Picture this: a process where reading annual reports, usually several, is condensed into a brief and efficient task. Think about having the crucial details, the key parameters, and the financial data instantly accessible and delivered in a fraction of seconds. Today, I’m going to show you how chatGPT and similar AI technologies are not just disrupting, but redefining efficiency and accuracy in business analysis.
While working in my previous organization as a financial analyst, I would be required to go through a number of annual reports redundantly in order to fetch specific information to create and maintain financial products. This would take us several days to get any concrete data. In this project, I harnessed the capabilities of chatGPT to fuel my analysis, resulting in a significant boost in both time efficiency and accuracy.
By leveraging chatGPT’s advanced language modeling and text generation capabilities, I created an interface that seamlessly interacts with the financial data and the annual report. The tool provides me with the URL which redirects me to an interface created for the analyst to interact with the relevant documentation they’re looking to analyze in real-time.
For example, I analyzed a section of the annual report of Apple in the year I was interested in. I asked the tool about the company’s major expenses and how they have changed over time. The tool submitted the response within a fraction of a second and even provided me with the exact location of the information in that annual report, without the need for manual browsing.
This tool can perform similar tasks for different companies, such as analyzing Google’s cash flow and its evolution. The tool provides the relevant information accurately and efficiently.
But how does it work? Currently, chatGPT has a limitation on the number of tokens it can process. For instance, the current model of chatGPT can only handle a limited number of tokens, which poses a challenge when examining and analyzing extensive documents such as annual reports that span over 100 pages. This is where my search model comes in.
The search model acts as a financial analyst who cites every result, places importance on accuracy, and ignores irrelevant information. It uses thought prompting and the right prompt to reduce the chances of inaccuracies and get more exact responses. By providing the user text query, background information, prompt instructions, and the chatGPT API key, the GPT Transformer performs the analysis quickly and efficiently.
Financial analysis is not limited to annual reports. To get a bigger picture, insights need to be derived from other important financial statements such as income statements, revenue statements, profit statements, and stock prices. By leveraging Python libraries like numpy and matplotlib, these financial statements can be accurately analyzed and visualized to drive insights.
The future of business analysis lies in leveraging AI technology to revolutionize the domain. It is important for all of us to keep up with the pace of this ever-evolving technology. In conclusion, I’m eagerly seeking new opportunities to contribute my skills and expertise in projects that tackle business problems and add value to organizations. Feel free to reach out to me on LinkedIn to discuss any potential opportunity or collaboration. Let’s connect together and make a meaningful impact.
