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As a finance student, it is important to understand how Artificial Intelligence (AI) impacts the industry and its future. What draws my attention to finance is that it is more than just money. A simple definition of finance is the study of money, however, its field is much bigger than that. Finance is the foundation of the global economy. It helps banks, firms, and businesses operate daily. Many career options in finance can consist of investment banking, wealth management, accounting, private equity, real estate, and financial planning. Up until recent years, most of the financial transactions have been manually processed. The rapid growth of AI can make financial transactions and decisions in a time-efficient manner. AI can evaluate financial data and pick up on patterns to provide insights and decision-making. AI can be used to evaluate information from software such as Microsoft Excel, an essential industry program. Regarding AI’s recent innovation in finance, ethical values must be taken into consideration. Commercial banks have mobile apps where their clients can make transactions instantly. A problem in finance can be theft and fraud detection. However, AI has grown powerful enough to detect fraud. On a bank’s website, there are chatbots to assist customers with questions and concerns that they may have. This can strengthen a bank’s customer service with their clients. Being aware of AI in finance holds significance as it can educate me in my career search within finance and provide me with financial protection. This padlet will address specific examples of AI use in finance, ethical considerations, and future trends.

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As previously mentioned many commercial banks offer AI chatbots on their websites to assist their customers. Popular banks such as Chase utilize this feature. If clients or customers have concerns about their accounts the AI chatbots can provide them with the resources they need. These chatbots are able to show clients information such as their account balance, analyze spending habits, and help with insurance. Common commercial banks such as Bank of America and TD Bank have their own chatbots that have found success. However, there are still concerns with these chatbots. Their responses can be limited as they can often just restate bank policies and regulations when asked a complex or personal question (Argyriou 2024).

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AI chatbots revolutionise the banking industry

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AI can detect financial fraud, by recognizing patterns and abnormal behaviors. It tracks a large sample of transaction history that comes from machine learning. Benefits of this technology can include higher accuracy, a reduction of false positives, and efficient response times. Companies such as BNY and PayPal are improving the accuracy of fraud detection. There are still concerns with AI in fraud detection. Bypassing system restrictions via jailbreak can cause concern, so companies need to strengthen their authorization protocols. When a false positive occurs a legitimate transaction can be flagged as fraudulent, which can cause issues for customers. As previously mentioned, companies are working on fixing this, and AI remains a powerful tool in the financial aspect of fraud detection (Rowinski 2025).

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Case Study | AI in Detecting Financial Fraud

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In risk management, AI can serve as a powerful tool. It partners with machine learning to enhance risk management. This technology can analyze complex data/information to forecast accurate financial risks. It facilitates real-time monitoring of transactions. Along with fraud detection, it can analyze the market for investment banks. This improves decision-making processes along with operational efficiency. Challenges with AI in risk management can be potential biases with algorithms and navigating regulatory compliance. Another concern can be the ethical implications when it comes to decision-making. Humans must use AI to help make decisions rather than AI to make financial decisions with consequences. Despite these challenges AI in risk management offers strong benefits when working with banks and markets (Ranjan 2025).

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What does the adoption of AI and Machine Learning look like in risk management?

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AI is growing more powerful in finance as time goes on. AI is already enhancing efficiency in finance, minimizing risk, and improving customer-client relationships. These improvements can range from daily financial monitoring all the way up to high finance such as investment banking. The spending investment of AI in these financial firms is only going to increase. By 2027 the industry of finance is expected to spend $97 billion on AI. In the corporate world of finance, AI is beginning to be an essential tool for firms. Analysts have begun using AI copilots in their offices. Firms such as Morgan Stanley and JPMorgan have AI assistants that summarize video meetings and generate follow-up emails. In the future, the copilots can help with investment strategies and predict market trends. AI will improve in areas such as fraud detection and risk management. In fact, a European neobank is using generative AI to improve the training speed of their automated transaction monitoring system that detects fraud and money laundering (Parker 2024).

Another aspect of AI in finance is ethical consideration. These considerations can consist in algorithmic biases that machine learning systems adopt in finance. This is why when AI is being used in finance humans must still be the ones making the final decision for transactions. Another ethical concern can be in data privacy where clients' and companies' private data can be leaked. To control these ethical concerns regulatory frameworks must be incorporated to hold firms accountable. Additionally, this can increase user trust firms have with their clients (Guo 2023). I do have some concerns regarding the future of AI in finance. Specifically in algorithm biases and data privacy. This can cause issues for employees in these firms when it comes to decision-making. With the advancements of AI, being able to comprehend financial elements can make it easier for students looking to pursue a career in finance to understand.

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As AI continues to grow in Finance it comes with many societal implications. One of these can be enhanced markets. This increases efficiency and liquidity in markets, which can make buying/selling assets, stocks, and bonds a quicker process. Investors can benefit from this as they will receive fair prices that are updated. While AI is rapidly advancing in finance it is not ready to replace humans when making trades and transactions (Sidoti 2024). In these investment processes firms are using “Robo-Advisors” which analyze sophisticated algorithms and market trends. This can help a firm's client make sharper decisions which further build trust. In addition, AI-powered finance tools are strategizing with clients for retirement. It can outline different alternative retirement plans/outcomes for their clients. A big concern with AI in finance is the privacy access it has. In order to be fully utilized, these AI systems require personal information and financial data. This data must be stored securely for firms to maintain trust with their clients. If not, legal actions can be taken. There is a cause for concern for AI interfering with applicants for entry-level jobs in finance. A fortune report revealed that AI could replace a handful of junior-level Wall Street banking jobs. Since AI can automate some aspects of entry-level finance jobs, it gives applicants the opportunity to improve and learn new skills in areas such as data analytics and programming. Candidates should adapt to improve on skills that AI can not replace, such as client relationships and planning. (Kennedy 2024). I do not believe that AI will be replacing jobs in finance as humans need to be the ones making decisions. AI must serve as a tool and guidance for firms and their clients. However, the expansion of AI will continue to make breaking into finance more difficult than it already is. As a student studying finance, I must continue to familiarize myself with my data and client skills.

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Throughout this research, I was able to find articles from reliable sources. I had to enter key phrases and words to find articles on the examples of AI in finance. I learned the necessity of trust financial firms must have with their clients with the emergence of AI. In addition, I believe the increasing involvement of AI in finance will make it more user-friendly with clients and investors. This can be with decision-making advising and chatbots. AI can serve as a great tool for firms in fraud detection and risk management as well. However, with that, a big concern within AI in finance is the security of data privacy.  Regarding the concern of AI replacing jobs in finance, I need to ensure that my technical and communication skills continue to evolve. AI must serve as a guide in the world of finance and leave the decision-making to humans.

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Argyriou, A. (2025, March 13). Chatting things through: The current state of Chatbots in Mobile banking. The Financial Brand. https://thefinancialbrand.com/news/artificial-intelligence-banking/chatting-things-through-the-current-state-of-chatbots-in-mobile-banking-182950 [Rationale: This article explains how banks are offering AI chatbots and how they work for their clients.]

Guo, M. (2023, March 27). The ethics of AI in Business and Finance. StreetFins®. https://streetfins.com/the-ethics-of-ai-in-business-and-finance/ [Rationale: This article discusses the ethical role of AI in finance and decision-making.]

Kennedy, K. (2024, July 12). How will AI affect entry level finance jobs?. Datarails. https://www.datarails.com/entry-level-finance-jobs-ai/ [Rationale; This article discusses the challenges for people looking to break into entry-level jobs in finance due to the rise of AI.]

Parker, D. (2024, October 4). The future of AI in Financial Services. Forbes. https://www.forbes.com/sites/davidparker/2024/10/03/the-future-of-ai-in-financial-services/ [Rationale: This article discusses the future role of AI in the financial industry.]

Ranjan, P. (2025, January 8). The opportunities and challenges of harnessing AI and ML for Financial Risk Management. Forbes. https://www.forbes.com/councils/forbestechcouncil/2025/01/08/the-opportunities-and-challenges-of-harnessing-ai-and-ml-for-financial-risk-management/ [Rationale: This article discusses how AI is involved with risk management and where it currently stands.]

Sanchez, V. (2023, March 17). The history of Siri and its impact on today’s technology. RoutineHub Blog. https://blog.routinehub.co/the-history-of-siri-and-its-impact-on-todays-technology/ [Rationale: This article provides an overview of AI detecting financial fraud and explains the benefits and challenges faced.]

Sidoti, G. (2024, August 13). The transformative IMPACT OF AI on Financial Services. Forbes. https://www.forbes.com/councils/forbesfinancecouncil/2024/07/08/the-transformative-impact-of-ai-on-financial-services/ [Rationale: This article discusses the overview of how AI finance impacts transactions and investments in firms.]