What You Will Learn

• Use CSI and FBI Pro prompting to get tailored AI outputs
• Draft and chunk long finance procedures and policies efficiently
• Automate repetitive data tasks with AI-generated Python code
• Enhance Excel, PowerPoint and ERP workflows using AI
• Perform secure, reproducible financial analysis and visualisations

Study Guide

Introduction

Welcome to the comprehensive guide on leveraging Generative AI in Corporate Finance. This course is designed to transform finance professionals into AI champions, equipping them with the tools and knowledge to integrate AI into their daily workflows. From enhancing productivity to improving financial analysis, generative AI offers a myriad of opportunities to streamline processes and gain deeper insights. This guide will walk you through each aspect in detail, ensuring you not only understand the concepts but also know how to apply them effectively.

Understanding the Gap in AI Adoption

Despite the buzz around AI, its adoption in corporate finance remains limited. Many professionals are aware of tools like ChatGPT but hesitate to incorporate them into their daily tasks. This reluctance often stems from a lack of understanding of practical applications. Our goal is to bridge this gap by showcasing specific use cases and actionable techniques that finance professionals can implement immediately.

Example:
A finance manager might use ChatGPT to draft emails or simplify complex financial language for stakeholders, streamlining communication and saving time.

Example:
An accountant could use AI to generate a first draft of financial procedures, reducing the time spent on documentation by up to 80%.

Four Key Categories of AI Application in Finance

1. Enhancing Productivity

AI can significantly boost productivity by assisting with word-based tasks. Whether it's drafting emails, creating presentations, or translating texts, AI can either perform these tasks or enhance the quality of work.

Example:
A CFO might use ChatGPT to draft a quarterly financial summary, ensuring clarity and conciseness in communication with board members.

Example:
A treasury analyst could leverage AI to translate financial reports into different languages, facilitating international collaboration.

2. Streamlining Finance Procedures

Generative AI can expedite the creation of finance procedures. By drafting a significant portion of documents, AI saves time and effort, especially when dealing with internal controls or auditors.

Example:
An internal auditor could use AI to draft compliance reports, focusing only on reviewing and finalizing the content.

Example:
A finance team might use AI to generate the initial draft of a new accounting policy, significantly reducing the workload.

3. Tool Enhancement

AI acts as a "super assistant" for existing tools like Excel, PowerPoint, SAP, Oracle, and QuickBooks. It provides instant support and tutorials, reducing the time spent searching for solutions.

Example:
A financial analyst could use AI to generate Excel formulas or troubleshoot errors, enhancing efficiency in data analysis.

Example:
An accountant might use AI to create PowerPoint presentations, automatically generating slides from financial data.

4. Improving Financial Analysis

AI can assist with preliminary analysis, uncovering insights, and generating visualizations that might not be immediately obvious. This enhances the quality of financial analysis.

Example:
A finance director could use AI to perform trend analysis, identifying patterns in financial data that inform strategic decisions.

Example:
A budget analyst might use AI to visualize financial data, making it easier to communicate findings to non-financial stakeholders.

Effective Prompting Techniques: CSI and FBI

1. CSI Technique

The CSI technique—Context, Specificity, and Instruction—improves AI prompting by providing detailed input, leading to more relevant and useful outputs.

Example:
When asking AI to draft a financial report, provide context (e.g., quarterly results), specify the desired outcome (e.g., include key performance indicators), and give clear instructions (e.g., use simple language for non-financial readers).

Example:
For creating a budget proposal, specify the context (e.g., annual budget), desired outcome (e.g., include cost-saving measures), and instructions (e.g., highlight significant variances).

2. FBI Pro Framework

Building on CSI, the FBI Pro framework—Format, Blueprint, Identity—further refines AI prompting by focusing on output format, content blueprint, and role identity.

Example:
Instruct AI to draft a legal letter by specifying the format (e.g., formal letter), blueprint (e.g., include legal actions), and identity (e.g., act as a lawyer).

Example:
When generating a financial analysis report, specify the format (e.g., executive summary), blueprint (e.g., include charts and graphs), and identity (e.g., act as a financial analyst).

Leveraging AI for Secure Data Analysis

Generative AI can handle financial analysis involving numerical data securely. By generating Python code for analysis, you can execute it in secure environments like Google Colab or local IDEs, ensuring data confidentiality.

Example:
A data analyst might use AI-generated Python code to perform a headcount analysis, generating visualizations like box plots without exposing sensitive data.

Example:
A finance team could use AI to automate the analysis of customer data, creating heatmaps to identify trends and insights securely.

Automating Repetitive Tasks

AI-generated Python code can automate repetitive tasks, such as combining multiple data files, saving significant time compared to manual methods.

Example:
A sales analyst could use AI to merge monthly sales data files into a single dataset, streamlining the reporting process.

Example:
An accountant might automate the consolidation of financial statements from different subsidiaries, ensuring accuracy and efficiency.

Creating Long Documents with the Chunking Method

The "chunking method" is essential for creating long-form content like finance procedures. By breaking down the task into smaller parts, you can manage the AI's context window limitations effectively.

Example:
When drafting a 50-page financial policy, first outline the document, then use AI to generate each section individually, ensuring consistency and detail.

Example:
For creating a comprehensive audit report, divide the task into sections such as introduction, methodology, findings, and recommendations, and prompt AI to draft each part separately.

Immediate Action and Accessibility

The course emphasizes that finance professionals can start using these AI techniques immediately, without needing enterprise-level AI packages. However, caution is advised against inputting confidential data without appropriate agreements.

Example:
A finance team could begin using AI for data visualization tasks, enhancing presentations and reports without waiting for a digital transformation.

Example:
An individual accountant might use AI to draft client communications, improving efficiency and effectiveness in daily operations.

Becoming AI Champions

The ultimate goal is to empower finance teams to lead AI adoption within their organizations. By mastering these tools and techniques, they can position themselves as leaders in the AI revolution.

Example:
A finance department could spearhead an AI initiative, training staff to use AI tools for various tasks, thereby enhancing overall productivity and innovation.

Example:
A CFO might advocate for AI integration in strategic planning, using data-driven insights to inform decision-making and drive business growth.

Conclusion

By now, you should have a comprehensive understanding of how to use Generative AI in Corporate Finance. From enhancing productivity to improving financial analysis, AI offers transformative potential for finance professionals. By applying the techniques and methodologies covered in this course, you can become an AI champion, leading the way in AI adoption and innovation within your organization. Remember, the thoughtful application of AI is key to unlocking its full potential and driving meaningful change in corporate finance.

Podcast

Frequently Asked Questions

Welcome to the FAQ section for the 'Video Course: How to Use Generative AI in Corporate Finance Full Course'. This resource is designed to address common questions and provide insights into effectively integrating generative AI into corporate finance workflows. Whether you're new to AI or looking to deepen your understanding, you'll find answers that cover basic concepts to advanced applications.

Why is there a gap between knowing about AI and using it daily for finance work?

Many finance professionals are aware of generative AI like ChatGPT but haven't fully integrated it into their daily workflows. This is often because they haven't yet identified concrete, practical applications relevant to their specific tasks. The course aims to bridge this gap by showcasing specific use cases and providing actionable techniques that finance professionals can immediately implement.

What are the primary areas within corporate finance where generative AI can offer significant benefits?

The course identifies four key categories:

• Productivity: AI can assist with word-based tasks like writing emails, minutes, presentations, translating text, and simplifying complex language for better communication.
• Finance Procedures: Generative AI can rapidly draft the majority (around 80%) of finance procedures, saving considerable time and effort when internal controls or auditors require documentation.
• Utilising Existing Tools: AI can act as a "super assistant" for tools like Excel, PowerPoint, SAP, Oracle, and QuickBooks, providing instant tutorials and solutions to problems, reducing time spent searching for answers.
• Financial Analysis: AI can aid in performing preliminary analysis, uncovering insights, and generating visualisations that might not be immediately obvious.

What is the CSI technique, and how does it improve prompting with generative AI?

The CSI technique stands for Context, Specificity, and Instruction. Instead of using generative AI like a search engine with simple keywords, the CSI method involves providing sufficient context about the task, being specific about the desired outcome, and giving clear instructions on what the AI should do. This structured approach leads to more relevant and useful outputs compared to basic keyword prompting.

How does the FBI Pro framework build upon the CSI technique for more advanced AI prompting?

The FBI Pro framework enhances the CSI technique by focusing on refining the output. It stands for:

• Format: Specifying the desired output format (e.g., a script instead of a formal letter).
• Blueprint: Describing the essential elements and content that should be included in the output (e.g., mentioning legal actions in a demand letter).
• Identity: Instructing the AI to adopt a specific persona or role (e.g., "act as a lawyer").

By incorporating the FBI Pro elements after establishing context, specificity, and instructions, users can achieve more tailored and impactful results from generative AI.

Can generative AI be used for financial analysis involving numerical data, and how can this be done securely with confidential information?

Yes, generative AI can be used for financial analysis. The course demonstrates how to upload and analyse headcount and customer data to generate visualisations and insights. To handle confidential data securely, the recommended approach is to ask the AI for the Python code required for the analysis. This code can then be executed in a secure, local environment like Google Colab, Azure, or a personal laptop, allowing users to work with sensitive data without directly inputting it into the AI platform. This also enables auditing of the code and repeated analysis on multiple files.

How can generative AI assist with the often time-consuming task of combining multiple data files for analysis?

Generative AI can provide Python code that automates the process of combining multiple data files (e.g., monthly sales files). By specifying the location of the files, users can execute this code in their secure environment, and the AI will efficiently merge the data into a single file, saving significant time and effort compared to manual methods or traditional tools like VBA.

What is the "chunking method," and why is it important for creating long-form content like finance procedures using generative AI?

The "chunking method" is a strategy for creating lengthy documents by breaking down the task into smaller, manageable parts. Generative AI models often have limitations on the input and output size (context window). For documents like 40-60 page procedures, instead of trying to generate the entire document at once, the user first defines an outline and then prompts the AI to write each section or chapter individually, ensuring a consistent style and detailed content within the AI's output limits.

What is the key takeaway message for finance professionals looking to adopt generative AI?

The main message is that finance professionals can start using generative AI tools like ChatGPT immediately to enhance their productivity, improve their workflows, and gain deeper insights from data, even without enterprise-level agreements or extensive technical expertise. By focusing on practical applications, using effective prompting techniques (CSI and FBI Pro), and employing secure methods for handling confidential data (like using AI-generated code in local environments), finance teams can become AI champions within their organisations. The course also provides a user guide to help finance professionals begin their journey with ChatGPT.

What is a common trend observed when asking finance professionals about their AI usage at work?

Many finance professionals have experimented with AI personally, but significantly fewer use it regularly for their daily work tasks. This course aims to increase the daily adoption of AI within finance teams by demonstrating practical applications and benefits.

How can Python be used in conjunction with generative AI for financial analysis involving numerical data?

Python acts as an intermediary for generative AI to analyse numerical data. ChatGPT can instruct Python to read data files, perform calculations, generate visualisations like graphs, and provide preliminary analysis that it cannot directly do itself. This collaboration allows for more robust data analysis capabilities.

Why might a finance professional choose to extract Python code from ChatGPT and run it in their own environment like Google Colab?

A finance professional might choose to use Python code in their own environment to handle confidential data securely, have the ability to audit the code, and overcome limitations ChatGPT might have with large datasets or the inability to run code directly. This ensures data privacy and control over the analytical process.

What is the benefit of using Python to automate the task of combining multiple data files for financial analysis?

Using Python to automate combining multiple data files saves significant time compared to manual methods or even using tools like VBA. It allows for the efficient aggregation of data from numerous files, enabling faster and more comprehensive analysis.

What is a crucial consideration when using generative AI tools with work-related data?

A crucial consideration is to avoid inputting any confidential data into generative AI tools if the user or their company does not have a specific agreement or enterprise package that ensures data privacy and security. Ensuring data confidentiality is paramount to maintaining trust and compliance.

What is the potential impact of generative AI on the productivity of finance professionals?

Generative AI can significantly enhance productivity by streamlining common tasks such as drafting reports, analysing data, and managing communications. Tools like ChatGPT can automate routine tasks, provide quick insights, and free up time for finance professionals to focus on strategic activities. This leads to improved efficiency and effectiveness within finance departments.

What are the security considerations and potential risks associated with using generative AI for financial analysis?

Security considerations include the risk of data breaches, unauthorised access, and the potential misuse of sensitive information. To mitigate these risks, it's crucial to use AI-generated code in secure, local environments and ensure that any data shared with AI tools is anonymised or non-confidential. Adhering to best practices in data security is essential to protect sensitive financial information.

How might the role of finance professionals evolve with the integration of generative AI?

As AI tools become more integrated into workflows, finance professionals may shift from routine data processing to more strategic roles, focusing on interpretation and decision-making. Skills in AI literacy, data analysis, and strategic planning will become increasingly important. The ability to leverage AI for insights and innovation will be a valuable asset in the evolving finance landscape.

How do traditional methods of creating financial procedures compare to those enabled by generative AI?

Traditional methods often involve manual drafting and significant time investment, while generative AI can automate and expedite the creation of financial procedures. AI can generate initial drafts, suggest improvements, and ensure consistency across documents. However, it requires oversight to ensure accuracy and compliance with regulations. The advantages of AI include speed and efficiency, but human expertise remains crucial for validation.

What is a strategy for successfully adopting generative AI tools in a finance department?

A successful strategy involves starting with small, manageable projects, educating the team on AI capabilities, and gradually integrating AI tools into daily operations. Key steps include identifying tasks that can benefit from AI, training staff on effective prompting techniques, and establishing protocols for data security. Becoming "AI champions" requires continuous learning and adaptation to new AI developments.