# data[].attributes.body
The financial industry is a dynamic sector that encompasses banking, investment, insurance, and asset management. It plays a crucial role in driving an economy's growth by facilitating capital flow and managing risk. AI is revolutionizing the financial sector by enhancing efficiency, accuracy, and the average customer's experience with financial aid. Applications such as algorithmic trading, credit scoring, fraud detection, and personalized financial advice are transforming traditional practices. By analyzing the trades and other vast datasets, AI can identify patterns and trends that inform better decision-making, ultimately leading to more tailored financial services. My interest in this industry stems from my time investing stocks and how financial decisions affects individuals, businesses, and communities. I am particularly fascinated by the intersection of finance and technology, especially as the world becomes increasingly reliant on online payments.

On my Padlet wall, I will cover the following topics:

1. AI Applications in the Financial Sector: Exploring how AI improves the economy through advanced algorithms and chatbots for customer experience.

2. Future Trends: Speculating on the future of AI in finance and potential ethical considerations.

3. Societal Impact: Speaks on the broader social issues AI will cause through its implementation into the financial sector.

Understanding the impact of AI on our financial decisions is important because it not only shapes economic landscapes but also offers innovative solutions to complex problems like how a person or company balances their books or advising how someone can save towards a fiscal goal. AI also still cannot be fully trusted to make reasonable responses so understanding the impact will empower the individual to make informed decisions. Some advice can be trusted while other advice must be heavily scrutinized.

# data[].attributes.body
AI is applied in aiding fraud detection by the utilization the machine learning algorithms to analyze the vast amounts of transactions and flag and isolate any anomalies. Technologies like supervised learning help models learn from historical data to distinguish between legitimate and suspicious activities. What is extremely interesting about using AI like this is while the human eye is able to discern between legitimate and fraudulent transactions, AI is able to sift through thousands of trades within minutes.

The benefits include significantly reduced losses from fraud and enhanced security for financial transactions. However, challenges persist, such as the requirement for high-quality, labeled training data and the potential for false positives, which can lead to customer dissatisfaction and trust issues. False positives might be less likely when using AI than when using a human.

# data[].attributes.body
AI is transforming credit scoring by employing machine learning models to assess an individual's creditworthiness more accurately with more and organized data. Technologies such as natural language processing analyze these diverse data sources such as social media and transaction history, to enhance credit assessments. This allows more people to be able to build credit even with limited financial history. However, challenges include the potential for bias in algorithms and the lack of transparency in decision-making processes, which can raise ethical concerns about fairness and accountability.

The more data used to build an accurate profile, the more risk there is for sensitive data to be leaked intentionally or not. There is also an issue of using social media specifically as people are not known to make the most responsible decisions when using social media. It compounds with the accidental data leakage problem along with AI taking posts on individual's socials out of context.

# data[].attributes.body
In algorithmic trading, AI leverages machine learning and predictive analytics to execute their trades at the most optimal prices based on the market data. The stock market is extremely violate so trading without loss was difficult until the introduction of AI. Techniques like deep learning analyze vast amounts of market information in real-time, enabling any trader to identify trends and make quick but well informed decisions. AI also helps in making prediction models to see if a company would to profitable to invest in for either the short term or the long term.

The benefits include increased trading efficiency and the ability to capitalize on market fluctuations. The ability to avoid sub optimal deals should not be ignored as many lose their life saving due to one wrong trade. However, challenges include the risk of overfitting models to historical data and the potential for more market volatility caused by automated trading, which can lead to unintended consequences.

# data[].attributes.headline
Future Trends and Ethical Considerations

# data[].attributes.subject
Future Trends and Ethical Considerations

# data[].attributes.body
The future of AI in the financial industry promises transformative advancements driven by increasing data availability and the constant evolution of machine learning models. One notable trend is the rise of extremely personalized financial services, where AI algorithms analyze individual client behavior and their spending patterns to offer customized/optimized investment strategies and financial products. Additionally, enhanced predictive analytics will enable firms to anticipate different market trends and consumer needs, leading to more proactive service offerings. Something similar is already deployed by sites like Amazon but these AI models would offer products not only suited to the user's tastes but also to their price range. Those with higher spending power would be offered luxury products while those with lower spending power would be offer cheaper and better deals. Another significant development is the integration of AI in regulatory compliance, known as RegTech. In simple terms, RegTech is technology used to ensure financial institutions are compliant with regulatory compliance and reporting. AI tools will streamline compliance processes, helping institutions efficiently navigate complex regulations while minimizing risks associated with non-compliance. This capability will be crucial as regulatory frameworks evolve to address more forms of online payment like cryptocurrency.

However, as AI continues to permeate the financial sector, ethical considerations must be at the forefront of these developments. Issues such as algorithmic bias pose significant risks; if AI systems are trained on biased data, they may perpetuate existing inequalities in lending and investment opportunities. Furthermore, the lack of transparency in AI decision-making processes raises concerns about accountability and consumer trust. To address these challenges, financial institutions must adopt ethical frameworks that prioritize fairness, transparency, and accountability. This includes conducting regular audits of AI systems, ensuring diverse and correct data representation, and fostering a culture of responsibility. As the financial industry embraces AI, balancing innovation with ethical considerations will be essential to create a fair and equitable financial landscape for all stakeholders.

# data[].attributes.body
The broader societal impact of AI in the financial industry is all encompassing. IT affects employment, privacy, equity, and other facets of society. As AI automates routine tasks such as data entry and transaction processing, it raises concerns about job displacement. Most likely those with skills that can be outsourced will be replaced. While some roles may become obsolete, new opportunities will emerge in AI management, data analysis, and compliance, necessitating a workforce heavily skewed towards being skilled in technology. There needs to be work programs geared towards reskilling those displaced so they can find employment in these new fields.

Privacy is another critical issue, as AI systems often require vast amounts of personal data to function effectively. Financial institutions must strike a balance between utilizing data for enhanced services and protecting customer privacy. Robust data governance frameworks are essential to ensure that consumers’ information is handled responsibly and with a healthy amount of transparency. Unique to the financial sector is the potential issue of destabilized markets or the economy. AI can make errors similar to humans, and society often places undue trust in AI outputs. This over-reliance can lead to misevaluations of stocks, resulting in serious consequences. The implementation of AI in the financial industry must be done at a steady and careful pace lest unnecessary consequences occur.

# data[].attributes.body
During my research on AI in the financial sector, I discovered the transformative potential of AI technologies, particularly in fraud detection and investment management. A significant challenge was navigating the vast amount of literature and understanding what bias existed in the literature. I overcame this by focusing on peer-reviewed articles and recent studies. Finding credible videos was also an issue due to how few had references to any studies or how long some were. I decided to find videos from those with some form of background in both finance and AI. The impact of AI on efficiency and decision-making in finance is profound, yet it raises critical ethical concerns, such as data privacy and bias. This research deepened my understanding of AI's dual nature: it offers immense benefits while requiring careful governance to mitigate associated risks and how AI is only as good as its user.

# data[].attributes.body
Here’s the alphabetized list of the references:

1. Adhikari, P., Hamal, P., & Jnr, F. B. (2024). Artificial Intelligence in fraud detection: Revolutionizing financial security. International Journal of Science and Research Archive, 13(1), 1457–1472. https://doi.org/10.30574/ijsra.2024.13.1.1860

2. Chen, M., Honarvar, I., & Lohre, H. (n.d.). The current state of AI for investment management. Retrieved July 26, 2025, from https://assets.ctfassets.net/tl4x668xzide/5tYd1K2cq0p3SbNRWXulmw/e130297205b1f4481041297451315c54/20231231-the-current-state-of-ai-in-asset-management.pdf

3. Pattnaik, D., Ray, S., & Raman, R. (2024). Applications of artificial intelligence and machine learning in the financial services industry: A bibliometric review. Heliyon, 10(1), e23492. https://www.sciencedirect.com/science/article/pii/S2405844023107006

4. Ridzuan, Nurhadhinah Nadiah, et al. (2024). AI in the Financial Sector: The Line between Innovation, Regulation and Ethical Responsibility. Information, 15(8), 432. https://doi.org/10.3390/info15080432

5. Svetlova, E. (2022). AI ethics and systemic risks in finance. AI and Ethics, 2. https://doi.org/10.1007/s43681-021-00129-1

6. greggwirth. “How AI Will Disrupt Fraud Prevention & Detection Technologies - Thomson Reuters Institute.” Thomson Reuters Institute, 23 Dec. 2024, www.thomsonreuters.com/en-us/posts/corporates/technological-considerations-fraud-prevention. Accessed 26 July 2025.

# data[].attributes.attachment_caption
AI in finance and ethical considerations 