Overview / CareersAI Careers in Financial Services
Financial services is all about managing money, whether in accounting, banking, fintech, insurance, investment management, or real estate. It is characterized by high-volume data processing, risk management, regulatory oversight, and customer service across retail, commercial, and institutional needs.
AI is widely used in financial services for algorithmic trading, credit scoring, customer service via chatbots, fraud detection, and risk assessment. Natural language processing supports analysis of financial news and earnings calls. Generative AI also helps with drafting financial reports, enhancing customer relationship management, and summarizing market insights.
Moving forward, AI could revolutionize personal finance by delivering automated wealth management, hyper-personalized financial advice, and real-time fraud prevention. It may reshape the labor force in finance, enabling strategic automation of high-volume manual processes while strengthening regulatory monitoring systems to ensure trust in global financial markets.
What would an AI professional contribute to financial services?
An AI professional in financial services could build predictive models for risk and fraud, develop intelligent automation tools for loan underwriting or investment analysis, or create AI agents for personal finance advising, among other tasks. Compliance-focused AI systems that monitor transactional data for suspicious activity also require the expertise of AI engineers who understand both technology and regulatory nuances.
What are the biggest opportunities and challenges for AI in the industry?
AI can detect anomalies faster than humans, anticipate risk and fraud, improve decision-making speed and accuracy, reduce operational costs, and serve as a catalyst for new financial products tailored to customer behavior. The major challenges for AI within financial services deal with algorithmic bias, data privacy, model transparency, and regulatory compliance. Financial decisions must also be explainable and auditable.
How does MSAI prepare students to lead in the financial services space?
The MSAI program helps students develop core skills in machine learning, data analysis, and model deployment, all of which are relevant to work in financial services. Students have taken electives in financial analytics. Potential collaborations with external fintech firms during the Industry Capstone project expose students to real-world financial datasets, helping them build impactful AI models that meet both technical and compliance standards.
Where have MSAI students and alumni interned or worked?
- Bain Capital Ventures
- Citi
- Goldman Sachs
- Robinhood
- US Bank
- Vanguard
- Visa
Featured Alumni
James Wilkinson
MSAI '23, Vice President of Algorithmic Trading, CitiHuman emotions have been blamed for everything from the stock market crash that led to the Great Depression to the “irrational exuberance” that drove unprofitable internet stocks in the 1990s. Algorithmic trading aims to take emotions out of trading and allow for faster, more profitable trades than humans can accomplish alone.
As vice president of algorithmic trading at the third largest US banking institution, James Wilkinson uses AI to keep human emotions and financial markets separate.
“I use a lot of data analysis day-to-day, and knowing how to translate this into machine learning modeling is extremely important,” Wilkinson said. “It has helped me gain the confidence needed to use some of the advanced models in the field in practice by knowing when they apply to certain problems.”
Liqian Ma
MSAI '22, Machine Learning Engineer, RobinhoodLiqian Ma turned to the MSAI program to better understand the potential of machine learning. Now he's applying lessons learned in the program to his work as a machine learning engineer at Robinhood, an online brokerage that offers commission-free trading in everything from stocks to cryptocurrency.
To stay current with the latest in ML, Ma relies on skills he learned in MSAI, particularly his time reading research papers on generative deep (neural network) models.
“My specific responsibilities are ML system design, development, and operations,” Ma said. “I enjoy building a new, useful product that impacts tens of millions of users.”
