Part 1

I must admit, I am honestly not that exposed to the way in which AI is being used in Finance. I blame that on multiple reasons, one of them being that professors themselves are still adopting the idea of AI – they focus more on teaching us theory than they do practical work. However, I am aware of the impact of AI in finance. From personal experience, I recently had a project for my corporate finance class where we had to evaluate a company using a financial model. This project was completely new to me and my class; we had never done anything like it before. We were already given a semi-structured model so we didn’t start from scratch, but we needed help understanding it. We used Claude to help us understand what data to use from the financial statements and where to place them in the model. We also had it double-check for errors and explain the already built-in model to us when we were confused. It honestly helped us a lot and made very minimal errors. As part of the project, we also met with the director of Arqam Capital Holding, who basically guided us through the project. He noticed that we were using Claude and joked about how it wouldn’t help, but then went on to tell us that the Arqam employees are currently taking a course on how to use Claude to make their work more efficient. Another encounter I had with Claude and financial modeling was through a friend who is taking a graduate-level financial modeling course – they basically spend the whole semester building financial models. Usually this is a very tedious and difficult task that can take a very long time to complete. He opened up Excel for me and there it was, Claude’s icon in the top right corner. He told me it basically helps them build the model from absolute scratch. I know Claude is not the only platform or AI tool used in finance; the effect of AI in finance is definitely much deeper than that. I expect that a lot of processes are automated now, such as tracking the market, algorithmic trading, and even portfolio management. I organized an event where we brought in two speakers specialized in finance – one had built an AI-designed app that acts as an online broker and portfolio manager, capable of designing portfolios, predicting returns over a chosen time period, and giving investment advice. The other speaker had managed to use algorithmic trading to build a portfolio with over 50% returns, which is genuinely remarkable. All of these encounters gave me a very surface-level and broad idea of how AI is used, and I honestly can’t wait to explore more.

Part 2: Exact Output

Tool used: Claude (Anthropic) | Link: https://claude.ai/share/4d8e1c26-b41f-448a-8274-5129caab625e | 

AI is already reshaping finance in some big ways, and the trend is accelerating. Here are the main areas:

Fraud Detection AI monitors millions of transactions in real time, flagging unusual patterns faster and more accurately than any human team could. Your bank likely already uses this when it texts you about a suspicious charge.

Personal Finance & Advice AI-powered apps can analyze your spending, suggest budgets, and give tailored financial advice — making services that used to require a human advisor available to everyone cheaply.

Trading & Investing Algorithms already execute a huge share of stock trades. AI can spot market patterns, react to news in milliseconds, and manage portfolios automatically based on your goals and risk tolerance.

Credit & Loan Decisions Instead of just checking a credit score, AI can assess a much wider picture of someone’s financial behavior to decide whether to approve a loan — potentially helping people who’d otherwise be overlooked by traditional scoring.

Customer Service Chatbots handle routine banking questions (balances, transfers, disputes) around the clock, freeing human agents for complex issues.

Risk Management Banks and insurers use AI to model risk more precisely — whether that’s predicting loan defaults, pricing insurance policies, or stress-testing portfolios against economic scenarios.

Regulatory Compliance Finance is heavily regulated and drowning in paperwork. AI can automatically scan transactions for money laundering signals, generate compliance reports, and keep up with changing rules.

Document & Contract Processing Reading and summarizing loan agreements, earnings reports, or legal filings — AI can do in seconds what used to take analysts hours.

The common thread is that AI is best at handling large volumes of data quickly and consistently, which is exactly what finance runs on. The biggest open questions are around fairness (are AI credit decisions biased?), accountability (who’s responsible when an algorithm makes a bad call?), and systemic risk (what happens if many banks’ AIs all make the same mistake at once?).

Here’s a breakdown of the real AI platforms actually used in finance today, matched to the tasks they handle:

📊 Market Research & Analysis

• Bloomberg Terminal — integrates AI and machine learning to help financial professionals analyze market data, run predictive models, detect patterns, and make informed investment decisions. It’s the gold standard for market research and trading intelligence. Team Academy Saudi
• AlphaSense — uses NLP to search earnings calls, filings, broker research, and news to surface what matters. GenAI speeds up summarization and trend discovery for better decisions. Complete AI Training
• Kensho (owned by S&P Global) — applies machine learning to historical market data to forecast event impacts and model macro scenarios. Quant teams and risk managers use it to understand how specific events — like policy changes, geopolitical developments, and earnings surprises — have historically affected asset prices. Hebbia

🤖 Predictive Models & Risk

• DataRobot — financial institutions use it to build credit risk models, fraud detection systems, and demand forecasts. The platform automates model selection, testing, and deployment. SSSgram
• Palantir AIP — solves data integration across complex institutions, helping banks make sense of large, messy datasets for risk and compliance. Prem AI

🔍 Fraud & Crime Detection

• Feedzai & SymphonyAI — specialize in financial crime detection, monitoring transactions in real time. Prem AI
• Quantexa — unifies siloed data and maps relationships to expose hidden connections, helping teams detect suspicious activity earlier and cut false positives. Complete AI Training

💳 Lending & Credit

• Zest AI — enables financial institutions to build fairer, more accurate credit models using machine learning, considering a wide range of data to improve risk prediction and expand credit access. Team Academy Saudi
• Upstart — uses AI to evaluate creditworthiness beyond traditional scores, making lending more inclusive.

🏦 Big Banks Building Their Own

• JPMorgan Chase integrated tools like Coach AI and a GenAI toolkit used by over half of its 200,000 employees, contributing to nearly $1.5 billion in savings, with improvements in fraud prevention, trading, and credit decisions. CPOstrategy

General-purpose tools like ChatGPT and Claude are also widely used by analysts for summarizing reports, drafting documents, and interpreting data — they’re the “everyday assistant” layer on top of the specialized platforms above

Part 3 — Interview with Dr. Jasmin Fouad, Professor of Finance at AUC

I interviewed Dr. Jasmin Fouad, a Professor of Finance at AUC. My questions centered around the development of AI, because I know she has been working in the field for quite some time – I wanted to get a “before and after” perspective on what she personally thinks about AI. Surprisingly, she told me that in her academic work she does not use AI at all, but she does see its impact on those around her. She also said that because some things are becoming more and more automated, the content she teaches is slowly adapting. She gave some examples that were not exactly AI-related, but they reflect the context of adapting to a developing world. For example, when she was studying finance, they didn’t even have Excel – they had to do everything by hand, which is a completely different experience from what we learn now. She said if Excel had such a big impact, she can’t imagine how AI will impact the field even further. She also referenced algorithmic trading and investing apps that have built-in AI that basically do the job of a portfolio manager, and are much more efficient and convenient for the client. When she mentioned AI doing the job, I asked her how she thinks it will affect employees – does she think AI will replace jobs? She said she does not think it will replace or eliminate jobs, but it will change the way the job itself is done. People have to learn how to use AI to make their work more efficient. She said we are living in a time where we have the tools to “perfect” our work, and so we must learn to use them to the best of our ability. In the new finance degree catalog – which I am not part of – there is a new required course: Data Science. I asked her if this is related to AI and how it helps people in finance. She confirmed that it is in fact a direct response by the university to recent developments in AI, and it aims to ensure students know how to input the right information, build models, and so on. I asked her if I am behind because I am not part of this catalog, and whether there is something one can do to have an advantage since everyone is using AI. Her response was quite interesting – she assured me that I am not behind, but she does encourage me to take courses to enhance my AI literacy. She suggested Python and Claude, and noted that even professors are constantly taking courses so they don’t fall behind. However, she said the edge I will gain in the workplace is not from the tools I use – in finance, a lot of it comes down to character. That honestly surprised me.

Part 4 — Interview with Ibrahim Morsi, Asset Management.

When it came to interviewing someone in the profession, I interviewed a family friend working in asset management. My questions centered around the ways in which he sees AI being used in his work, how he thinks it will develop further in the field, how people in the field react to using AI – whether it’s a tool that does the job for them or one they’re skeptical of – and what risks come with using AI at work. I also asked him to give advice on how to adapt to this changing environment. He mentioned that in his job he is required to monitor market trends, build quantitative forecasting models, and prepare reports and presentations for clients and management. He said that the development of generative AI and large language models has impacted his workflow because it makes the research process much easier, and it also assists him in coding. When I asked what he uses, he mentioned Claude and ChatGPT as coding assistants to build analytical tools and machine learning models used for pricing and forecasting. He also mentioned that on the quantitative side, he uses Python and Excel. I asked him about algorithmic trading because I had been hearing it mentioned a lot. He said it is actually not new and that generative AI is a much more recent development – algorithmic trading dates back to the 2000s, though he noted that it has become far more sophisticated and has accelerated dramatically. He generally spoke about AI in a positive light, stating that it does make work much more efficient, but acknowledged that there are risks. He said people don’t fully depend on AI – for example, it helps with quantitative analysis, but there is a qualitative side that comes from the portfolio manager’s own judgment, so human input is essential. He also mentioned that to work with AI, you have to fully understand the output in order to explain it to your clients, so it’s not blind work – you have to learn how to work with it and have some background yourself. He advised students to take courses in coding and explore quantitative finance, but also to invest heavily in soft skills such as critical thinking, communication, and deep knowledge of the field, because he believes these human qualities are becoming more valuable as technical tasks become increasingly automated.

Part 5 — Reflection

Honestly, I was expecting to hear that AI is deeply integrated in the field – and I did find that from my interviews. I was already somewhat aware of this. However, what surprised me was a common thread in both of my interviews: when I asked about getting an edge, Dr. Jasmin mentioned having “character,” and Ibrahim also advised investing in soft skills. I know this may sound straightforward and obvious, but it is something I feel is often overlooked. People are so excited about AI nowadays that it leads them to believe that in order to stand out, they must master AI. Yes, it is absolutely crucial to understand and work with it – but if you truly want to stand out, you must become more human, not more robotic, because the norm has become automated. I know this does not only apply to finance, but I felt it was a valuable lesson I came out with – it brought my attention back to something I had been distracted from.

There is also a really clear difference between my professor’s answers and Ibrahim’s when it comes to AI usage. Dr. Jasmin mentioned that she does not use it at all in her work, especially not in teaching. She did acknowledge its impact and noted that we must learn to use the tools, but her answer – although surprising – makes sense to me, because as I mentioned earlier, professors are very slowly adapting to the AI shift. It’s as if they’re afraid of it. Part of it, I think, is not just fear, but also the fact that AI is completely disruptive in the sense that, in order to adapt at the same pace it’s evolving, professors might have to fundamentally change the way they teach – which they are doing, but very slowly. The introduction of Data Science as a required course is an example of that adaptation. When it comes to Ibrahim, I think his age makes a difference. He is relatively new to the field and entered at the stage where AI was booming, so he had to grow alongside it. His answers clearly reflect a deeply integrated work environment, and he gave me a much more detailed and comprehensive picture of how AI is used. Dr. Jasmin offered more of an outside perspective, whereas Ibrahim spoke from direct experience. Before the interviews, I expected to receive responses more like Ibrahim’s from both of them – mainly because when I asked AI about it, it listed so many ways AI is being used in finance, giving me the impression that it had already firmly established itself and that working with it had become the norm for everyone. After the interviews, I find myself leaning more toward Ibrahim’s perspective – not because it contradicts Dr. Jasmin’s, but because it gave me a better picture of what it might actually be like when I am working in the field, inshallah. I had always heard people talking about taking courses in coding, Python, Claude, and so on, but I never really took it seriously. Now it feels much more personal, and I am genuinely afraid that if I don’t equip myself with these tools, I will fall behind. In addition to that, I will make an effort to not only improve my technical skills, but also my soft skills – so that I can truly have an edge, not just in a highly automated field, but a male-dominated one as well.