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There are many reasons I chose finance as my industry for this project. The first reason is that I'm majoring in finance, so this is the industry I plan to go into. I have always been interested in the financial industry while I was growing up. Growing up, I always wanted to make money, and this is the perfect field to go into for that. With financial knowledge, you will be able to make smart investments, predict market trends, and protect your own money.  In this Padlet project, we will go over the examples of AI use in the finance industry. This will also cover the future trends and ethical considerations of AI and its social impacts. Lastly, it will conclude with an overview video and a reflection of the whole project. The financial industry has seen rapid integration of AI technology in the past years, and it's not stopping. AI applications in the finance industry are very important to me because they can change how the whole industry operates. Learning about these AI applications can be the difference between you getting a job and not. New technology and AI applications will be released, and getting familiar with these will give you an advantage in the financial job market. I see AI as a game-changer that betters financial decision-making while also raising ethical and regulatory questions that must be addressed.

As someone who is interested in AI and finance, it is great to see how the two can work together to make more money.

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Algorithmic trading is when stock investors use algorithms that make decisions based on historical data to execute trading decisions. The algorithm analyzes vast datasets, identifies patterns, and makes rapid trading decisions to exploit market inefficiencies and maximize profits. These algorithms apply machine learning and deep learning to analyze market trends and financial news before making trades. This benefits the financial industry because these algorithms run through a billion possible solutions before picking the best option. This gives you an advantage over the companies not using algorithmic trading because you are able to see the billions of possibilities, and you have the best one. One challenge this brings is that the algorithm has to be updated constantly with the latest financial data.

https://youtu.be/73fnrywIhl8?si=5w0mz6ITCyLEYdZ1

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Companies have started to implement AI algorithms to analyze credit scoring and manage risk. 1.5 billion people around the world do not have access to the services of a bank or similar financial institution. AI-based credit scoring is the most promising and relevant solution. Credit scoring evaluates how well a bank's customer can pay and is willing to pay off debt. AI-based credit scoring transforms traditional credit assessments by utilizing advanced machine learning algorithms that evaluate an extensive range of data points, significantly surpassing the scope of conventional scoring methods. One benefit is Inclusivity; AI can provide credit scores for individuals with little to no traditional credit history, thus improving financial inclusion. But the main criticisms revolve around the opacity of machine learning models and the potential for biased decisions.

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AI-Powered Credit Scoring: Transforming the Future of Lending and Borrowing

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AI fraud detection utilizes machine learning algorithms to detect fraudulent activities within large datasets by identifying patterns and anomalies indicative of fraud. As these algorithms continuously learn from incoming data, their predictive accuracy steadily improves, enabling them to quickly adapt to emerging fraudulent strategies. This proactive approach provides businesses with a robust, technology-driven tool to ensure transaction integrity and strengthen security measures. AI-driven fraud detection systems provide significant benefits for businesses aiming to protect themselves from continuously evolving threats. By leveraging artificial intelligence, companies can enhance security, boost operational efficiency, and improve customer experience. These advantages include Cost reduction, Real-time detection and prevention, Customer trust, and satisfaction. Some challenges include False positives, customer friction, and Integration with existing systems.

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Don’t Let Scammers Win! - How AI Helps in Fraud Detection

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When talking about the future trends of AI, you have to look at the short and long-term considerations. One short-term trend you have to take into consideration is the automation of data processing. Data processing is a long, drawn-out process that AI applications can do in seconds. AI can be used to process emails, documents, and multimedia to get information, this will largely cut down manual data processing. Another short-term trend in the financial industry is that AI will track financial news, social media trends, and market sentiment to detect risks and opportunities. This can be done faster by AI than any human analyst. AI can possibly use this information to automatically make trades, which puts the institution ahead of people who do not use AI. Now, we have to look at the long-term trends and developments that we will see in the future. One trend is that AI will play a huge role in fraud protection in the future. One example of this being used currently is Neobank using AI to improve fraud detection. Another future trend is the use of synthetic AI data to practice financial models. This will help the company make better trades. One ethical consideration we have to take into account is the bias that AI might demonstrate during fraud protection. While bias might occur right now in AI, companies have to keep on working on a solution to solve AI bias. Personally I think that AI bias is the most important thing we have to fix. If we are relying on AI to make important decisions, it is best if the AI acts fairly towards everyone in society.

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The integration of AI and the development of AI applications in the financial industry will bring societal impacts to the industry. One thing AI will impact is customer trust and privacy. Using AI will improve customer service by using hyper-personalization, robo-advisors, fraud detection, and AI-powered customer service. One concern this brings is that hyper-personalization requires immense personal data to work, which brings up the problem of data privacy. The bias the AI generates will also impact society in the finance industry. AI could make money stuff easier for everyone, but it also risks exacerbating existing inequalities. AI will be able to provide access to financial services to disadvantaged communities by using alternative credit scoring and AI-driven microfinance solutions. This brings up the problem of algorithmic bias. If AI models are trained on biased historical data, they may perpetuate discrimination in loan approvals, credit scoring, and financial assistance. Another societal impact that AI brings is that it is driving financial innovation, but it also presents risks to market stability and employment. AI helps increase productivity, efficiency, and cost savings, which benefits firms and customers. However, AI automation may lead to redundancy in traditional banking roles, such as tellers, financial analysts, and compliance officers, which leads to loss of employment. These societal impacts of AI in the finance industry show me that although AI can be very helpful, it can also be harmful. We have to look at how much harm we are willing to risk to implement AI into the financial industry.

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padlet has a 2 minute limit

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AI usage in the finance industry has been rapidly growing these past few years. Whether AI technology is being used for customer service, making investments, or protection from fraud, it has been shown to be very useful in this industry. AI is continuing to trend in the finance industry with new developments of AI applications, the end is limitless. Every company is going to do its best to generate money, and if AI can help with that, it will be used. Although AI is very useful in the workplace since it can automate a lot of repetitive tasks, some people will lose their jobs. We as humans have to decide how ethical it is to let AI take over jobs.

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Ai trading: How ai is used in stock trading. Built In. (n.d.). https://builtin.com/artificial-intelligence/ai-trading-stock-market-tech - This article has good insight into how AI is used in stock trading and the finance industry.

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 article gives good information on how the finance industry uses AI for risk assessment and credit scoring.

Parker, D. (2024, October 4). The future of AI in Financial Services. Forbes. https://www.forbes.com/sites/davidparker/2024/10/03/the-future-of-ai-in-financial-services/ - This article goes over the future trends of AI in the financial industry.

Understanding AI fraud detection and prevention strategies. DigitalOcean. (n.d.). https://www.digitalocean.com/resources/articles/ai-fraud-detection#cost-reduction - This article provides information on how AI is used to prevent fraud and secure data.

What are the social implications of using AI in finance?. AI in Finance: Ethical and Social Implications. (2023, October 30). https://www.linkedin.com/advice/0/what-social-implications-using-ai-finance-br5kf - This article goes over the social impact of AI and the ethical concerns that it brings.