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It’s very interesting to see how Articicial intelligence is rapidly changing society. Many industry landscapes are transforming including the Finance industry. The financing industry is rapidly evolving and AI is playing a central role. I personally am not deeply invested in finance, but I am considering a future career in fintech. Furthermore, I feel that understanding AI's applications in this field is a crucial first step. AI technologies are reshaping how financial institutions operate with one of the areas I am most interested in being algorithmic trading. Human traders rely on educated guesses based on their knowledge of previous market data and extensive market analysis, but AI powered algorithms can process this large amount of data and execute trades at a speed which humans can’t. This intrigues me because I am interested in how accurate these predictions are and whether these systems can make trading more accessible to a wider audience. Especially since, I know a lot of my peers are exploring investing and trading.

For this padlet wall, I plan to highlight AI applications in fraud detection, algorithmic trading, and credit scoring. I will also explore the future trends such as personalized financial services, ethical considerations like algorithmic bias, and the broader societal imapct of AI in the finance industry. By exploring how AI optimizes these areas I can gain knowledge on how innovation can make finance more accessible. Which I find to be very exciting.

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These days the industry uses machine learning to analyze transaction patterns and find unusual behavior in real-time. According to IBM, banks use AI systems to detect suspicious activity quickly which reduces overall financial losses and protects customer accounts. The technology specifically used is machine learning, anomaly detection, and real time data analytics. The benefits of this is faster detection than previously which in turn improves customer trust and reduces fraud in general. Despite these benefits there are some challenges such as false positives and data privacy concerns. There are also instances where fraud techniques are also evolving to evade the AI pattern recognition which is alarming. Overall, I think AI fraud detection is ultimately a good thing. This is something that affects virtually everyone and I don’t know about other people but I would trust a computer to find a needle in a haystack (the one suspicious transaction out of hundreds of normal ones) much more than a human.

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AI based credit scores assesses creditworthiness using traditional and alternative data. Datrics.ai highlights that AI can help lenders evaluate borrowers more accurately which can improve inclusivity. They use machine learning, predictive modeling, and big data analytic to gauge the borrowers creditworthiness. It can be beneficial because it can give more accurate assessments and reduce the risk of defaulting. It can also make it so more people qualify for loans because it checks more than the traditional data. However, this also opens up vulnerabilities to algorithmic bias, lack of transparency, and data privacy concerns. I find this to also be helpful for making finance more accessible. If the algorithmic bias problem can be solved it would be nice to implement because more people can be eligible for various financial opportunities.

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Algorithmic trading uses AI to automate stock trading using historical and real time data. WorldQuant, a global quantitative investment firm, runs competitions like the International Quant Championship, which had double participation numbers in 2025 because they adopted AI. These competitions make it so students and up and coming traders can create AI-driven trading models. They use machine learning, natural language processing, and predictive analysis to achieve these models. The benefits of these systems is faster trade execution and improved accuracy along with better risk management. Despite this AI algorithms can't really account for market volatility and reliance on algorithms can make it hard for people to react when unpredictable event occur. I think this is very exciting because it makes stock trading and investing more accessible. In these times where many people are struggling to make ends meet stock trading can bridge that gap for many people. Making trading more accessible through personalized AI finance assistants can help a lot of people.

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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The future of AI seemingly brings significant upgrades to the finance industry. This means increased automation of the daily routine tasks and more sophisticated algorithmic trading. Highly personalized financial services is also showing much promise. These services further streamline operations and improve risk management making financial products more accessible for a wider audience. AI could in the future help younger or more inexperienced investors make informed decisions. Additionally AI-driven fraud detection and credit scoring are likely to become even more precise addressing some of the current concerns in their implementation.

In the finance industry ethical considerations are very important. There is a large amount of sensitive data involved in this field and as such data privacy will always be a significant worry. Another big concern is algorithmic bias, AI trained on biased data can unintentionally discriminate based on certain groups. This is especially a crucial concern in lending or insurance. Moreover, over reliance on automated systems in trading or risk assessment has a chance to create systemic vulnerabilities in the financial market.

AI has a potential to greatly improve accessibility and efficiency, but responsible implementation is a must. It will be interesting to see how regulation evolve to address these challenges as technology evolves. The future of AI in the finance industry will probably be a mix of efficiency driven technology and strong ethical regulation and oversight. Hopefully they can ensure that financial systems benefit everyone while also minimizing risks.

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Flinders, M., Smalley, I., & Schneider, J. (2025, September 16). AI fraud detection in banking. IBM. https://www.ibm.com/think/topics/ai-fraud-detection-in-banking

This source provided information on how AI is applied in fraud detection, including the technologies used and the benefits and limitations.

AI drives surge in Worldquant’s University Quant contest participation | reuters. (n.d.). https://www.reuters.com/technology/ai-drives-surge-worldquants-university-quant-contest-participation-2025-10-06/

This source provided details on how AI in influencing algorithmic trading and standardizing access to quantitative trading models.

Datrics. (2023, September 25). Ai credit scoring: The Future of Credit Risk Assessment. Datrics for Financial Services AI-Powered Solutions. https://www.datrics.ai/articles/the-essentials-of-ai-based-credit-scoring

This source explained how AI improves credit scoring and described the technologies involved as well as the benefits and limitations of AI in lending.

Bund Summit 2024 “Navigating a Changing World” Remarks by Tobias Adrian, F. C. and D. of the M. and C. M. D. (2024, September 6). Artificial Intelligence and its impact on financial markets and financial stability. IMF. https://www.imf.org/en/News/Articles/2024/09/06/sp090624-artificial-intelligence-and-its-impact-on-financial-markets-and-financial-stability

This source described the future trends and potential developments of AI in finance and outlined some ethical considerations.

KPMG Global AI in finance report. KPMG. (n.d.). https://kpmg.com/xx/en/our-insights/ai-and-technology/kpmg-global-ai-in-finance-report.html

This source gave me a broader perspective of AI adoption in finance.

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There are both positive and negative societal implications of the implementation of AI in the finance industry. On the positive side, AI can increase accessibility and efficiency in financial services. For example, AI driven credit scoring and personalized banking can allow more people to access loans, investment opportunities, and financial advice. More people will be able to make more informed decisions. On the negative side, AI raises many concerns about employment in the financial industry. Automation is booming and implementing this in financial institutions may displace traditional roles like bank teller, analysts, and back-office staff. Privacy is also a critical issue AI relies on a large quantity of sensitive financial data. Equity is another issue, AI discriminating against groups is a critical failing of these systems. AI systems in the financial industry has the potential to be great but actually putting it into practice can perhaps cause unforeseen issues.

I personally glad to see that industry pushing to become more accessible. However, I don't think many of these AI systems can be implemented currently. AI heavily relies on data, I don't think that we have a large enough dataset for AI to make unbiased decisions. Also, I don't think bias can be completely erased because patterns can be found in almost all human behavior. The potential innovation is very exciting but I can't help but question the actual ability to put it into proactive in a beneficial and fair manner.

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During this assignment I decided on the three AI applications I wanted to talk about and the explored sources relating to those areas. That was really my main challenge, trying to find things that I felt connected/interested in. I learned that a lot of financial institutions are creating personalized financial assistant apps. This was interesting because while I do use some auto investing tools, I didn't know that was powered by AI. Now, it seems like that was obvious but I never really put two and two together. Through this I felt like I need to pay more attention to how AI is changing society as a whole. New technologies are coming out everyday and it seems if you aren't attentive you'll miss the new trends all together.