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I chose the finance industry because money and technology are important aspects of our daily lives. I've always been curious about how people manage, save, and spend their money, and how technology is making these processes faster and easier. Artificial intelligence (AI) has transformed the way the finance industry operates, from online banking to credit decisions. I think it's amazing how computers can now perform tasks that previously required human judgment, such as detecting fraud or providing investment advice.
AI in finance is used in a variety of ways. For example, it enables people to invest through robot advisors, protects customers from scams with fraud detection systems, and makes credit scoring more balanced by utilizing new types of data. These tools make financial services more accurate, safe, and accessible to a wider range of people.
On my Padlet wall, I intend to demonstrate how each of these AI tools works, the technology that powers them, and how they benefit both businesses and customers. I'll also discuss the challenges associated with using AI in finance, such as data privacy and bias.
I chose this industry because I believe that everyone should know how technology affects their finances. Understanding AI in finance demonstrates how powerful it can be when used responsibly. Personally, I find it inspiring that artificial intelligence can help make the financial world more equitable and accessible to all, not just those with a lot of money or experience.

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Robot advisors – how AI helps people invest automatically.

Fraud detection – how machine learning protects customers from scams.

Credit scoring – how AI models assess financial risk using alternative data.

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Robot advisors are online tools that allow people to invest their money automatically. They ask about your goals, risk tolerance, and investment time horizon before creating and managing a portfolio for you. These systems use machine learning to analyze user data, algorithms to make informed investment decisions, and, in some cases, chatbots with natural language processing to answer questions. Robot advisors make investing more affordable and convenient for everyone, track investments around the clock, and eliminate human errors and emotions. However, they lack a thorough understanding of personal needs, struggle during major market changes, and some users continue to distrust computer based advice. However, I believe that Robot advisors make investing easier for beginners, which I appreciate. It's great that anyone can grow their money without the need for a financial advisor. I believe that in the future, people will combine robot advisors with real humans to achieve the best of both worlds.

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Robo-Advisors vs. AI in Financial Planning: What You Need to Know

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Banks use artificial intelligence to detect and stop fraudulent or suspicious transactions. It monitors millions of payments in real time for errors such as large transfers or unusual spending. AI detects signs of fraud using machine learning, anomaly detection, and in some cases, neural networks. It can quickly detect scams, protect people's money, and save banks a significant amount of money. However, AI can make mistakes, accidentally block legitimate purchases, and must be updated frequently to remain accurate. This is interesting to me because AI acts as a digital guard, keeping people's money safe, but it can also cause problems when it accidentally blocks honest transactions.

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Generative AI for Scam and Fraud Detection in Banking

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believe AI in credit scoring is critical because it can help more people obtain loans, including those with little or no credit history. It speeds up decision making, lowers the risk of bad loans, and improves access to funds for those in need. However, AI can have drawbacks. It may use biased or unfair data, be difficult to understand, violate privacy, or make incorrect decisions. This interests me because it demonstrates how AI can do a lot of good for this industry, but it must be used with caution and fairness.

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Supercharging Credit Application with Generative AI

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Future Trends and Ethical Considerations

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Future Trends and Ethical Considerations

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In the future, AI will continue to transform the finance industry, making banking faster, smarter, and more personalized. AI could provide advice based on individual spending and saving habits, detect fraud faster, and assist more people in obtaining loans by utilizing data sources other than traditional credit history. AI can also detect risky behavior, prevent money laundering, and enhance security for both banks and customers. These advancements may make financial services more accessible, efficient, and secure for all. However, there are significant ethical concerns to address. AI relies on vast amounts of personal financial data, which raises privacy concerns. AI systems can also be biased, denying loans or financial services to certain people. As AI automates more tasks, some finance jobs may be replaced, impacting workers. Transparency and accountability are critical for ensuring people can trust AI decisions. From my perspective, the future of artificial intelligence in finance is exciting because it has the potential to make banking more fair, efficient, and accessible. At the same time, AI systems must be carefully monitored to avoid bias, protect privacy, and ensure fairness. Balancing innovation and responsibility will determine whether AI benefits everyone in the financial industry or just a small group of people.

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AI is transforming the finance industry, and its effects extend beyond banks and customers. One significant impact is on employment. As AI automates tasks like fraud detection, investment advice, and credit scoring, some finance jobs may be reduced or eliminated. While AI improves efficiency, it also means that workers must adapt by learning new skills or taking on roles that require human judgment and creativity. Privacy is another major concern. AI makes extensive use of personal financial data, including spending habits, account information, and payment history. If this information is misused or leaked, it may endanger individuals and undermine trust in financial institutions. AI also has implications for equity and fairness. Algorithms can be unintentionally biased, denying loans or services to specific groups. Companies must monitor AI systems to ensure that decisions are made fairly and transparently. Beyond employment, privacy, and equity, AI impacts society by increasing financial accessibility. It can help people with little credit history access loans, provide personalized investment advice, and protect customers from fraud. From my perspective, these societal implications make AI both exciting and challenging. I see the potential for AI to improve financial services, but I also understand the need to carefully manage its risks. Overall, AI's societal effects demonstrate that technology can benefit people if used thoughtfully and ethically.

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During my research into AI in finance, I discovered how it is transforming banking through fraud detection, credit scoring, and robot advisors. Finding credible sources was a challenge for me, so I used Wikipedia as a starting point to help me identify key topics to research further from trustworthy sources. I discovered that while AI can improve the speed, accessibility, and safety of financial services, it also raises ethical concerns such as privacy, bias, and job displacement. This research taught me that AI is powerful but must be used responsibly, and I now recognize both its advantages and disadvantages in finance.

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McKinsey & Company. (2023). This article provides a detailed analysis of how AI is being used in the banking industry, including practical examples, benefits, and challenges. It is useful for understanding current trends, real world applications, and the overall impact of artificial intelligence on financial services.

Investopedia. (2025). This article explains how robot advisors use artificial intelligence to manage investments, as well as their benefits and limitations. It helps explain the role of AI in personal finance and how technology is making investing more accessible and efficient.

Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., & Rodríguez Barrero, M. S. (2024). This article discusses multiple studies on how AI and machine learning are used to detect financial fraud. It explains the most common methods, data types, and ways to measure success. The review shows trends, including most studies focusing on credit card fraud and fewer using simulated data for testing. This source is useful for understanding how AI is improving fraud detection in finance and which techniques are most effective.

Chen, Y. (2025). This article provides an in depth review of how deep learning is used to detect financial fraud, including the most effective models, datasets, and techniques. It highlights the trends and challenges of using AI to prevent fraud. This source is useful for understanding the role of advanced AI techniques in improving financial security, as well as how technology is assisting banks and customers in more effectively identify and avoid fraudulent activities.

Robert, Jr., J. (2024). This article discusses the impact of AI enabled credit scoring on financial inclusion, with a focus on an underserved population of more than one million people. It explains how artificial intelligence can improve credit access for people who have traditionally been excluded from financial services. This resource is useful for understanding how AI promotes equity and expands financial opportunities.