I chose finance because of my strong interest in fintech innovation and digital banking. As someone passionate about technology and business, I find it fascinating how AI can analyze vast amounts of financial data, helping institutions make better lending decisions, detect anomalies, and improve customer experiences. AI is not only streamlining financial processes but also opening up new opportunities for those who previously struggled to access traditional banking services.

In this Padlet wall, I will explore three key AI applications in finance:

1. Fraud Detection – How AI prevents financial crimes in real-time.

2. Robo-Advisors – AI-driven investment tools that personalize financial planning.

3. Credit Risk Assessment – How AI improves lending decisions while raising ethical concerns.

Through this research, I aim to understand both the benefits and ethical challenges of AI in finance. While AI enhances efficiency, issues like algorithmic bias and data privacy must be carefully addressed to ensure fairness and trust in financial systems. I believe that with responsible AI development, finance can become more inclusive and innovative, shaping the future of banking and investment.

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Using AI in fraud detection provides several benefits. The main advantages are that AI can detect fraudulent activities faster than human analysts, and it can reduce false positives by continuously learning user behavior (Hasan et al., 2022). It can also improve security for online transactions and banking services.

Meanwhile, the technology raises several concerns. AI models can occasionally block legitimate transactions. Discover Global Network Insights (2024) discusses that fraudsters evolve tactics, additionally requiring constant AI updates. Another major challenge is that data privacy concerns arise with AI tracking user behavior when fraud detection is being operated.

I find AI in fraud detection fascinating because it strengthens financial security while improving customer trust. However, ensuring AI models remain fair and adaptive is crucial, as overly strict systems might inconvenience legitimate users. The future of AI in fraud prevention will likely involve deeper integration with biometrics and blockchain, further enhancing security.

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Some benefits can be that it provides low-cost, accessible investment advice for individuals, eliminates emotional biases in financial decision-making, and continuously optimizes portfolios based on market conditions (Yasar, 2025). However, it also faces some challenges. it lacks human intuition and adaptability to unique financial situations. Also, AI-driven models may not always predict extreme market volatility and there is a potential bias in algorithms that favor certain investment strategies (Onabowale, 2024). Additional research must be made to find a solution to these concerns and strengthen the technology for future advancements.

I find robo-advisors exciting because they make investing more accessible to everyday users who may lack financial literacy. However, human advisors are still needed for complex financial planning. In the future, I believe AI will become a hybrid tool, assisting rather than replacing human advisors, allowing for more customized and balanced financial planning.

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This technology expands financial access by evaluating non-traditional data (e.g., utility bills). It also reduces human biases in lending decisions and speeds up loan approval processes, enhancing customer experience (Edunjobi et al., 2024). However, AI models can inherit biases from training data, leading to unfair lending decisions. In addition, Bello (2023) emphasizes that the lack of transparency in AI-driven credit scoring can cause trust issues. Also, regulatory concerns exist over data privacy and fairness in AI lending.

I find AI-driven credit risk assessment impactful because it helps financially underserved individuals access credit. However, ensuring transparency and fairness in AI decisions is essential. In the future, I expect governments to enforce stricter AI regulations, ensuring responsible lending practices while maintaining AI’s efficiency.

However, ethical concerns must be addressed. One major issue is algorithmic bias in lending, where AI models trained on biased historical data could unintentionally discriminate against certain groups, reinforcing financial inequalities. I find this particularly concerning because AI is meant to increase accessibility, not restrict opportunities. Another key issue is data privacy, as AI relies on massive amounts of personal financial data. Without proper safeguards, consumer information could be exploited, leading to security risks. Job displacement is another challenge, as automation replaces traditional finance roles, requiring workers to adapt to AI-driven systems.

From my perspective, the biggest challenge is AI transparency. Many AI models operate as "black boxes," making it difficult for consumers and regulators to understand or challenge financial decisions. This lack of explainability could erode trust in AI-powered financial services. Moving forward, I believe the industry must focus on developing fair, transparent, and accountable AI systems. AI should be used to empower individuals and improve financial inclusion, not create additional barriers. Balancing efficiency with ethics will be critical to ensuring AI benefits everyone in the financial sector.

AI-powered automation is replacing many traditional roles in banking, such as customer service representatives and financial analysts. Robo-advisors and AI-driven credit assessment models reduce the need for human involvement, leading to job losses in certain sectors (Srividya et al., 2024). However, AI also creates new roles in AI development, data science, and cybersecurity, requiring a shift in workforce skills.

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AI in finance relies on vast amounts of user data, raising concerns about data privacy and security breaches. Pashang and Weber (2023) noted that the use of alternative data sources in credit risk assessment—such as social media activity or transaction history—could lead to privacy violations if not properly regulated.

While AI can help eliminate human bias, it can also reinforce biases if the training data is flawed. AI-driven credit assessments, for instance, might unfairly disadvantage certain groups if models are trained on historical data that reflects past discrimination (McBride et al., 2022). This can widen financial inequality, making access to loans and financial services harder for marginalized communities.

AI’s impact on finance is significant, but its long-term success depends on balancing efficiency, fairness, and transparency. As AI-driven finance grows, I believe regulations must evolve to ensure ethical and responsible AI implementation, preventing discrimination and ensuring equitable access to financial services.

One challenge I faced was finding unbiased sources on AI ethics, as companies often promote their AI advancements without discussing potential drawbacks. I overcame this by cross-referencing academic papers and industry reports.

This assignment reinforced my interest in fintech. AI has the potential to make finance more inclusive and efficient, but responsible deployment is necessary to avoid reinforcing economic inequalities. Moving forward, I believe combining AI with ethical governance will shape the future of financial services.