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As an Economics major at Stony Brook University, I’ve always been fascinated by the intersection of mathematics, finance, and technology. My academic journey has been shaped by a strong passion for mathematical thinking, how numbers, patterns, and data can help us understand and predict the world. That’s why I chose to focus on the finance industry for this project: it represents one of the most dynamic and data driven fields where artificial intelligence is already making a profound impact.

Finance is a natural fit for AI because the industry thrives on high volumes of complex, real time information, exactly the kind of environment where AI excels. From predicting market behavior to managing financial risk and optimizing investments, AI is reshaping how financial institutions operate. I’m particularly interested in how AI can be used to forecast the stock market, interpret economic indicators, and generate insights that would be impossible to uncover using traditional tools alone. As someone who values both quantitative analysis and real world application, I see AI as a bridge between theoretical economics and actionable financial strategy.

In this Padlet project, I’ll explore three key AI applications in finance: fraud detection, robo-advisory investment tools, and customer service chatbots. Each of these examples showcases different technologies such as machine learning, predictive analytics, and natural language processing. I’ll also discuss future trends and ethical concerns, like bias in algorithms, data privacy, and transparency in decision-making, and reflect on the broader societal impact of AI in the finance sector.

This topic is personally significant to me because I believe the future of economic understanding will increasingly depend on the integration of AI. The ability to synthesize vast amounts of data and uncover hidden patterns has the potential to revolutionize how we understand markets, risk, and global financial trends.

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Finance – AI in Fraud Detection

AI powered fraud detection platforms, like those used by PayPal, employ machine learning algorithms to monitor, learn from, and flag transactional behavior in real time. These systems analyze thousands of variables including device usage, geolocation, transaction speed, and behavioral patterns to distinguish legitimate activity from fraud (PayPal Editorial Staff).

The benefits of this technology are impressive. They are remarkably efficient in detecting fraud before it occurs, significantly reducing chargebacks and protecting customer trust (Saran). These models also improve over time, adapting to new threats without requiring manual rule setting (Bailey).

Despite their power, these systems often suffer from opacity, users don’t always know why transactions are flagged. Additionally, the models require large, clean datasets and can still misidentify legitimate users as fraudulent, especially if the user deviates slightly from usual patterns (Bailey).

Personal Insight: As someone passionate about mathematics and finance, I find this application of AI compelling because it transforms raw data into real world protection. It highlights how probabilistic thinking and machine learning can safeguard entire economies from cyber threats.

Works Cited

Bailey, John. How AI Is Used in Fraud Detection. IBM, 2023, https://www.ibm.com/blogs/industries/ai-fraud-detection/.

PayPal Editorial Staff. “How PayPal Uses AI to Combat Fraud.” PayPal Stories, 2022, https://newsroom.paypal-corp.com/How-PayPal-Uses-AI-to-Combat-Fraud.

Saran, Cliff. “How AI Is Revolutionising Financial Fraud Detection.” ComputerWeekly, 12 Oct. 2022, https://www.computerweekly.com/feature/How-AI-is-revolutionising-financial-fraud-detection.

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Finance – AI in Robo-Advisory Investing

Robo-advisors like Betterment, Wealthfront, and Vanguard use AI algorithms and quantitative models to offer personalized, low cost investment advice. These systems gather user data, such as financial goals, age, and risk tolerance, to create automatically rebalancing portfolios and tax efficient strategies (Betterment).

These systems bring several advantages. By removing emotional decision making and automating trades, robo-advisors increase access to disciplined, data-driven investing. They reduce costs dramatically compared to traditional human advisors and are available 24/7, appealing especially to younger, tech savvy investors (Marr).

However, robo-advisors may miss nuanced life circumstances like major purchases, job changes, or unique ethical investing preferences. Furthermore, some users may lack trust in an entirely algorithmic system and miss the reassurance of human guidance (PWC).

Personal Insight: As an economics major, I find robo-advisors especially interesting because they combine finance theory, statistics, and machine learning in a real world tool that reshapes how people grow wealth. They bring abstract models from the classroom into practical use, helping individuals make smarter financial decisions.

Works Cited

Betterment. “How Robo-Advisors Work.” Betterment, https://www.betterment.com/resources/how-robo-advisors-work.

Marr, Bernard. “How AI Is Powering Robo-Advisors And Changing Wealth Management.” Forbes, 17 Jan. 2023, https://www.forbes.com/sites/bernardmarr/2023/01/17/how-ai-is-powering-robo-advisors-and-changing-wealth-management/.

PWC. Asset and Wealth Management Revolution: The Power to Shape the Future. PWC Global, 2023, https://www.pwc.com/gx/en/industries/financial-services/assets/pwc-awm-revolution-2023.pdf.

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Finance – AI Chatbots in Banking

AI powered chatbots are becoming essential tools for customer service and internal operations in finance. JPMorgan’s COiN uses machine learning to review complex legal documents in seconds, while Bank of America’s Erica employs natural language processing (NLP) to answer client questions, provide account updates, and offer budgeting suggestions (Ahmed Raza; Bank of America).

Benefits include 24/7 availability, resolution of millions of queries, and freeing up human staff for more nuanced customer needs. COiN alone has saved JPMorgan over 360,000 hours by automating contract review, an incredible efficiency gain (Ahmed Raza).

Still, these AI systems struggle with understanding tone, sarcasm, and highly specific or emotional questions. Miscommunications can damage customer trust, and maintaining and training these bots requires significant data input and ethical oversight (Gobeyond.ai).

Personal Insight: I’m fascinated by how AI can simulate parts of human interaction while also delivering performance improvements on a massive scale. It’s a powerful example of how economics, technology, and customer psychology intersect in modern finance.

Works Cited

Ahmed Raza. “AI Chatbots in the Finance Industry: Use Cases and Benefits.” BusinessTechWeekly, 15 Mar. 2023, https://www.businesstechweekly.com/ai-in-finance/chatbots-in-finance/.

Bank of America. “Meet Erica, Your Virtual Financial Assistant.” Bank of America Newsroom, 2023, https://newsroom.bankofamerica.com/content/newsroom/press-releases/2023/04/meet-erica--your-virtual-financial-assistant.html.

Gobeyond.ai. “How Financial Institutions Can Improve Customer Service with AI Chatbots.” Gobeyond.ai, 2022, https://www.gobeyond.ai/resources/chatbots-in-financial-services/.

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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As artificial intelligence continues to evolve, its role in the finance industry will likely become even more transformative. In the near future, we can expect AI to drive personalized financial services, customizing investment strategies, credit offerings, and budgeting tools to individual users in real time. Algorithms will increasingly integrate more diverse data points like social media behavior, real time location, and spending history to create more accurate financial profiles and risk assessments. Quantum computing could also accelerate AI’s ability to model financial markets and predict economic shifts with greater precision.

However, this rapid advancement comes with serious ethical considerations. A primary concern is algorithmic bias, especially in credit scoring and lending decisions. If the data used to train AI systems reflects historical inequalities, then these systems can perpetuate or even worsen discrimination, denying loans to qualified applicants based on skewed metrics. Another major issue is data privacy. As AI becomes more data hungry, financial institutions must walk a fine line between innovation and respecting user consent and confidentiality.

Personally, as an economics major with a passion for mathematics and technology, I find this intersection between AI and finance both exciting and cautionary. The potential to improve financial access, efficiency, and planning is enormous. However, it’s equally important that these tools are used responsibly. Transparency, human oversight, and ethical AI design should not be afterthoughts, they must be central to any future development.

Ultimately, AI has the power to democratize finance and make it more intelligent, but only if it's developed with fairness, security, and inclusivity in mind. As someone preparing for a future in this field, I hope to be part of the movement that ensures AI is used not just intelligently, but ethically as well.

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AI’s integration into the finance industry has brought both significant benefits and complex societal challenges. One of the most visible impacts is on employment. AI powered systems can now perform tasks like fraud detection, customer service, and portfolio management with impressive speed and accuracy. While this leads to increased efficiency and lower costs for financial institutions, it also raises concerns about job displacement, particularly in mid level roles such as analysts, loan officers, and customer support staff. At the same time, new jobs are being created in AI development, data science, and ethical oversight, but these often require advanced technical skills, contributing to a growing skills gap.

Privacy is another pressing concern. AI systems rely heavily on personal financial data to deliver services like credit risk modeling and robo-advising. This level of data dependency increases the risk of breaches and unethical surveillance if not properly regulated. Consumers may not always be aware of how much of their information is being collected or how it’s used in decision making processes.

Equity and fairness also come into play. Without careful design, AI can unintentionally reinforce existing financial inequalities. For instance, predictive models trained on biased data may offer worse loan terms to marginalized groups or deny access altogether. This could widen the economic divide rather than close it.

From a personal standpoint, I view AI in finance as a powerful tool with the potential to improve lives, but only if used responsibly. The societal implications make it clear that we need strong governance, transparency, and public awareness. As someone studying economics, I believe the focus should be on creating inclusive financial technologies that uplift communities, not just profit margins. Understanding these broader impacts shapes how I think about ethical innovation and the responsibilities future professionals like myself will carry.

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Bailey, J. (2023). How AI is used in fraud detection. IBM. https://www.ibm.com/blogs/industries/ai-fraud-detection/
[Rationale: Provides an in-depth explanation of AI techniques in fraud detection, which is essential for understanding practical applications in finance.]

Betterment. (n.d.). How robo-advisors work. Betterment. https://www.betterment.com/resources/how-robo-advisors-work
[Rationale: Offers clear insights into how robo-advisors use AI to personalize investing, supporting the section on AI-driven wealth management.]

Marr, B. (2023, January 17). How AI is powering robo-advisors and changing wealth management. Forbes. https://www.forbes.com/sites/bernardmarr/2023/01/17/how-ai-is-powering-robo-advisors-and-changing-wealth-management/
[Rationale: Explores current trends and future potential of AI in investment management, enriching the analysis of robo-advisors.]

PayPal Editorial Staff. (2022). How PayPal uses AI to combat fraud. PayPal Stories. https://newsroom.paypal-corp.com/How-PayPal-Uses-AI-to-Combat-Fraud
[Rationale: Details a leading company's approach to AI-based fraud prevention, grounding the fraud detection example.]

PWC. (2023). Asset and wealth management revolution: The power to shape the future. PwC Global. https://www.pwc.com/gx/en/industries/financial-services/assets/pwc-awm-revolution-2023.pdf
[Rationale: Provides a comprehensive report on AI’s impact in asset management and finance, useful for future trends discussion.]

Saran, C. (2022, October 12). How AI is revolutionising financial fraud detection. ComputerWeekly. https://www.computerweekly.com/feature/How-AI-is-revolutionising-financial-fraud-detection
[Rationale: Highlights AI challenges and benefits in fraud detection, adding depth to the ethical considerations.]

Ahmed Raza. (2023, March 15). AI chatbots in the finance industry: Use cases and benefits. BusinessTechWeekly. https://www.businesstechweekly.com/ai-in-finance/chatbots-in-finance/
[Rationale: Explains the growing role of AI chatbots in financial customer service, supporting the chatbot section.]

Bank of America. (2023). Meet Erica, your virtual financial assistant. Bank of America Newsroom. https://newsroom.bankofamerica.com/content/newsroom/press-releases/2023/04/meet-erica--your-virtual-financial-assistant.html
[Rationale: Describes a prominent AI chatbot in banking, illustrating real-world application.]

Gobeyond.ai. (2022). How financial institutions can improve customer service with AI chatbots. Gobeyond.ai. https://www.gobeyond.ai/resources/chatbots-in-financial-services/
[Rationale: Discusses ethical and operational challenges of AI chatbots in finance, contributing to societal impact insights.]

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Researching AI applications in finance has been an eye opening experience. Initially, I found it challenging to navigate the vast amount of information and identify credible sources that specifically addressed real world AI use cases. To overcome this, I focused on well known companies and trusted publications, which helped narrow down the scope and ensure reliability.

Through this process, I learned how deeply AI is transforming finance, not only improving efficiency, but also raising important ethical and societal questions around bias, privacy, and employment. I was particularly interested in how AI combines advanced mathematics and economics to make complex predictions and automate decision making.

This research broadened my understanding of AI beyond just technology; it’s a powerful force that shapes people’s financial lives and the economy at large. It reinforced my belief that responsible development and oversight are essential to ensure AI benefits everyone fairly. Overall, this project has deepened my appreciation for the intersection of AI, finance, and ethics.