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My name is Aissatou Niang Sakho, and I am an Aspiring Investment Analyst and Impact Investing Enthusiast currently completing my last semester at Stony Brook University, majoring in Economics. My passion for finance is deeply rooted in both academic exploration and hands-on experience, particularly through my recent internship at the Fonds Islamique de Relance (FIR), part of FONSIS in Dakar, Senegal. There, I worked as a Financial Analyst, conducting investment screenings, building financial models, and drafting project briefs. This experience not only sharpened my technical and strategic thinking skills but also confirmed my commitment to sustainable and investment analysis.

I chose the finance industry for this assignment because it aligns with my career goals and personal mission to drive real change through impact investing. Finance is undergoing a major transformation due to artificial intelligence (AI), and I’m particularly interested in how AI enhances investment analysis. From robo-advisors to predictive analytics and natural language processing, AI is reshaping how decisions are made, risks are assessed, and portfolios are managed.

In this Padlet wall, I will explore three specific AI applications in investment analysis: robo-advisors like Betterment, predictive analytics tools used for market forecasting, and large language models (LLMs) used by firms like BlackRock to analyze financial texts. I will also discuss future trends, ethical considerations, and the broader societal impact of AI in finance.

This topic is significant to me because I’ve seen firsthand how manual financial modeling can be time-consuming and complex. AI offers tools that can streamline these processes, making finance more accessible, efficient, and inclusive especially in developing countries like Senegal.

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2. Examples of AI Usage: Post 1: Robo-Advisors – Betterment

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2. Examples of AI Usage: Post 1: Robo-Advisors – Betterment

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Betterment uses machine learning and Monte Carlo simulations to create personalized investment portfolios based on user goals and risk tolerance. These robo-advisors automate rebalancing, tax-loss harvesting, and asset allocation. The technology democratizes access to financial planning, making it affordable and scalable. However, challenges include limited human oversight and potential algorithmic bias. As someone who manually built financial models during my internship, I see robo-advisors as powerful tools that could enhance efficiency and accessibility in impact investing, especially in underserved regions.

Tech Used: Machine Learning
Benefits: Personalized investing, automation
Challenges: Bias, lack of human judgment

https://www.youtube.com/embed/RQs5XZ1SDl0?autoplay=1</iframe>

Personal Insight: I find Betterment fascinating because it shows how AI can simplify investing for people who may not have access to traditional financial advisors. As someone who manually built financial models during my internship, I see robo-advisors as tools that democratize finance. They make investing more inclusive, especially for young professionals and underserved communities. I’m excited about how this technology can be adapted for impact investing in emerging markets like Senegal.

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Post 2: Predictive Analytics – RTS Labs & Techopedia

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Post 2: Predictive Analytics – RTS Labs & Techopedia

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Predictive analytics uses neural networks and modeling techniques to forecast market trends and optimize investment strategies. These tools analyze historical data, economic indicators, and real-time market signals to detect risks and opportunities. Benefits include improved forecasting accuracy and faster decision-making. Challenges include data quality and model transparency. During my internship, I manually forecasted project outcomes AI could have enhanced accuracy and saved time. Predictive analytics is a game-changer for investment analysts seeking to make data-driven decisions.

Tech Used: Neural Networks, Predictive Modeling
Benefits: Real-time forecasting, risk detection
Challenges: Data quality, explainability

https://www.youtube.com/embed/tdV9L3C-hxQ?autoplay=1</iframe>

Personal Insight: Predictive analytics is one of the most exciting areas in finance because it allows analysts to anticipate market movements and risks before they happen. During my internship, I had to forecast project outcomes manually. Seeing how AI can automate and improve this process makes me realize how much more efficient and accurate investment analysis can become. I believe predictive analytics will be essential for scaling sustainable finance and making smarter, data-driven decisions.

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What Is Predictive Analytics | How Does Predictive Analytics Work | Data Analytics | Simplilearn

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This research deepened my understanding of how AI is reshaping investment analysis. I learned about technologies like machine learning, predictive analytics, and LLMs, and how they enhance decision-making. One challenge was finding credible sources and understanding technical terms, but I overcame this by cross-referencing academic and industry reports. This assignment helped me connect my internship experience with emerging trends in finance. I now see AI not just as a tool, but as a catalyst for inclusive and ethical investing. It has inspired me to explore how AI can be used to scale impact in African markets and beyond.

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link : https://drive.google.com/file/d/1yZiNcbuC52vXrWaqFWtX318GCotMCoId/view?usp=share_link

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AI’s impact on society through finance is profound and multifaceted. On one hand, it promotes financial inclusion by offering low-cost, automated services to underserved populations. Robo-advisors and AI-powered microfinance platforms can reach individuals who lack access to traditional banking systems. This is especially relevant in regions like Senegal, where financial infrastructure is still developing and many small businesses and individuals remain excluded from formal financial services. AI can help bridge this gap by providing scalable, personalized financial solutions.

Moreover, AI enables real-time decision-making, which can improve the efficiency of financial institutions and reduce costs for consumers. It also supports better fraud detection, credit scoring, and risk management, which can enhance trust and stability in financial systems. However, these benefits come with trade-offs.

On the other hand, AI may displace entry-level jobs in banking and finance, such as customer service representatives, loan officers, and data entry clerks. This raises concerns about workforce reskilling, job polarization, and economic equity. Additionally, privacy is a major issue—AI systems often process sensitive financial and personal data, requiring robust safeguards and ethical data governance.

AI also influences equity in financial decision-making. If algorithms are trained on biased or incomplete data, they may perpetuate discrimination in lending, investing, or insurance. Ethical oversight and inclusive data practices are essential to prevent these outcomes.

From my perspective, AI has the potential to empower communities and democratize finance. But it must be implemented with care. During my internship, I saw how strategic capital can transform businesses. AI can accelerate this transformation—but only if it’s inclusive, transparent, and aligned with human values.

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BlackRock. (2025). How AI is transforming investing.
[Rationale: Real-world use of LLMs in investment analysis.]

Betterment. (2024). How Betterment’s tech helps you manage your money.
[Rationale: Explains machine learning in robo-advisors.]

Techopedia. (2024). Predictive Analytics in Finance.
[Rationale: Covers predictive analytics use cases and benefits.]

RTS Labs. (2025). Predictive Analytics in Finance.
[Rationale: Details investment optimization and forecasting.]

CFA Institute. (2025). AI in Investment Management.
[Rationale: Academic insights on AI augmenting human decision-making.]

IBM. (2025). What is AI financial modeling?
[Rationale: Explains AI’s role in financial modeling and forecasting.]

LeewayHertz. (2025). AI for investment analysis.
[Rationale: Comprehensive overview of AI tools and benefits.]

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 3. Future Trends and Ethical Considerations  

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 3. Future Trends and Ethical Considerations  

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AI in finance is evolving rapidly, with future trends pointing toward increased use of agentic AI autonomous systems capable of making complex investment decisions. These tools will likely integrate blockchain, real-time data feeds, and behavioral analytics to create hyper-personalized financial strategies. AI will also play a larger role in sustainable and Islamic finance, helping screen investments for ESG and Shariah compliance.

However, ethical considerations must be addressed. Algorithmic bias can lead to unfair lending or investment decisions. Lack of transparency in AI models may erode trust, especially in ethical finance. Data privacy is another concern, as AI systems often rely on sensitive financial and personal data.

As someone passionate about impact investing, I believe AI must be used responsibly. It should enhance not replace human judgment. Ethical audits, inclusive datasets, and transparent algorithms are essential to ensure fairness. In Islamic finance, AI must respect religious principles, requiring collaboration between technologists and scholars.

Personally, I’m excited about AI’s potential to scale ethical investing and make financial services more inclusive. But I’m also cautious about its limitations. The future of AI in finance depends on how well we balance innovation with integrity.

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Post 3: Text Analysis with LLMs – BlackRock

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Post 3: Text Analysis with LLMs – BlackRock

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BlackRock uses large language models (LLMs) and natural language processing (NLP) to analyze earnings calls, financial reports, and news articles. These tools extract sentiment, detect trends, and uncover hidden insights from unstructured data. Benefits include granular analysis and faster decision-making. Challenges include interpretability and ethical use of data. As someone who drafted project briefs and analyzed business models, I see LLMs as tools that can enhance strategic thinking and reduce research time.

Tech Used: NLP, LLMs
Benefits: Sentiment analysis, insight extraction
Challenges: Transparency, ethical concerns

https://www.youtube.com/embed/1y9nam3vzNE?autoplay=1</iframe>

Personal Insight:
BlackRock’s use of large language models to analyze financial texts is a game-changer. These models can process earnings calls, news articles, and analyst reports to uncover insights that would take humans hours to find. As someone who drafted project briefs and evaluated businesses, I see LLMs as tools that can enhance strategic thinking and reduce research time. I’m especially intrigued by how AI agents debate investment decisions—this could redefine how portfolios are built in the future.

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BlackRock's AI Stock Pickers DEBATE and Win!   The explainer video🤖📊