Online Graduate Certificate in Applied AI in Finance
Program Overview
Enhance your finance skills by adding Artificial Intelligence (AI) proficiency to your repertoire.
Applied AI in Finance
In the ever changing landscape of the finance industry, new technology continues to influence decision making in organizations around the world. Quantitative tools have enhanced the ability of organizations to make data driven financial decisions and the advent of machine learning and artificial intelligence has disrupted the industry at a blistering pace. This certificate includes courses that demonstrate the intersection of finance and artificial intelligence and prepare students to utilize this emerging technology in their careers.
This certificate can be completed completely online and offers asynchronus learning, providing working professionals with an opportunity to gain new skills without disrupting their careers. In order to ensure student success, students will receive support from dedicated University of Cincinnati Online staff from enrollment through graduation.
Program Highlights
High Quality Education
Students enrolled in the Applied AI in Finance Graduate Certificate will learn from world-renowned faculty at UC's Carl H. Lindner College of Business who are working on and researching AI's latest finance applications. With experiential classroom learning, students will gain an understanding of how to incorporate artificial intelligence into the financial decision making process.
Courses in this program will expose learners to both traditional financial data analysis techniques as well as the transformative power of enhanced analysis using AI. Through hands on experience, students will learn how to develop machine learning models for real world financial datasets and be exposed to the challenges of applying AI to financial data.
Although no experience with AI tools is required, a basic background in Python programming and information systems is desired.
Flexibility
- 100% online
- Can be completed in two-three semesters (9-12 months) of study.
- Credits gained from this certificate can be applied to other graduate degree programs at the Carl H. Lindner College of Business.
Support from Application through Graduation
At UC, you’ll have a full support team behind you:
Enrollment Services Advisor: Your go-to resource during the application process
Student Success Coordinator: Helping you prepare for classes and stay on track
Access to Resources: Access to university resources that will support you through your program including online learning expectations and resources, health and wellness resources, and academic support
Curriculum
UC's online Applied AI in Finance graduate certificate program is 12 credit hours and can be paired with an LCB graduate degree.
| Course | Title/Description | Credit |
|---|---|---|
| FIN7031 | Financial Econometrics Analysis of financial data is a core component of investment management. You need to be comfortable and adept at sampling, modeling, regression analysis, and hypothesis design and testing in order to be an effective financial analyst. In this course we will cover many of the basic statistical and probability concepts that are central to financial analysis. Along the way we will touch on various finance concepts and terms, so, in part, this course will provide you with a conceptual introduction to various investment topics. |
3 |
| FIN7032 | Quantitative Equity Investing This course introduces students to applied research and applications in quantitative equity investing. First, students will learn about the empirical evidence related to prominent equity factors including size, value, and momentum. Second, students will learn how to construct and backtest factor strategies using real data. Finally, students will be exposed to real-world applications of factor investing in quantitative asset management via case studies. The goal of the course is to equip students with necessary knowledge and skills for applied research in quantitative equity management. More broadly, this course can be a useful part of training for students who are interested in a career in financial data analytics. |
2 |
| FIN7047 | The main objective of the course is to introduce students to fintech and cryptocurrency. The goal is to understand the fundamental concepts underlying financial technologies and their applications. Examples of these include artificial intelligence, machine learning, and blockchain in financial markets, such as business activities, financing, and investments. The course consists of four parts. The first part introduces the status quo and fundamentals of financial technologies (fintech) as well as their main applications, including artificial intelligence, payments, robo-advising, insure-tech, and blockchain. The second part covers the mechanisms and applications of artificial intelligence and machine learning by focusing on the use of natural language processing (NLP) and large language models (LLM). The third part focuses on blockchain and cryptocurrencies, discussing Bitcoin, Ethereum, stablecoin, and NFTs. The last part of the course focuses on cryptocurrency markets and portfolios. The cryptocurrency market comprises exchanges (both on-chain and off-chain) and spot and derivative contracts. Cryptocurrency portfolio analysis studies the risk and return tradeoff of such portfolios. The course combines lectures, class discussions, and case study analyses. |
2 |
| FIN7053 | Algorithmic Trading This course provides a comprehensive treatment of the fundamental principles required to design and implement algorithmic trading models in financial markets. The course will introduce the best practices and the formal process of generating trading ideas, the differences between low-frequency and high-frequency trading signals, back-testing and its associated biases, optimization techniques, and industry metrics for evaluating algorithmic trading models’ performance. Students will have the opportunity to implement basic algorithms in well-known paper-trading platforms. |
2 |
| FIN7057 | Financial Applications of Machine Learning and Artificial Intelligence This course provides comprehensive coverage of machine learning and artificial intelligence applications in finance. Students will learn fundamental ML algorithms and their specific applications to financial problems including return prediction, risk management, portfolio optimization, and corporate finance. Advanced topics include large language models for financial text analysis and sentiment analysis techniques for extracting trading signals from news and earnings calls. The course emphasizes both theoretical foundations and practical implementation using Python and industry-standard libraries. Students will gain hands-on experience developing ML models for real world financial datasets and understand the unique challenges of applying AI in financial markets. |
3 |
Admission Requirements
Prerequisites
- Applicants must hold a bachelor's degree (in any field of study) from a college or university accredited by an agency recognized by the U.S. Department of Education for Title IV purposes, or the international equivalent.
Complete the online application and submit the application fee.
Standard Application Fees*:
- $65.00 for domestic applicants to most degree programs
- $70.00 for international applicants to most degree programs
- $20.00 for domestic applicants to Graduate Certificates
- $25.00 for international applicants to Graduate Certificates
- Fee waivers are automatically applied for applicants who:
- are currently serving in the US armed forces
- are veterans of the US armed forces
*Application fees are waived for Spring 2027 applications submitted by November 1st, 2026.
All applicants are required to upload unofficial transcripts during the application process, showing all undergraduate and graduate course work completed, including degrees granted and dates of conferral.
Official transcripts are not required until the student has received and accepted an offer of admission from the university. Once the offer has been confirmed, the student must submit official transcripts.
Students who have received degrees from the University of Cincinnati do not need to submit official paper copies of their UC transcripts.
For questions regarding international students, contact an Enrollment Services Advisor.
Transcripts can be submitted electronically or by mail. To see if your transcript(s) can be ordered electronically, visit the links below and search for your previous school(s).
- Parchment
- Please select “University of Cincinnati – Main Campus” as the recipient of your transcript.
- National Student Clearinghouse
- Please have your transcript sent directly to admissions@uc.edu.
If you do not see your past school(s) listed on either site, please contact the school(s) directly. Then, mail your sealed, unopened, official transcripts to:
Please mail sealed, unopened, official transcripts to:
University of Cincinnati
Office of Admissions
PO Box 210091
Cincinnati, Ohio 45221-0091
Current resume or CV.
Application Deadlines
| Term | Application Deadline | Classes Start |
|---|---|---|
Spring 2027 Summer 2027 Fall 2027
|
November 15, 2026 April 15, 2027 July 1, 2027 |
January 11, 2027 May 10, 2027 August 23, 2027 |
Tuition & Fees
The University of Cincinnati's online programs allow students to pursue degrees without having to quit their jobs. Our online courses offer the same quality education found on campus in a modern format that fits students’ tight budgets and busy schedules.
The University of Cincinnati's online course fees differ depending on the program.
To view tuition information and program costs, visit the Online Program Fees page.
Accreditation & Rankings
- The University of Cincinnati and all regional campuses are accredited by the Higher Learning Commission.
- The Carl H. Lindner College of Business holds AACSB accreditation. AACSB International business accreditation is an achievement earned only by programs of the highest caliber. Institutions that earn accreditation confirm their commitment to quality and continuous improvement through a rigorous and comprehensive peer review. Less than one-third of U.S. business school programs and only 15% of business school programs worldwide meet the rigorous standards of AACSB International accreditation.
Careers & Outcomes
Who Is This Program For?
This program may be a good fit for:
- Finance professionals seeking practical experience with AI and machine learning
- Financial analysts, investment professionals, and risk professionals
- Data and technology professionals working in financial services
- Professionals interested in quantitative investing, fintech, or algorithmic trading
- Bachelor’s-prepared professionals with foundational Python and information-systems knowledge
Career Opportunities
Combined with relevant education and professional experience, the knowledge developed through this certificate may support responsibilities related to:
- Financial data analysis
- Quantitative investment research
- Financial risk modeling
- Fintech and AI implementation
- Algorithmic trading analysis
- Portfolio and market analytics
Relevant settings include banks, investment firms, insurance companies, fintech organizations, consulting firms, corporate finance departments, asset-management companies, and financial-technology vendors.
The certificate alone does not qualify someone for a particular financial, investment, data-science, or AI position. Employers may require a relevant degree, technical proficiency, professional experience, registration, or additional credentials.
Career Outlook
The closest broad BLS benchmark is financial analysts. The national median annual wage was $103,570 in May 2025, although pay differed between financial and investment analysts and financial risk specialists. Employment is projected to grow 7% from 2025 to 2035, with approximately 29,500 openings annually. These figures represent national occupational data, not certificate or graduate outcomes. Source: U.S. Bureau of Labor Statistics—Financial Analysts.
Build Your Credentials
Depending on their existing backgrounds and career goals, professionals may also pursue the CFA, Financial Risk Manager, Chartered Alternative Investment Analyst, cloud, data-platform, Python, or machine-learning credentials.
With the appropriate education, technical proficiency, professional experience, and additional credentials, professionals may advance into areas such as:
- Quantitative financial analysis
- Investment and portfolio analytics
- Financial risk management
- Fintech product development
- AI-enabled financial modeling
- Financial analytics leadership
Continue Your Education at UC
Certificate credits may be applied to eligible graduate degree programs offered by UC’s Carl H. Lindner College of Business including the Master of Science in Finance, Master of Science in AI Management, and Master of Business Administration.
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