AI in your Profession Assignment (Finance)

Where or how the AI is currently being used, or might be used in the near future, in the Finance field, whether academically while studying or in the workplace

First of all, I would like to start by saying that I am majoring in finance, and AI is already widely used in this field. What is interesting is that each area within finance tends to rely on different AI tools depending on the task. For example, in Investment Banking and Private Equity, tools like Claude are often used for financial modeling and valuation, while for portfolio optimization and general financial analysis, professionals often use ChatGPT. In addition, AI is not limited to these areas. It is also heavily used in algorithmic trading, where models can process large datasets and detect patterns much faster and more accurately than humans. From an academic perspective, I personally use AI tools to better understand complex financial concepts such as valuation, forecasting, and risk analysis. AI also helps me in building financial models and other models as well. However, there are also disadvantages to using AI. One major disadvantage is the risk of sharing highly confidential financial data with other platforms. Because of this, many investment banks and financial institutions prefer to collaborate with AI companies to develop private and secure AI systems instead of relying on free AI tools such as ChatGPT or Claude. In the future, I believe that there will be more advanced agentic AI systems that can independently build complex financial models and assist in decision-making. While this may increase the unemployment rate in some roles, it may create new opportunities where professionals work alongside AI.

Ways AI will be used in the Finance field

AI has shifted from being just a pilot project to becoming a core part of operations in the finance sector. One major development is the rise of highly personalized banking, where AI can analyze customer behavior and predict financial needs in advance, such as suggesting loans or adjusting investment portfolios based on life events.

At the same time, finance is increasingly using a “human + AI agent” model, where AI handles repetitive and data-heavy tasks like financial closing, invoice processing, and drafting reports. This allows professionals to focus more on analysis and decision-making rather than routine work.

AI is also playing a critical role in fraud detection. Advanced systems can now verify identity through behavioral patterns like typing and mouse movements, while analyzing large amounts of data in real time to detect suspicious activity more accurately and reduce false alerts.

Finally, AI is transforming strategic planning. Instead of relying on fixed annual budgets, companies now use AI to run continuous simulations based on market conditions, helping them make faster and more flexible financial decisions.

Remark: for this question, i used Gemini, and here is the link for the group chat: https://gemini.google.com/app/d2906280606d89c6

Interview Questions for Professor

From your perspective, how has the integration of AI reshaped the underlying decision-making frameworks in finance, particularly in areas like portfolio management and optimization?

Which AI tools do you use the most in your work, and how have they changed the way you start a new project?

When AI suggests an investment or market trend, how do you check if it is actually correct and not misleading?

Do you think AI will make investing more accessible to everyone, or will it increase the gap between big institutions and individual investors?

Interview questions for the Professional

How do you incorporate AI-generated insights into your decision-making process while ensuring they align with broader business strategy and risk management frameworks?

Given the increasing use of AI in forecasting and scenario analysis, how do you assess the reliability of these models under volatile or unprecedented market conditions?

In your view, does AI enhance or challenge traditional advantages that big companies use to stay ahead of competitors, particularly for large institutions compared to smaller players?

How do you manage the risks associated with heavy dependency on AI, especially in areas where human judgment and experience have historically played a critical role?

Looking ahead, what skills and mindsets do you believe will be essential for finance professionals to remain relevant and effective in an AI-driven environment?

Key findings from the Professor Interview

I reached out to one of my professors, Dr. Noah Farhadi, Professor of Finance in the Department of Management at the Onsi Sawiris School of Business at AUC.

From his point of view, AI has really changed how decisions are made in finance, especially when it comes to portfolio management and optimization. He explained that instead of relying heavily on intuition, the process is now much more data-driven, since AI can handle and analyze huge amounts of data that would have been impossible to process before. When I asked Dr. Noah about the AI tools he uses, he highlighted that models like XGBoost are particularly useful because they can capture complex, non-linear relationships in financial markets, which leads to better portfolio construction and risk evaluation. He also mentioned that he regularly uses tools, such as ChatGPT, as part of his workflow, not just for insights but also to support coding, structural analysis, and interpreting results. Because of that, the way he starts a new project has shifted, with AI now playing a role from the early beginning of the project design to speeding things up and making the overall process more efficient.

At the same time, he made it clear that using AI requires careful judgment, especially when it comes to investment or market trends. Since he has worked with AI multiple times before, he has developed a sense of when its outputs are reliable and when they might be misleading or even hallucinating. He doesn’t take AI-generated results at face value. Instead, he cross-checks them against financial theory, past data, and other models to make sure they actually make sense. When I inquired if AI would make investing more accessible to ordinary small investors, he mentioned that he believes AI has the potential to make investing more accessible, especially if universities start offering more courses that teach students how to use these tools properly. However, he pointed out that large institutions still have an advantage over individual investors, since they have better access to data, technology, and expertise, which widens the gap between these two groups. Even so, he thinks that with the right knowledge and training, AI can still help individual investors make better and more informed decisions.

Key findings from Professional Interview

I have also contacted a professional in the field of finance, who prefers to remain anonymous due to her contract obligations with CI Capital Holdings, Egypt, regarding the use of AI tools at financial institutions.

When I asked her how AI-generated insights are incorporated into decision-making while still aligning with the broader business strategy and risk management, she made it clear that, in her role in asset management, AI is not actually used to make financial decisions. She explained that at CI Capital, the use of AI is allowed but tightly controlled, and in practice, it is mainly used as a supporting tool rather than a decision-maker. Instead of relying on AI for investment calls, they use it to assist with routine tasks, such as organizing and spreading financial statements into Excel, which helps improve efficiency without interfering with the decision-making process. When I asked if AI models are reliable, especially in unpredictable market conditions, she said that whenever AI is used, they make sure to validate it by back-testing the results and/or recommendations against actual historical data to see if the outputs are quite accurate and consistent. She emphasized that this step is essential for her and her colleagues from other departments as well to ensure that AI is not producing misleading results, particularly given the uncertainty of financial markets.

When I questioned whether AI enhances or challenges the traditional advantages that big companies use to stay ahead of competitors, she explained that, in their case, it does not really disrupt the existing structure, since AI is not part of the high-level financial decisions. Instead, it acts more as a tool for efficiency rather than a source of competitive advantage. For CI Capital, traditional strength points, such as expertise, experience, and access to information, remain the main advantages for decision-making in the large financial institutions. On the question of managing the risks of heavy dependency on using AI in finance, she emphasized that this is addressed by keeping AI in a limited, supportive role and relying primarily on human judgment and established financial practices. Finally, when I asked about the skills and mindsets needed going forward, she replied that finance professionals still need strong analytical thinking, practical experience, and the ability to critically evaluate AI outputs rather than accepting them at face value. Her views reflected a cautious and controlled approach. AI is useful for improving workflow efficiency, but it does not replace the core principles of finance or the importance of human judgment, which is great news for the rest of us, graduating seniors!

My reflection on the key findings

In my opinion, I originally expected both Prof. Farhadi and the CI Capital professional to say that they use AI all the time and that it plays a central role in their daily work, especially in areas like investment banking and portfolio management. I have expected that AI would be heavily integrated into decision-making and optimization processes. However, after listening to their perspectives, I realized that this is not really the case. While AI is certainly present, it is not as dominant as I thought, particularly in high-level financial decisions. For example, the professor highlighted how AI supports analysis and improves efficiency, but still requires strong human judgment and validation, while the professional from CI Capital made it clear that AI is used in a much more limited and controlled way, mainly for supporting tasks rather than actual financial decision-making. This was surprising to me since I follow many professionals and analysts online and watch a lot of videos and seminars. I got the impression that these experts rely heavily on AI for things like scriptwriting, video creation, chart analysis, and problem-solving, so I expected the same level of dependence in real-world finance. Instead, it seems that traditional methods, experience, and human judgment still play a much bigger role in finance than I anticipated.

At the same time, I think this does not mean that AI will remain limited in the future. Based on what I understood from both perspectives, AI is still developing within the finance industry, especially in Egypt, and its adoption may take time due to the need for reliability, regulation, and proper integration into existing frameworks. However, there is a strong possibility that its role will grow as tools become more advanced and easier to use, and as institutions become more comfortable relying on them. From my side, this made me realize that I should not just depend on AI passively the way I do in coursework, but instead focus on understanding it more deeply and using it in a more thoughtful and practical way. I think it is important for me to explore different types of AI tools, not only large language models but also things like AI agents and machine learning applications, so that I can build a stronger skill set and be ready to apply them effectively in a real work environment when the industry starts adopting them more widely.