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 ------- 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

Visual Insights with ChatGPT What is ChatGPT Vision? 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 ChatGPT Vision employs CNNS, specialized for image processing. These network analyze images by breaking them down into features and patterns, enabling a comprehensive understanding of visual content. What are CNNs? Convolutional Neural Networks They are built to recognize and understand complex patterns within visual information. They are a type of deep learning neural network specifically designed for processing and analyzing visual data, such as images and videos. Finance Industry Applications Document Processing and Verification • It’s analysis streamlines process and reduces human error in financial documentation. Investment and Trading - visual data analysis • Analyzes visual data from various sources such as charts, graphs, and market trends, providing insightful patterns. Risk Assessment - automated visual insights • Facilitates automated risk assessment by analyzing financial data, market trends, and various visual indicators. Customer Experience - enhancement • Offering visual search capabilities & facilitating personalized financial recommendations based on visual cues. Fraud Detection and Security • It analyzes and identifies fraudulent patterns within visual data associated with transactions. Types of Images You Can Upload • Photos and Art • Charts and Data Visualization • Social Media Content • Educational/ Technical Material What Makes A Good Prompt? • Be specific in your prompt. • Use keywords and phrases that are relevant to your topic. • Avoid using jargon or technical language. • Provide examples of the type of content you’re looking for. • Fine-tune your prompts in case the results don’t meet your expectations 👉 What are your thoughts on ChatGPT Vision?

Prompt Engineering There are different types of prompt engineering. We have compiled the best prompt engineering techniques to maximize your output. 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 Basic Prompting Frameworks: CSI (Context Specific Instruction) + FBI (Format Blueprint Identity) Contextual Information: Include relevant background to provide a comprehensive understanding. Precise Language: Use specific, unambiguous terms for clarity. Analogies and Examples: Use comparisons to simplify complex financial concepts. Advanced Prompting Techniques Agent Prompting: Framing prompts to make GPT act as an 'agent‘. Explicit Reasoning: AI details its process or reasoning in a clear, step-by-step manner. Chain-of-Thoughts: Breaking down a complex query into a series of simpler, logical steps. Chunking: Complex information broken down into smaller, more manageable 'chunks'. Prompt Optimization & Expansion: Start with a basic prompt and ask the AI to improve it. Team Prompting: Simulates a collaborative team environment using multiple agents. Socratic Prompting: Asking questions that lead the AI to explore a topic deeply. Fact Checking: Using prompts to verify the accuracy and credibility of information. Meta Cognition: Prompting involves encouraging the AI to reflect on its own thought process. Meta Cognition: Prompting involves encouraging the AI to reflect on its own thought process. 👉Which other LLMs do you know? 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

History of LLMs How many do you know? 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 1. LLAMA 2 7B to 70B parameter text models evolve. Llama-2-Chat shines in dialogue, and rivals top models in safety. Released July 18, 2023, with a custom commercial license on Meta's site. 2. GPT-2 GPT-2, predecessor to GPT-3 by OpenAI, generates human-like text, released in 2019 with fewer parameters but impressive language capabilities. 3. ERNIEBOT Limited knowledge on "Erniebot"; possibly a newer or specialized model post-January 2022 in the language model domain. 4. DOLLY 2.0 Abu Dhabi's TII created Falcon language models—Falcon-40B leads, aiming to rival closed-source LLMs. Parameters: 7B & 40B, Apache 2.0 licensed, launched June 5, 2023. 5. MPT MosaicML releases MPT-30B: English-code transformer, 8k token context, FlashAttention for speed, HuggingFace/NVIDIA support, 30B parameters, Apache-2.0 License since June 22, 2023. 6. ALPACA As of Jan 2022, ALPaCA wasn't widely known among large language models. Subsequent developments might have changed its recognition or status. 7. BLOOM BLOOM: 176B-parameter model in 46 languages & 13 programming languages, excels in text gen, adaptable for Info Extraction & QA. 8. BERT BLOOM: 176B-parameter model in 46 languages & 13 programming languages, excels in text gen, adaptable for Info Extraction & QA. 9. OPENCHATKIT Open-source chatbot, GPT-NeoXT-Chat-Base-20B-v0.16: sustainable, excels in dialogue, Q&A, classification, and summarization. 10. FLAN-T5 FLAN-T5, an evolved T5, excels in zero/few-shot NLP tasks with 1000+ tasks covered in multiple languages. Variants range from 80M to 11B parameters, targeting diverse language support. 👉Which other LLMs do you know? 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

Chain-of-Thought What does this prompt engineering technique involve? Break down a complex query into a series of simpler, logical steps. 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 Level 1: Breakdown of Main Problem Problem: Improving the Accuracy of Cash Flow Forecasts Data Collection, Data Analysis, and Process Improvement are the three main steps needed to enhance forecast accuracy. Level 2: Sub-Problems Breakdown Data Collection involves sourcing and acquiring data. Data Analysis covers trend analysis and model development; Process Improvement focuses on feedback integration and forecast refinement. Ask for Specific Tasks Data Collection: Identify key sources and automate data gathering. Data Analysis: Conduct statistical analysis and refine models. Process Improvement: Compare forecasts to actuals and adjust based on insights. Chain-Of-Thought Procedure & Example Procedure: "How to automate my bank reconciliation" 1. Identify the Core Question Start by understanding the main question you want to answer. The main objective is to automate the bank reconciliation process. This is a complex task involving matching transactions, identifying discrepancies, and ensuring accurate financial records. 2. Break it down Decompose the main question into smaller, more straightforward questions. Decompose this task into smaller, manageable steps: identifying common transaction types & patterns, defining rules, developing methods. Integrating these processes into an automated system. 3. Sequential Queries Frame your prompt by including these smaller questions in a logical sequence. Frame your prompt in a logical sequence addressing each part: Here you can ask help for automating your bank reconciliation process, give transaction examples then as to categorize & analyze. 4. Guide the Reasoning In your prompt, guide the AI through the reasoning process. Ask it to explain how transaction matching rules can be formulated based on past data, or which method can assist in flagging anomalies. 5. Synthesize the Conclusion Guide it to synthesize these individual insights into a coherent conclusion. The final part of your prompt should instruct the AI to combine insights from each step to outline a comprehensive strategy for automating bank reconciliation. 6. Review and Refine Refine the prompt to address specific areas, creating a feedback loop that enhances accuracy and depth. -------- 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

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 Become a master of integrating your data with AI Here is how you can enhance your skills from basic level to mastery: 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 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. ------- 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 Explained to Kids AI is everywhere in our lives and in our work. Especially, we, the finance professionals, know the importance of it. 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 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. ------- 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

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

Team Prompting Make your GenAI work like a team of experts! In this approach, different hypothetical 'agents' or 'characters' within the AI, each with distinct skills, roles, and perspectives, are defined and assigned specific tasks. 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 How to do it: • Define the Agents and their Roles • Assign Tasks and Sequence • Facilitate Collaboration Team Prompting Example: Saas Company Prompt and Outcome: You will act as a team of experts working for a SaaS company. The problem you need to solve today is [insert problem]. Here are the experts working in the team. You will make each one work after the other. Their work needs to be connected. FP&A Analyst: Analyzes financial data to identify cash flow trends and areas for improvement. The FP&A Expert starts by analyzing financials and identifying key areas. Marketing Manager: Develops strategies to increase revenue through customer acquisition and retention. The Marketing Manager then uses this analysis to devise revenue-boosting strategies. Web Developer: Implements technical solutions to optimize the SaaS platform for better customer engagement and sales. Web Developer implements technical enhancements based on the Marketing Manager's strategy. Other use cases example: • Slides preparation: CFO, Storytelling Expert, Powerpoint Expert • Cash action plan: Controller, Supply chain, Procurement • Automation project: Accountant, ERP specialist, Automation expert 👉 What do you think of this approach? -------- 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