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Artificial intelligence in finance uses algorithms (machine learning, predictive analytics, and natural language processing) to automate tasks such as fraud detection, investment management, and customer service. Recent reviews highlight that AI’s role in finance has expanded across every major sector of banking and fintech (Nature, 2025).

I decided to write my assignment on finance because I have always been passionate about entrepreneurship and with the advent of AI, I have also become interested in learning about how this new technology is shaping the financial world. Every business no matter it's size depends on financial stability and systems they can trust. Businesses that can properly integrate AI systems will achieve both a stabler financial environment, and trustworthy systems that can help their business to get ahead. The businesses that do not make this integration will fall behind, and eventually disappear.

In my padlet wall, I will cover three major-applications of artificial intelligence in finance:

Fraud detection using machine-learning

Allows banks and payment platforms to identify suspicious activity.

AI-Driven 'Robo-advisors' in investment management

Helps users build personalized financial portfolios based on algorithms and data, not emotion.

NLP-powered customer service chat-bots

Transforming how financial institutions are communicating with their clients, by offering 24/7 service.

Finance is where money meets technology, and the evolution of artificial intelligence will determine what direction this relationship goes in.

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PayPal is a financial transaction platform that allows users to transfer money between two parties, securely. The app uses machine learning and neural networks to scan millions of transactions, identifying patterns that might indicate fraud. This integration of deep learning and anomaly detection has improved PayPal’s fraud prevention process, enabling real-time monitoring, fewer false negatives, and stronger consumer protection.

This video explains exactly how AI detects fraud:

https://www.youtube.com/watch?v=ABY16Fetq3U

Research shows that AI-based fraud detection systems outperform traditional rule-based methods in both accuracy and response speed (International Journal of Scientific Research and Analysis, 2024)

https://ijsra.net/sites/default/files/IJSRA-2024-1860.pdf

Before AI, PayPal relied on manual reviews and rule-based systems, which were slower and significantly less scalable. Now, automated models detect suspicious behavior almost instantly. Despite this, challenges remain, as data privacy concerns, algorithmic bias, and occasional false positives still exist.

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JPMorgan Chase is a Titan in the finance industry, and they have also began to integrate AI into their structure. COiN is a platform developed by JPMorgan that uses NLPs to review legal documents.

The advent of COiN has revolutionized the review process of legal documents, where as normally the review and interpretation process takes upwards of 360,000 hours annually, now can be done rapidly, and with a near 0% error rate, according to an article from the CTO Magazine (CTO, 2024)

https://ctomagazine.com/jp-morgan-chase-accelerates-ai-adoption/

The benefits of this AI implementation include a drastically reduced time and cost in processing these documents, as well as improvements to accuracy. Despite these improvements however, challenges still include risks such as overdependence on algorithms, and potential data privacy issues.

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Betterment is an investment company that uses AI to manage investment portfolios for their clients. The algorithms they use rely on predictive modeling and reinforcement learning to track market behavior, and adjust investments to help clients bring in higher returns.

AI has given Betterment several benefits including lower fees, easier access to investing, and reduced emotional bias from users. Despite these benefits, these systems don’t have human intuition, over-rely on data, and many investors are untrustworthy of its decisions, as there is a lack of transparency in how the algorithm makes its decisions.

This video touches on how platforms like Betterment have opened doors for people who could not previously obtain quality investing information, while also detailing this mistrust many more wealthy people have in AI, when dealing with their money:

https://www.youtube.com/watch?v=jFsRosUWyYE

Studies from the Financial Planning Association and the National Library of Medicine show that while robo-advisors do improve accessibility, many users still question their transparency and reliability (Financial Planning Association, 2024; PMC, 2024).

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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AI has already vastly changed the landscape of the finance industry, with new technologies being created every day. Fraud detection, automated predictive investing, and document processing are only at the beginning of what is to come. As AI advances, new technologies will become mainstream and change the financial industry even more. With the rapid development of new AI technologies, new questions regarding ethical use such as privacy, transparency, and algorithmic bias are arising.

Some of the main ethical concerns regarding the technologies talked about so far in this padlet involve:

Determining whether or not an AI system is trustworthy based on it's transparency when making decisions

The sharing of private information in regards to sharing documents for AI to analyze, as well as when analyzing transaction history on platforms like PayPal

Future AI trends that will likely follow as a response to these concerns include:

Federated Learning for Data Privacy, where banks collaborate to detect fraud without leaking customer data (Lucinity, 2024).

Hybrid 'robo-advisors' where an AI investor will be matched with a human partner to oversee and manage decision making, making way for trust to be built with new AI investment platforms (PMC, 2024)

Explainable AI (XAI) is a corporation started by Elon Musk that is helping customers understand why an AI system makes certain decisions, such as financial investment recommendations. This will help to create more transparency between AI and human, further building a trustful relationship (ScienceDirect, 2023).

One can also expect stricter policy globally on how AI can be used in the financial industry.

I personally believe that the future of finance depends on balancing AI's innovations with ethical use. AI brings many benefits to finance, but must be used fairly and transparently. If we as a society rush ahead and do not create guidelines for fair use, humans will end up serving technology, and not the other way around.

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For this project, I researched the impact that AI has had on the finance industry. Being someone interested in finance, I was naturally drawn to this topic over the other options, and decided to explore tools like PayPal's automated fraud detection, Betterment's AI-powered investment, and JPMorgan Chase's 'COiN' AI document processor.

A challenge that I faced when doing my research was finding credible sources, that described concepts to me in simple, digestible terms. In learning the language of AI, and understanding these concepts, I have curated a stronger grasp on how machine learning models actually work.

Originally, I thought AI could only bring net positives to the industry, but with the rate of advancement and deployment of AI systems and tools, challenges have arisen such has job replacement due to automation and poor trust in some AI systems.

Overall, I believe with more regard for ethical use, AI will be a massive benefit for the industry as a whole.

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AI has permanently changed the finance industry in only a few years. From fraud detection to investment management, it’s now intertwined in almost every financial transaction, affecting businesses, workers, and the economy as a whole. While these innovations bring new opportunities, they also raise many social and ethical concerns.

Employment:
AI automation has replaced repetitive tasks like manual fraud detection, document review, and basic customer service. These systems perform faster and on a larger scale but often at the cost of human jobs. For example, JPMorgan’s 'COiN' platform, mentioned before, replaced over 360,000 hours of contract review with AI automation. However, new positions are emerging in AI oversight, data ethics, and cybersecurity, which are all fields that require human oversight.

Data Privacy:
AI depends on massive datasets that include users' private financial information. Every transaction leaves a digital footprint that could be leaked or sold without user consent. A possible solution is federated learning across banks, like mentioned previously. Data-privacy and cybersecurity are one of the biggest concerns regarding ethical use in AI, and stricter policies regarding data usage and transparency will need to be created and enforced to build a stronger sense of trust in these systems.

Equity:
AI has made investing more accessible by lowering the cost of financial advice through robo-advisors like Betterment. This innovation gives more people access to professional investment tools once reserved for wealthier people. Despite these benefits, algorithmic bias remains a concern, as some models may unintentionally disadvantage certain groups in credit scoring or loan approvals.

The more I learn about AI in finance, the more I realize its impact reaches far beyond profits and losses. Jobs, privacy, and fairness are all on the line. As a society, we need to establish safeguards and ethical standards now to prepare for the transformations still to come.

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1.
CTO Magazine. (2024). JP Morgan Chase accelerates AI adoption. https://ctomagazine.com/jp-morgan-chase-accelerates-ai-adoption/
Provides verified details on JPMorgan’s COiN platform, showing how AI reduced 360,000 hours of document review and improved efficiency.

2.
Financial Planning Association. (2024). Customer trust and satisfaction with robo-adviser technology. https://www.financialplanningassociation.org/learning/publications/journal/AUG24-customer-trust-and-satisfaction-robo-adviser-technology-OPEN
Examines investor perceptions of robo-advisors, focusing on trust, transparency, and satisfaction with AI-based portfolios.

3.
International Journal of Scientific Research and Analysis. (2024). Artificial intelligence in fraud detection: Revolutionizing financial systems. https://ijsra.net/sites/default/files/IJSRA-2024-1860.pdf
Explains how machine-learning models improve accuracy and speed in detecting financial fraud, relevant to PayPal’s AI systems.

4.
Lucinity. (2024). Federated learning in FinCrime: How financial institutions can fight crime without sensitive data sharing. https://lucinity.com/blog/federated-learning-in-Describes privacy-preserving AI methods that allow banks to collaborate on fraud detection without exposing customer data.

5.
Malik, R., & Sethi, D. (2023). Implementing artificial intelligence empowered financial advisory. ScienceDirect. https://www.sciencedirect.com/science/article/pii/S0148296323008536
Discusses how NLP and automation are being integrated into financial advisory systems such as JPMorgan’s COiN.

6.
Nature. (2025). A scientometric review of artificial intelligence in finance. https://www.nature.com/articles/s41599-025-04850-8
Offers a comprehensive peer-reviewed overview of AI’s applications, benefits, and ethical challenges across global financial sectors.

7.
National Library of Medicine. (2024). The emerging field of robo-advisor: A relational analysis. https://pmc.ncbi.nlm.nih.gov/articles/PMC11382191
Provides scholarly insight into how AI-driven robo-advisors function, highlighting advantages, risks, and their role in modern investing.