AI Finance Club

ChatGPT User Guide Get better at using ChatGPT with this one-pager! Get the High Resolution for free: https://lnkd.in/d4-4fHeP Here is what is inside: • 11 Hidden Functionalities • 10 Tips for Troubleshooting • How to set up your privacy • How to create your account • How to navigate the interface • 4 Advanced Features of GPT4 • ChatGPT 3.5 vs 4 Comparison • 10 Advanced Prompting Techniques • 6 Prompting Helps for Basic Prompting

Python in Excel for financial data visualization Follow Christian M., follow him if you want to learn more about Python in Excel. ---------- Here's the original post: You can now use Python in Excel for financial data visualization It enables new capabilities like creating heatmaps in seconds! How to enable it: https://lnkd.in/e_a-9RCu Once you have it, you and your FP&A teams can build these advanced data visualizations. I created an Excel file with code to help you build and customize heatmaps for finance. Comment Python in Excel or message me and I can send it to you! A heatmap is a data visualization where individual values are represented as colors. In hashtag #finance, a heatmap can help you quickly understand correlations between different metrics like Revenue, Expenses, Profit, and more. Each cell color shows the strength and direction of the correlation, helping you identify patterns or anomalies at a glance. You can use heatmaps for: ✅ Simplified Data Interpretation: Large financial datasets are easier to interpret when visualized as color-coded relationships. ✅Quick Identification of Trends: Heatmaps enable teams to see strong relationships, or potential risks, instantly. ✅Data-Driven Insights: By focusing on correlations, FP&A teams can prioritize metrics that have the strongest financial impact. ------------- Follow AI Finance Club to continue learning about AI for finance.

AI Explained to Kids PS. Join the AI Finance Club 5-day trial and explore some of AI Finance Club's most valuable members-only content: https://lnkd.in/euPYzZmk AI is everywhere in our lives and in our work. Especially, we, the finance professionals, know the importance of it. But have you tried to explain to your kid? Here is the simplest explanation you will find: 1. Definition AI makes computers smart like characters in Toy Story, WALL-E, or R2-D2. It enables toys to learn and play with you, mimicking human-like intelligence. 2. History AI's journey started in the 1950s, evolving from basic games to intricate roles in cars, phones, and medicine, advancing daily. 3. How to Use AI AI acts as a magical aide, sourcing games, aiding homework like a tutor, and playing music, akin to magic in Harry Potter, enhancing real-world tasks. 4. Practical Example AI powers video games, adding challenge and fun like Toy Story characters. It acts as a storyteller and detective, enhancing school projects with music and narratives. 5. Pro Tips • AI-powered games mimic player moves like magical chess. • Smart devices akin to curious elves learning new tricks. • Share AI experiences to showcase its adaptive learning to friends and family. • Let AI be your daily guide, unveiling exciting facts and activities. 6. Key Takeaways • AI is likened to a storybook companion, continually learning. • Gadget evolves into clever helpers, like characters in a fairy tale. • It transforms gadgets into intelligent aides, evolving like fairy tale characters.

Explicit Reasoning What is it? "Explicit Reasoning" is a prompting technique where AI details its process or reasoning in a clear, step-by-step manner. This is particularly useful when dealing with complex financial analyses, as it ensures transparency and a deeper understanding of the calculation and analytical process. This is a way to force the model to act like a spreadsheet. And it reduces the probability of having mistakes in the reasoning & calculations. How to Use Explicit Reasoning? Here is a prompt example for the Liquidity KPIs Calculation: "Using the following data, calculate the three most important liquidity KPIs for our company. Please provide a step-by-step explanation of each calculation. Assume our current assets are $500,000, inventory is $150,000, current liabilities are $250,000, and cash & cash equivalents are $200,000." Expected Outcome for the Current Ratio Calculation 1. Define the Current Ratio: "Current Ratio is calculated by dividing current assets by current liabilities." 2. Insert Data: "Current assets are $500,000, and current liabilities are $250,000." 3. Calculate: "Current Ratio = Current Assets / Current Liabilities = $500,000 / $250,000." 4. Result: "The Current Ratio is 2.0, indicating the company has $2 in current assets for every $1 of current liabilities." ChatGPT's Output: 1. Current Ratio The Current Ratio measures a company’s ability to cover its short-term liabilities with its short-term assets. Formula: Current Ratio = Current Assets / Current Liabilities In your case: Current Assets = $500,000 Current Liabilities = $250,000 Current Ratio = $500,000 / $250,000 = 2 So, the current ratio for your company is 2, which means that you have twice as many current assets as current liabilities. A current ratio above 1 indicates good liquidity. 2. Quick Ratio (Acid-Test Ratio) The Quick Ratio is a more conservative measure of liquidity that excludes inventory from current assets, as inventory can sometimes be difficult to convert to cash quickly. Formula: Quick Ratio = (Current Assets - Inventory) / Current Liabilities In your case: Current Assets = $500,000 Inventory = $150,000 Current Liabilities = $250,000 Quick Ratio = ($500,000 - $150,000) / $250,000 = $350,000 / $250,000 = 1.4 3. Cash Ratio The Cash Ratio is the most conservative measure of liquidity, as it only considers cash and cash equivalents as current assets. Cash Ratio = (Cash & Cash Equivalents) / Current Liabilities In your case: Cash & Cash Equivalents = $200,000 Current Liabilities = $250,000 Cash Ratio = $200,000 / $250,000 = 0.8 The cash ratio shows how many dollars in cash and cash equivalents you have to cover every dollar of current liabilities. A cash ratio of 0.8 indicates that you have $0.80 in cash for every $1 in current liabilities. A cash ratio of less than 1 may be concerning, especially if your business doesn’t keep a lot of inventory.

AI Integration Checklist PS. Join the AI Finance Club 5-day trial and explore some of AI Finance Club's most valuable members-only content: https://lnkd.in/euPYzZmk Become a master of integrating your data with AI Here is how you can enhance your skills from basic level to mastery: Basic (Moving from level 1 to 2) Developing Competence • Automate Error Checking: Utilize Excel's data validation for errors. • Standardize Data Entry: Create SOPs, templates, dropdown lists. • Educate Team Members: Train on data quality importance. • Initiate Data Integration: Start small-scale projects with ETL tools. Intermediate (moving from level 2 to 3) Strategic Integration • Implement Real-Time Validation: Set data validation rules. • Employ Master Data Management: Use MDM systems like Snowflake. • Assign Data Owners: Designate data segment owners. • Develop Feedback Loops: Establish consumer-producer feedback. Advanced (moving from level 3 to 4) Advanced Application • Predictive Data Quality Monitoring: Use statistical models and AI. • Leverage Predictive Analytics: Apply predictive analytics. • Utilize Microsoft Azure: Access AutoML for AI implementation. • Implement Comprehensive Tracking: Use tools for lifecycle visibility. Mastery (moving from level 4 to 5) Visionary Leadership • Data Governance Framework: Establish policies and standards. • Integrate Systems: Overcome data silos with unified management. • Adopt Centralized Data Management: Employ advanced solutions like Snowflake.

ChatGPT Desktop App for Finance Credits to Christian M.; follow him for more valuable content on AI for finance. ------------ Here's the original post: OpenAI released a ChatGPT Desktop App for Windows. I created this guide for FP&A and hashtag #finance teams on how to use it. It includes step-by-step guides for: ✅ Scenario Planning and “What-If” Analysis ✅ Improving Financial Reporting ✅ Generating forecasts with Machine Learning ✅ Preparing executive summaries --------- Follow the AI Finance Club to continue learning about AI for Finance.

Get Your Time Back PS. Join the AI Finance Club 5-day trial and explore some of AI Finance Club's most valuable members-only content: https://lnkd.in/euPYzZmk A mini-course on effective prompting Here are the prompting techniques you must learn: Basic Prompting Start with the fundamentals of prompting. Use precise language, include contextual information, employ creative phrasing, and use analogies & examples to clarify your requests. Chain-of-Thoughts Break down complex queries into simpler steps: • Level 1: Breakdown of the main problem • Level 2: Sub-problems breakdown • Level 3: Ask for specific tasks This approach allows the AI to focus on each part systematically for better outcomes. Chunking Master the art of breaking complex information into smaller, manageable parts. Apply this technique to areas like: • Financial analysis • Research • Tools • Problem-solving • Writing Explicit Reasoning Explicit Reasoning is an AI technique that explains its process in a clear, step-by-step manner This ensures transparency and accuracy in responses. Agent Prompting Agent Prompting involves presenting prompts as tasks or queries. Assign distinct tasks or queries to AI "agents" within a framework to tackle different aspects of your workload. Great for dividing and conquering large tasks efficiently. Team Prompting Make AI work like a team of experts. Create distinct roles with specific tasks for hypothetical agents within the AI. Steps include: 1. Define the agents and their roles 2. Assign tasks & sequence 3. Facilitate collaboration for cohesive results Socratic Prompting Dive deep into a topic by asking critical questions and uncovering assumptions: • Level 1: Ask open-ended questions • Level 2: Challenge assumptions • Level 3: Seek clarifications This encourages AI to analyze problems from multiple angles. Meta-Cognition Encourage the AI to reflect on its own thought process, biases, and decision-making. Use this for: • Self-reflection • Evaluation and rating of responses • Leveraging insights • Requesting adjustments based on its performance Prompt Optimization & Expansion Start with a basic prompt and continuously improve it for better results. This technique helps you discover the right words and sentence structures to use, ensuring optimal output. Fact-Checking Use prompts to verify the accuracy and credibility of information by: 1. Questioning the source 2. Cross-verifying facts 3. Asking for recent data 4. Requesting third-party links for reference Iterative Inquiry & Sequential Questioning Ask questions in a sequence to gradually refine and improve the understanding or output of a task. This ongoing process sharpens the accuracy and depth of AI responses over time. Want the full course? Head over to AI Finance Club https://ai-finance.club/ or scan the QR code to get started. There's an ongoing 5-day free trial; try it now!

How can FP&A Teams and CFOs use AI Agents? Credits to Christian M.; follow him for more valuable content on AI for finance. ---------- Here's the original post: How can FP&A Teams and CFOs use AI Agents? First, what are AI Agents? Think of AI agents as financial co-pilots in your finance teams. Just like an experienced co-pilot assists the captain by managing routine tasks, analyzing flight data, and alerting the pilot to turbulence ahead, hashtag #AI agents can and will do the same for your hashtag #finance and FP&A teams. They can handle the repetitive work like merging Excels, creating baseline forecasts and doing data visualizations and reporting for you. I believe AI Agents will be one of the future tools of finance teams. They can reduce manual effort, boost accuracy, and provide a strategic view of you and your teams. What are some use cases? ✅ Financial Forecasting & Predictive Analysis: AI Agents can analyze historical data and market trends to create precise forecasts for revenue, expenses, and cash flow. ✅Scenario Modelling and Sensitivity Analysis: You can build an AI agent that simulates your company's financial future thousands of times under different conditions to anticipate risks and plan strategically. ✅ Budgeting and Planning Automation: You can use them to auto-generate budgets based on past performance and strategic goals, minimizing manual input and errors. Then a human can review! ✅ Variance Analysis and Anomaly Detection: AI Agents can compare actuals vs. forecasts, pinpoint anomalies, and flag unusual patterns for review. More details and examples here: https://lnkd.in/exmFHzT5 How to test them? So far, the easiest way to test I have found is via Replit. I did an article one month ago on it but the more I use it, the more I think is one of the best tools in the AI Agent landscape. This is the article: https://lnkd.in/eRPYnTGA Also, I found this image with other AI Agents websites, I am starting to explore them and will write more on other AI Agents start-ups but want to share the image for you her -------- Follow AI Finance Club to get the best insights on the AI advancements for finance.

If AI can help accounting, the whole finance function, as well as the whole company, will benefit from it. Main Challenges Faced by Accounting • Manual processing of invoices and receipts Problem: prone to errors and inefficiencies. • Time-consuming bank reconciliations Problem: Due to a high volume of transactions & different types of bank statement formats. • High amount of manual entry of journal entries Problem: Increasing the risk of inaccuracies but also time spent on low value activities. Let's start with the invoicing. Where can AI help? The solution: digitize and automate your Invoice Processing. AI, particularly OCR technology, can automatically extract data from invoices and receipts. OCR means Optical Character Recognition and is a technology that converts images of typed, handwritten, or printed text into machine-readable text. Then it will use NLP (Natural Language Processing) to understand the information read from the invoice and contextualize it. NLP is a technology in AI that enables computers to understand our human language. Which tool to use? Azure Cognitive Services for Invoice Processing If you are already in the Microsoft Azure environment, you can use the Azure Form Recognizer module. Form recognizer is a component of Azure Cognitive Services that uses OCR and machine learning to extract text, key-value pairs, and tables from documents. How does it help: Automates the extraction of data from invoices and receipts, significantly reducing manual processing time and increasing accuracy. Practical Implementation: Accounts payable teams can integrate Azure Form Recognizer into their workflow to automatically process incoming invoices, extract relevant data (such as vendor name, invoice number, and amount), and populate this data into their financial systems without manual input using an API. The second area where you can use AI to improve your work is reconciliations. Here are two ways you can do it: The first option is to build your own mini-algorithm. The 5 steps: 1. Get the data from your bank and your system using digital files (get CSV or Text files if you can). 2. Clean this data using rules (formatting of date, removing columns, combining files, enriching data). 3. Create your matching rules using an algorithm 4. Analyze exceptions 5. Act on exceptions: either book the transaction in your system or ask the bank to correct it (sometimes it happens that the bank makes a mistake). Second Option: Off-the-shelf solutions I found some solutions that automate reconciliations. I haven't tested them yet, so this is to take with a pinch of salt. The third area where you can use AI to improve your work is Automation of Journal Entries There are still areas where accounting has to book many entries like: • Revenue recognition • Accruals • Tax booking For this, I have found three innovative accounting systems that start-ups are using and which are leveraging AI: • Puzzle • Truewind • Booke.ai

How to use AI for Financial Analysis? Credits to Christian M.; follow him for more valuable content on AI for finance. ----------- Here's the original post: How to use AI for Financial Analysis? Below you can see a step-by-step guide: hashtag #AI is transforming crucial tasks in hashtag #Finance such as budgeting, forecasting, financial analysis, and corporate performance management. AI guided experiences can provide the ability to analyze vast amounts of data, utilize process-based data models to uncover valuable insights, and enhance the accuracy of financial projections. This guide contains use cases and workflows for: ✅ Optimize your financial operations ✅Accounting document evaluation ✅Contract accounting guidance ✅Amendment comparisons ✅Contract management ✅Build a business case ✅Contract review ---------- Follow AI Finance Club if you want to upskill yourself on AI for finance.