What You Will Learn

• Perform rapid AI-driven financial comparisons and identify cost drivers
• Use direct data upload workflows securely in pro AI environments
• Conduct headline-based analyses and implement Excel formulas and pivots
• Generate and refine Python-based dashboards for automated reporting
• Write effective prompts and critically evaluate AI outputs

Study Guide

Introduction

Welcome to the course on Performing a Financial Analysis With AI in 5 Minutes. In the evolving landscape of finance, speed and accuracy are crucial. This course is designed to empower finance professionals with the tools and techniques to leverage AI, specifically ChatGPT, to perform rapid and insightful financial analyses. Whether you're dealing with urgent queries from your CEO or seeking to streamline your analytical processes, this course will guide you through the methodologies to achieve these goals efficiently.

We'll explore three primary approaches to using AI for financial analysis: direct data upload, headline-based analysis, and advanced analysis using Python. Each method offers unique advantages, from handling sensitive data securely to creating dynamic dashboards for in-depth analysis. By the end of this course, you'll be equipped to transform your financial analysis workflows, making them faster and more effective.

AI for Speed and Efficiency in Financial Analysis

In the fast-paced world of finance, the ability to quickly analyze data can be a game-changer. AI tools like ChatGPT have revolutionized how financial analysis is conducted, reducing the time required from hours or days to just minutes. This course demonstrates how AI can help you break down numbers, uncover the drivers behind cost spikes, and deliver comprehensive analyses swiftly.

For instance, imagine receiving an urgent email from your CEO about a £125,000 cost increase. With AI, you can respond promptly, providing detailed insights into the causes of this increase. AI's ability to process large datasets rapidly and generate visualizations makes it an invaluable tool for finance professionals.

Three Key Methodologies for AI-Powered Analysis

Direct Data Upload (for Pro/Secure Environments)

The first method involves directly uploading financial data, such as a general ledger in Excel format, into ChatGPT. This approach is particularly effective in secure environments where data privacy is ensured. By uploading your dataset, you enable AI to perform immediate comparisons, identify significant variances, and provide detailed breakdowns with visualizations.

For example, when analyzing the £125,000 cost increase, AI might identify that personal cost and technology and software cost are the primary drivers. It can further break down these costs by department, revealing that the IT and Sales departments are largely responsible. This method not only speeds up analysis but also enhances the clarity and accuracy of your findings.

Additionally, integration with tools like Canva allows you to draft and edit communications based on AI's findings. You can quickly prepare an email to your CEO, complete with charts and tables, to explain the cost changes.

Headline-Based Analysis (for Confidential Data or Non-Pro Accounts)

When dealing with sensitive data that cannot be uploaded, headline-based analysis offers a viable alternative. This method involves providing ChatGPT with the structure of your data—such as the headers of your Excel file—and asking for step-by-step instructions to perform the analysis manually in Excel.

For instance, you can copy the headers and prompt AI with, "Here is how my data looks like in my Excel file. Help me analyze this report and prepare an analysis." AI will then guide you through creating new columns to calculate variances, generating pivot tables, and crafting visualizations like clustered bar charts.

This approach ensures that no confidential data is shared while still leveraging AI's capabilities to enhance your analysis. It's a practical solution for environments where data privacy is paramount.

Advanced Analysis with Python (for Robust and Automated Solutions)

For those seeking more in-depth and automated solutions, advanced analysis with Python is the way to go. By leveraging AI tools like ChatGPT and platforms like Google Colab, you can generate Python code to create dynamic dashboards that allow for interactive and repeatable analyses.

Suppose you want to create a dashboard that displays key financial drivers and allows for drill-down analysis by department and category. By prompting AI to generate the necessary Python code, you can achieve this. The dashboard can be designed to update automatically with new data, making it a powerful tool for recurring financial analyses.

However, it's important to note that AI-generated code may require refinement. Iteratively refining your prompts and critically evaluating the output ensures that the final product meets your analytical needs.

The Importance of Prompt Engineering

One of the key factors in successfully using AI for financial analysis is crafting well-defined prompts. Clear and specific instructions, such as requesting "visuals tables and commentaries," lead to more comprehensive and usable results from ChatGPT.

Defining the context, such as stating, "I'm a finance manager," helps AI tailor its responses to your needs. This aspect of prompt engineering is crucial for maximizing the effectiveness of AI tools in your analytical processes.

Iterative Refinement and Critical Evaluation of AI Output

The process of using AI for financial analysis is not always straightforward. As demonstrated in the Python analysis example, AI-generated code might not be perfect on the first attempt. Users must be prepared to evaluate the output critically and refine their prompts or the code itself to achieve the desired results.

This iterative refinement process is essential for ensuring that AI-generated analyses are both accurate and insightful. By embracing this approach, finance professionals can harness AI's full potential for enhancing their analytical capabilities.

Democratisation of Advanced Analytical Techniques

AI is democratizing access to advanced analytical techniques, enabling finance professionals without extensive coding skills to create dynamic dashboards and perform sophisticated analyses. By adopting AI-powered methods, you can enhance your analytical capabilities and add greater value to your organization.

For example, a finance manager with limited coding experience can use AI to generate Python code for a dynamic dashboard. This empowers them to perform in-depth analyses that were previously out of reach, ultimately leading to more informed decision-making.

Conclusion

By leveraging the methodologies covered in this course, you can now perform financial analysis with AI in just 5 minutes. Whether you're uploading full datasets, providing data headlines, or using Python for advanced automation, these techniques offer significant advantages in speed and accuracy.

As you integrate AI into your financial analysis workflows, remember the importance of thoughtful application and critical evaluation. AI is a powerful tool, but its effectiveness depends on how well you define and refine your prompts. By embracing these practices, you can transform your analytical processes and deliver greater value to your organization.

Podcast

There'll soon be a podcast available for this course.

Frequently Asked Questions

Frequently Asked Questions: Using AI for Financial Analysis

This FAQ section is designed to provide comprehensive insights into using AI for financial analysis, as demonstrated in the 'Video Course: Perform a Financial Analysis With AI in 5 minutes'. Whether you're a beginner curious about the basics or an experienced professional looking to deepen your understanding, this guide aims to address all your questions and equip you with the knowledge to leverage AI effectively in financial analysis.

1. What was the urgent situation that prompted the exploration of AI in financial analysis?

The CEO sent an urgent email noting a significant cost increase of £125,000 in the previous January, requiring a swift and thorough analysis to understand the cause of this spike. This urgency highlighted the need for faster and more efficient methods of financial analysis.

2. What are the three primary methods demonstrated in the video for using AI in financial analysis?

The video demonstrates three powerful ways to leverage AI:
Direct Data Upload: Uploading the complete financial dataset (like a general ledger in Excel) directly into a pro version of a tool like ChatGPT for immediate analysis.
Headline-Based Analysis: Describing the structure and key headers of the data to the AI, without sharing the actual figures, to receive step-by-step instructions for analysis within tools like Excel.
Advanced Analysis with Python: Using AI tools like ChatGPT and platforms like Google Colab to generate Python code for creating dynamic and automated financial dashboards.

3. What key benefits does uploading data directly to AI offer for financial analysis (assuming it's permissible)?

Uploading data allows the AI to perform a rapid comparison of financial periods, identify significant cost changes across different categories and departments, and generate visualisations like tables and charts to illustrate these changes. It can also assist in drafting communications, such as emails to explain the findings, potentially integrated with tools like Canva.

4. How can financial analysis be performed using AI without uploading sensitive data?

By providing the AI with a description of the data structure, including the column headers, and stating the analytical goal, the AI can generate step-by-step instructions and even specific formulas to perform the analysis manually within familiar tools like Excel. This method ensures no confidential data is directly shared with the AI platform.

5. What are some practical Excel techniques that AI can guide you through for financial analysis based on data headlines?

The video highlights using AI to get instructions for:
* Creating new columns to calculate variances between periods.
* Generating pivot tables to summarise data by different dimensions (e.g., department and category).
* Sorting pivot table data to identify the largest changes.
* Creating visualisations like clustered bar charts to represent cost breakdowns and differences.
* Modifying chart elements, such as reversing the order of categories, for better presentation.

6. How can Python and AI be used to create dynamic financial dashboards?

By prompting the AI to generate Python code (suitable for platforms like Google Colab), users can create interactive dashboards. These dashboards can display key financial drivers at a high level and allow for drill-down capabilities – for example, clicking on a department to see the main cost drivers within that department, or further drilling down by category. This automation allows for efficient analysis of recurring financial data.

7. What was a key takeaway regarding the reliability of AI-generated code for advanced analysis?

The video demonstrated that not all AI-generated code is immediately perfect or fit for purpose. The initial Python code produced by the AI didn't create the desired dynamic dashboard, highlighting the importance of iteratively refining prompts and validating the AI's output to achieve the intended analytical outcome.

8. What is the overall message regarding the integration of AI into financial analysis?

The main message is that AI offers significant potential to streamline and accelerate financial analysis, whether through direct data processing, guided manual analysis, or advanced automation with coding. By adopting these AI-powered techniques, finance professionals can gain insights more quickly, respond to urgent requests efficiently, and ultimately add greater value to their organisations.

9. What was the initial trigger for the financial analysis described in the video?

The financial analysis was initiated by an urgent email from the CEO stating that costs had jumped by £125,000 in the previous January. This prompted the finance manager to investigate the cause of the significant increase.

10. Name the first method of financial analysis using AI demonstrated in the video. What key feature allows for the use of potentially confidential data in this method?

The first method demonstrated was uploading the full data set into a Pro ChatGPT account. The key feature allowing for this is having a Pro account, which, according to the presenter's company checks, permitted the upload of confidential data.

11. Explain the second method of financial analysis shown in the video. What is a significant advantage of this approach?

The second method involved describing the data structure to ChatGPT without uploading the actual figures. This approach allows for AI-powered analysis guidance and step-by-step instructions for performing the analysis in tools like Excel, without exposing sensitive information.

12. What was the main purpose of using pivot tables in the second analytical method? What benefit did sorting the pivot table provide?

The main purpose of using pivot tables was to summarise the total costs and easily compare the figures between January 2024 and January 2025. Sorting the pivot table allowed for the identification of the largest cost changes by category and department.

13. Describe the third method of financial analysis presented in the video. Which tool is primarily used for this more advanced analysis?

The third method involved performing an advanced analysis using Python and Google Colab. This approach enables the creation of dynamic and potentially automated dashboards for deeper dives and reusable monthly analysis.

14. What was the presenter's initial experience with the Python code generated by the AI? What lesson did this illustrate?

The presenter's initial experience with the first Python code generated was that it produced a basic, non-dynamic chart that wasn't particularly helpful. This highlighted that not all AI-generated output is immediately perfect and may require refinement.

15. What improvement did the presenter request for the Python-based dashboard? What functionality did this enhancement provide?

The presenter requested a dashboard that could show the main cost drivers quickly and then allow for drill-down capabilities. This enhancement enabled users to click on a department to see its main cost drivers and further click to see the categories within that department.

16. According to the presenter, what is a key advantage of using Python for financial analysis, especially for recurring tasks?

A key advantage of using Python for financial analysis, as highlighted, is the ability to create dynamic and automated dashboards that can be reused every month. This requires only updating the data source for the analysis to be rerun.

17. Name two specific cost categories that were identified as significant drivers of the cost increase in the example analysis.

Two specific cost categories identified as significant drivers of the cost increase were personal costs and technology and software costs. These were consistently highlighted in the initial ChatGPT analysis and the subsequent dashboard.

18. What did the presenter suggest viewers do if they are finance professionals interested in simplifying their workload with AI?

The presenter suggested that finance professionals interested in simplifying their workload with AI should click below to get started with a 5-day email course where he shares his techniques.

19. Discuss the pros and cons of the three different AI-powered financial analysis methods presented in the video. Consider factors such as data security, level of detail, automation capabilities, and ease of use.

Each method has its unique pros and cons. Direct Data Upload offers speed and comprehensive analysis but raises data security concerns. Headline-Based Analysis is secure and easy to use but may lack depth. Advanced Python Analysis provides automation and detail but requires technical know-how and initial setup time.

20. Explain how AI tools like ChatGPT can assist finance professionals in performing urgent financial analysis tasks, as demonstrated in the scenario presented in the video. What are the key benefits of using AI in such situations?

AI tools like ChatGPT can rapidly process and analyse large datasets, providing immediate insights and visualisations. This speed is crucial in urgent situations, allowing finance professionals to quickly understand and communicate financial changes. AI also offers flexibility in handling data securely without direct uploads.

21. Critically evaluate the presenter's experience with using Python for financial analysis with AI. What does this suggest about the current capabilities and limitations of AI in generating sophisticated analytical tools?

The experience highlighted that AI-generated Python code might not be perfect initially and could require iterative refinement. This suggests that while AI is powerful, it still needs human oversight and expertise to tailor and validate outputs for specific analytical needs.

22. Based on the video, outline the steps a finance professional could take to integrate AI into their monthly financial analysis workflow. Consider the different levels of integration discussed.

Integration can be approached gradually: Start with Headline-Based Analysis for secure, manual insights. Progress to Direct Data Upload for rapid analysis if data security permits. Finally, implement Advanced Python Analysis for automated, dynamic dashboards, which can streamline monthly reporting tasks.

23. How does the use of visuals, such as tables and charts, enhance the communication of financial analysis findings, as emphasised in the video? Discuss the role of AI in facilitating the creation of these visuals.

Visuals transform complex data into understandable insights, making it easier to identify trends and anomalies. AI facilitates this by quickly generating accurate charts and tables, ensuring that the presentation of data is both informative and visually appealing. This enhances communication and decision-making processes.

24. What are some common challenges or misconceptions about using AI in financial analysis, and how can they be addressed?

Common challenges include data security concerns, the perceived complexity of AI tools, and the fear of job displacement. These can be addressed by ensuring robust data privacy measures, providing clear training and resources to demystify AI, and highlighting AI's role in augmenting rather than replacing human expertise.

25. What are practical steps a business can take to implement AI in their financial analysis processes?

Businesses can start by identifying key pain points in their current financial analysis workflows. Then, they should evaluate AI tools that address these needs, considering factors like data security and ease of integration. Gradual implementation, starting with pilot projects, can help teams adapt and refine AI usage effectively.

26. What potential obstacles might a company face when integrating AI into their financial analysis, and how can they overcome these?

Obstacles include resistance to change, initial costs, and technical skill gaps. Companies can overcome these by fostering a culture of innovation, highlighting long-term cost benefits, and investing in training programs to upskill employees in AI technologies.