶𝘳𝘦 𝘢𝘯𝘥 𝘊𝘰𝘮𝘱𝘭𝘪𝘢𝘯𝘤𝘦 𝘙𝘪𝘴𝘬 SEC disclosures now require clarity about AI’s operational, cybersecurity, and accuracy risks. Inaccurate disclosures = enforcement exposure. 𝘐𝘯𝘷𝘦𝘴𝘵𝘰𝘳-𝘓𝘪𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘙𝘪𝘴𝘬 If an AI failure causes financial harm — and the risk wasn't adequately disclosed — securities litigation becomes a real possibility. 𝘊𝘰𝘯𝘵𝘳𝘢𝘤𝘵𝘶𝘢𝘭 𝘙𝘪𝘴𝘬 Vendor agreements behind AI systems must now include clauses on: • AI risk factors • Representations and warranties • Training data provenance • Model-change notice • Security and audit rights 𝘎𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 & 𝘈𝘶𝘥𝘪𝘵 𝘙𝘪𝘴𝘬 Boards must integrate AI into ERM, internal audit, and oversight. “We didn’t know” is no longer defensible. Regulators expect structured governance — logs, risk registers, assessments, and controls. 𝐓𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐀𝐈 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 This is where AI Governance programs make the difference between compliance and crisis. AI Governance helps organizations: 1️⃣ Map AI systems across the enterprise 2️⃣ Identify and assess material AI risks 3️⃣ Document controls, testing, and monitoring 4️⃣ Build disclosure-ready evidence for SEC filings 5️⃣ Update contracts and procurement to reflect AI reality 6️⃣ Implement accountability frameworks aligned with NIST AI RMF, ISO 42001, and state AI laws 7️⃣ Demonstrate transparent oversight to regulators and investors When AI risk becomes an SEC-level issue, AI Governance becomes a board-level responsibility. 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞 AI is now a generator of both opportunity and material legal exposure. Companies that implement strong AI Governance now will be the ones best prepared to meet regulatory expectations — and avoid the lawsuits, disclosure failures, and reputational damage accumulating around poorly governed AI. If your organization is integrating AI, now is the time to build the governance foundation.

Throwing AI tools at your team without a plan is like giving them a Ferrari without driving lessons. AI only drives impact if your workforce knows how to use it effectively. After: 1-defining objectives 2-assessing readiness 3-piloting use cases with a tiger team Step 4 is about empowering the broader team to leverage AI confidently. Boston Consulting Group (BCG) research and Gilbert’s Behavior Engineering Model show that high-impact AI adoption is 80% about people, 20% about tech. Here’s how to make that happen: 1️⃣ Environmental Supports: Build the Framework for Success -Clear Guidance: Define AI’s role in specific tasks. If a tool like Momentum.io automates data entry, outline how it frees up time for strategic activities. -Accessible Tools: Ensure AI tools are easy to use and well-integrated. For tools like ChatGPT create a prompt library so employees don’t have to start from scratch. -Recognition: Acknowledge team members who make measurable improvements with AI, like reducing response times or boosting engagement. Recognition fuels adoption. 2️⃣ Empower with Tiger Team Champions -Use Tiger/Pilot Team Champions: Leverage your pilot team members as champions who share workflows and real-world results. Their successes give others confidence and practical insights. -Role-Specific Training: Focus on high-impact skills for each role. Sales might use prompts for lead scoring, while support teams focus on customer inquiries. Keep it relevant and simple. -Match Tools to Skill Levels: For non-technical roles, choose tools with low-code interfaces or embedded automation. Keep adoption smooth by aligning with current abilities. 3️⃣ Continuous Feedback and Real-Time Learning -Pilot Insights: Apply findings from the pilot phase to refine processes and address any gaps. Updates based on tiger team feedback benefit the entire workforce. -Knowledge Hub: Create an evolving resource library with top prompts, troubleshooting guides, and FAQs. Let it grow as employees share tips and adjustments. -Peer Learning: Champions from the tiger team can host peer-led sessions to show AI’s real impact, making it more approachable. 4️⃣ Just in Time Enablement -On-Demand Help Channels: Offer immediate support options, like a Slack channel or help desk, to address issues as they arise. -Use AI to enable AI: Create customGPT that are task or job specific to lighten workload or learning brain load. Leverage NotebookLLM. -Troubleshooting Guide: Provide a quick-reference guide for common AI issues, empowering employees to solve small challenges independently. AI’s true power lies in your team’s ability to use it well. Step 4 is about support, practical training, and peer learning led by tiger team champions. By building confidence and competence, you’re creating an AI-enabled workforce ready to drive real impact. Step 5 coming next ;) Ps my next podcast guest, we talk about what happens when AI does a lot of what humans used to do… Stay tuned.

🚨 SEC just released its FY2026 examination priorities, and one theme runs through almost every section: AI is now a core supervisory focus, not a side note. Here’s what stands out for risk and compliance teams: 1. AI governance is now an exam expectation. Examiners will evaluate whether firms have meaningful policies, testing and oversight for AI technologies used in fraud prevention and detection, AML and back-office operational workflows, and whether AI-enabled automation is supervised with the same rigor as traditional processes. Reviews will also consider firm integration of regulatory technology to automate internal processes and optimize efficiencies. 2. Cyber reviews will probe AI-driven threats. SEC calls out the rise of polymorphic malware and other AI-enabled attacks. Firms should expect deeper reviews of access controls, incident response readiness, vendor dependencies and data-handling practices. 3. AML programs must show both rigor and oversight. Tailored, risk-based AML programs, independent testing, adequate customer identification programs (CIPs), SAR quality and OFAC monitoring remain key priorities. For teams using AI in monitoring or SAR drafting, human oversight will be a key exam point. From my perspective, AI in compliance is entering its accountability era. Regulators want transparency, controls and evidence that AI-powered automation is making risk management stronger, not introducing new uncertainty. Financial institutions adopting AI partners should calibrate to that expectation.

I just watched 80 AI agents work simultaneously on a single spreadsheet. Each pulling different data points. Revenue figures from SEC filings.  Credit ratings from Moody's.  Current ratios from balance sheets. All happening in parallel while I grabbed coffee. Normally, this would mean opening endless browser tabs, hunting through investor relations pages, copying numbers into spreadsheets. Instead, I used AI agents to automate this entire research. Then, used Gemini in Sheets to analyze the data. Here's the real insight: Working with spreadsheets is still complete slop. We've had ChatGPT for 3 years, yet most financial analysis still happens the old way.  You ask an AI a question, get a text response, then manually structure it yourself. That doesn't make sense for research like this. Some workflows need spreadsheet agents, not chat interfaces. So, I used this agentic spreadsheet tool, Ottogrid. Here's what I did: Created a table with 10 companies. Added columns for the financial metrics I needed. Instead of researching each cell manually, I selected the entire range and hit "Run cells." Ottogrid turned every empty cell into an AI agent: ↳ Agent 1: Find Apple's FY2024 revenue ↳ Agent 2: Get Apple's credit rating ↳ Agent 3: Calculate Apple's current ratio ↳ Agent 80: Find Intel's total debt All running simultaneously. All finding exactly what I specified. 2 minutes later: Complete financial analysis ready. Then I moved everything to Google Sheets and used Gemini to create Financial Health Scores and identify red flags across all companies. All without writing or even trying to remember a single spreadsheet formula. This isn't for massive datasets.  But if you can automate one routine research task that eats 2-3 hours of your day, the ROI is obvious. The professionals using AI agents for research definitely have an unfair advantage over those still doing everything manually. If you find this useful, Repost 🔁 to share it with your friends. I share practical AI implementations for finance professionals. To get started: 📩 Subscribe to Unwind AI for AI news, tools, and tutorials: https://lnkd.in/dunsQXDS ⭐️ Star the repo for opensource AI finance agents: https://lnkd.in/db2UynaZ ✅ Follow me for more such AI tools, news, workflows, and insights.

Today we released a complete guide to the best AI tools for finance teams A growing trend of modern finance teams are implementing AI native tools to improve the efficiency of their teams and work -- leading to better results, faster growth, and better operations But most of time the question I get asked is: "How can I get started using AI for my work" Some finance professionals have already tried using generic AI tools and seen performance issues combined with an inability to automate real processes. Others just don't quite yet know where to start. This guide will help. To create this guide we gathered data from our conversations with 100s of finance leaders who are using AI today in their finance teams and grouped them according to workflows they help automate -- specifically * AI FP&A Tools * AI FP&A tools enable strategic finance leaders to choose more automation without losing flexibility when reporting and analyzing financial data through AI. We highlight Concourse as the leading AI FP&A tool * AI Accounting Tools * AI accounting tools are helping reduce reconciliation work through automating the close process to reduce human errors and free up time for more strategic tasks. We highlight Numeric, Truewind, and Rillet as leading tools * AI Accounts Receivable Tools * AI AR tools analyze customer payment history to predict potential delinquencies and prioritize collection efforts in addition to helping teams track and invoice contract terms seamlessly. We highlight Maxio, Sequence, and JustPaid as leading tools * AI Accounts Payable Tools * AI AP tools are changing how businesses manage their invoices and payment flows through the automation of manual tasks like receipt capture, policy enforcement, and rich payment controls. We highlight Brex, Ramp, and Jeeves as leading tools * AI Treasury Tools * AI treasury tools leverage LLMs to analyze market liquidity trends, predict cash flow, and optimize financial management to help treasury teams stay on top of liquidity and capital needs. We highlight Nilus, and Finley as leading tools Full guide in the comments with use cases, customers, funding and more for every tool we highlight I hope this guide is a great starting point for finance teams who are looking to modernize their stack and tools that have native AI capabilities Happy to personally help make introductions to any of the companies listed in the guide as they have all built amazing products and companies If automating reporting and analysis of financial data with AI agents is top of mind please reach out to me or Concourse and we would love to give you a demo. All feedback is welcomed!

Harvard Business Review just found that executives using GenAI for stock forecasts made less accurate predictions. The study found that:  • Executives consulting ChatGPT raised their stock price estimates by ~$5.  • Those who discussed with peers lowered their estimates by ~$2.  • Both groups were too optimistic overall, but the AI group performed worse. Why? Because GenAI encourages overconfidence. Executives trusted its confident tone and detail-rich analysis, even though it lacked real-time context or intuition. In contrast, peer discussions injected caution and a healthy fear of being wrong. AI is a powerful resource. It can process massive amounts of data in seconds, spot patterns we’d otherwise miss, and automate manual workflows – freeing up finance teams to focus on strategic work. I don’t think the problem is AI. It’s how we use it. As finance leaders, it’s on us to ensure ourselves, and our teams, use it responsibly. When I was a finance leader, I always asked for the financial model alongside the board slides. It was important to dig in and review the work, understand key drivers and assumptions before sending the slides to the board. My advice is the same for finance leaders integrating AI into their day-to-day: lead with transparency and accountability. 𝟭/ 𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗮 𝘀𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿, 𝗻𝗼𝘁 𝗮𝗻 𝗼𝗿𝗮𝗰𝗹𝗲. AI should help you organize your thoughts and analyze data, not replace your reasoning. Ask it why it predicts what it does – and how it might be wrong. 𝟮/ 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝗔𝗜 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝘄𝗶𝘁𝗵 𝗵𝘂𝗺𝗮𝗻 𝗱𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻. AI is fast and thorough. Peers bring critical thinking, lived experience, and institutional knowledge. Use both to avoid blindspots. 𝟯/ 𝗧𝗿𝘂𝘀𝘁, 𝗯𝘂𝘁 𝘃𝗲𝗿𝗶𝗳𝘆. Treat AI like a member of your team. Have it create a first draft, but always check its work, add your own conclusions, and never delegate final judgment. 𝟰/ 𝗥𝗲𝘃𝗲𝗿𝘀𝗲 𝗿𝗼𝗹𝗲𝘀 - 𝘂𝘀𝗲 𝗶𝘁 𝘁𝗼 𝗰𝗵𝗲𝗰𝗸 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸. Use AI for what it does best: challenging assumptions, spotting patterns, and stress-testing your own conclusions – not dictating them. We provide extensive AI within Campfire – for automations and reporting, and in our conversational interface, Ember. But we believe that AI should amplify human judgment, not override it. That’s why in everything we build, you can see the underlying data and logic behind AI outputs. Trust comes from transparency, and from knowing final judgment always rests with you. How are you integrating AI into your finance workflows? Where has it helped vs where has it fallen short? Would love to hear in the comments 👇

The finance department, often deemed as one of the more mundane areas, is undergoing a remarkable transformation with the advent of AI technology. While initially met with skepticism, AI has now become a pivotal force in revolutionizing financial processes. AI-driven solutions are revolutionizing the field by enhancing speed, precision, and strategic insights. Here are some compelling real-world applications: - **Automated Reconciliation & Close**: Previously, tasks like reconciling transactions were laborious. Now, AI engines streamline this process by automatically matching transactions from the ERP, reducing manual effort significantly. Deloitte's research indicates that AI-driven reconciliation can slash manual processing by up to 80%, leading to substantial cost savings for large enterprises. - **Intelligent Invoice Processing and OCR**: Handling a high volume of invoices manually is daunting for many businesses. AI-powered OCR tools now facilitate swift and accurate invoice processing, enabling real-time expense categorization and cross-verification with purchase orders. Organizations leveraging AI-enabled OCR reportedly process invoices 2.4 times faster and at a 60% lower cost, as per APQC. - **Smart Vendor & Customer Management**: AI aids in managing suppliers and customers efficiently. By automating tasks like matching supplier invoices to internal records and predicting late payments for accounts receivable, AI enhances operational efficiency. Research by Gartner reveals that automated AR collections can reduce Days Sales Outstanding (DSO) by 10-20%, optimizing working capital utilization. The integration of AI in finance is reshaping traditional practices, offering unprecedented opportunities for improvement and innovation. It's truly a transformative era for the financial landscape! #AI #FinanceTransformation NAKAD Sambhav Jain Avinash Uttav Chinmaya Gawde Bikash Ranjan Mishra Akash Kejriwal Arun Yadav Raman S.

Finance teams are using AI automation to lead, not chase. Here's how to cut 30% of manual work with smart prompts. Smart finance teams are already slashing hours of manual effort with AI Agents + Purposeful Prompting. Repetitive reporting, reconciliations, or digging through data takes up a lot of time. That’s where AI steps in—automating routine tasks, reducing human error, and delivering insights in seconds, not days. Here’s what finance teams are getting with AI promts: → 30% less manual work across reporting, planning, and analysis → Fewer errors from copy-paste fatigue and spreadsheet overload → Instant summaries, variance explanations, and cash flow insights   → Faster decision cycles for FP&A, audits, and strategic pivots But the key to AI success is not just using AI—it’s knowing how to prompt it right. The best teams tailor Generic chat tools don’t cut it. Start by asking: - What’s draining the most time each month? - Where are accuracy or consistency issues costing us? - Which insights are always late or incomplete? Then bring in AI Agents trained, domain-specific, secure, and able to speak your business language. Caution: Not every AI solution is plug-and-play. Poorly designed prompts waste more time than they save. And security is a non-negotiable. Choose solutions built for finance-grade data protection. If you're leading a finance team and not already using AI Agents + strategic prompting, you’re loosing time, insights, and a competitive edge. Here are 7 examples of basic AI prompts for finance teams. _______________ Please share your thoughts in the comments. Follow me, Beverly Davisfor more finance insights. #Finance #Efficiency #FinanceTransformation #AI #AIAgents #PromptEngineering #FinanceLeadership #FinTech #OperationalExcellence #FinancialOperations #FinanceAutomation