Master of Finance Curriculum | Weatherhead School at Case Western Reserve University

Master of Finance Curriculum

Complete the Master of Finance Degree Curriculum in as Little as 9 Months or Stay Longer*

  • Complete the Master of Finance degree’s 30 credit hours in two semesters, OR
  • Stay up to two years to complete 39 credit hours with departmental certification in a track specialization by taking appropriate additional electives. You may also apply for summer internship opportunities during this time, available on a competitive basis.
  • Transition right into our master's program after completing your undergraduate degree: no work experience is needed. Typical undergraduate majors include business, economics, physics, accounting, engineering and math.

View the Master of Finance degree curriculum below:

Core Courses

These core courses provide students with the tools and techniques that build a strong foundation for a career in finance.

Students enrolled in a 2-semester long core course – Individual, Team, and Career Development - complete real-world assignments using big data under the supervision of corporate executives and potential employers. By leveraging on the executives’ vast industry experience and financial knowledge in the classroom, students gain real experience in areas such as intangibles valuation, credit scoring, and fixed-income hedging. Corporate seminars delivered by industry executives enable students to network and learn from executives in areas such as private equity, REITs, financial analytics, and more. International students can also participate in a finance-customized Spoken English Language Program to enhance their communication and presentation skills.

Blockchains and AI: Applications in Finance and Business course offered in Master of Finance curriculum

During the Master of Finance program at Weatherhead School of Management, you'll get ahead by studying and applying the latest technology. The popularity of blockchain technologies has increased exponentially since the release of bitcoin in 2009. While bitcoins garnered a lot of attention during the initial days, the focus has shifted over time to the underlying technology: blockchain. This wildly innovative technology has made possible tasks that were hitherto deemed implausible: validate ownership in a digital asset, verify the true state of a transaction without relying on a costly intermediary etc.

The list of businesses that are impacted by this technology makes for an impressive reading: supply chain, health care, insurance, foreign exchange transfers, real estate, etc. If the emphasis of blockchain technology is on trust, that of Artificial Intelligence is on predictions. Accurate predictions and sound judgments are two critical ingredients of any decision making process. Recent developments in a field called machine learning (and its sub-field, deep learning) have led to dramatic improvements in the accuracy of predictions made by these algorithms. Significantly, this gain in accuracy has been accompanied by a reduction in overall costs.

Artificial Intelligence for Financial Modeling Course

This course places emphasis not only on understanding the theoretical underpinnings of various AI models but also on building, evaluating, and critiquing AI models as they apply to the finance industry. This course begins with an introduction of Machine Learning models; various key ideas such as bias-variance tradeoff, cross-validation, regularization techniques are introduced with relevant examples from Finance. The course then proceeds to discuss Artificial Neural Networks and its relevance to Deep Learning. Foundational ideas such as back-propagation are discussed in sufficient detail; emphasis is placed on evaluating the performance of all these models.


Whether you’re interested in financial risk management courses, corporate finance, or financial big data, the Master of Finance degree curriculum offers you the flexibility of determining the structure of your program based on your long-term goals and specific areas of interest. You can graduate with the basic program of 30 credit hours or work toward additional departmental certification, available upon successful completion of 39 credit hours in a specific track:

Corporate Financial Analytics Track (STEM Eligible)

Any other course as determined appropriate by the Master of Finance Faculty Director

Corporate Finance Track

Any other course as determined appropriate by the Master of Finance Faculty Director

Risk Management Analytics Track (STEM Eligible)

Any other course as determined appropriate by the Master of Finance Faculty Director

Financial Big Data Analytics Track (STEM Eligible)

  • FNCE 431 – Fixed Income Markets and Their Derivatives
  • FNCE 433 – Quantitative Risk Modeling
  • FNCE 460 – Investment Strategies
  • FNCE 470 – Financial Models Using Big Data
  • FNCE 471 – Applications in Financial Big Data
  • FNCE 493 – Blockchains, Cryptocurrencies, and Cryptoventures
  • FNCE 494 – Artificial Intelligence for Financial Modeling

Any other course as determined appropriate by the Master of Finance Faculty Director

as well as appropriate electives offered by other departments, as approved by the program faculty director.

Please note:

  • All courses are contingent upon availability of appropriate staff, and need not be offered every time.
  • Enrollment in elective courses is contingent upon appropriate performance in the program.
  • All students must work towards specializing in a track.
  • International students fulfill visa requirements each semester. Contact International Student Services for more information.
  • Students who choose to stay beyond two semesters can apply for internship opportunities on a competitive basis. Visit the Career Management page to learn more.

*contingent upon obtaining all of the necessary approvals

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