Master of Science in Financial Technology
The MS in Financial Technology prepares graduates to encounter new technologies in Finance and to make the important assessments about how they impact the business models and institutions across the finance industry.
Students will gain critical thinking skills for decision making through a cross-discipline approach to finance, analytics, and strategy. The focus is on how technology and AI create new opportunities for both the finance industry and society. Technology has advanced throughout the history of finance, creating new market structures, new competitors, and new services that have achieved greater levels of access around the world.
The MS in Financial Technology focuses on the impacts of data-driven financial analytics, AI applications, data visualization, as well as blockchains, stablecoins, and tokenized assets. These technologies are enabling major advances in securities trading, cross-border instant payments, digital assets and currencies.
This STEM-designated program is designed to be completed either as full time or part time within two years. The program can be taken entirely on-line with many of the courses also available in-person. Candidates interested in either FinTech start-ups or the development side of traditional institutions will find the program will help enhance or advance their career.
The overarching Learning Goals of the program are:
GOAL I: Analyze financial technologies from the context of their impact on the institutions and regulators of the finance industry.
GOAL II: Discriminate between different technologies and the different business models, services, and opportunities they enable within the financial industry.
GOAL III: Understand recent advances in machine learning, artificial intelligence, data analysis, and algorithmic trading.
GOAL IV: Understand regulation and the legal framework pertaining to FinTech and recognize the ethical implications associated with financial technologies.
Requirements
The program consists of 10 three-credit courses: seven required and three electives.
| Code | Title | Credits |
|---|---|---|
| Required Courses | ||
| DATA 6510 | Data Design and Visualization | 3 |
| DATA 6570 | Generative AI Applications in Business | 3 |
| FNCE 6991 | Blockchain and Digital Assets | 3 |
| FNCE 6992 | Decentralized Finance | 3 |
| FNCE 6993 | Algorithmic Trading | 3 |
| FNCE 6994 | Ethical Considerations in and Regulation of FinTech | 3 |
| MGMT 6508 | Strategic Management of Technology and Innovation: The Entrepreneurial Firm | 3 |
| Electives | ||
| Select 3 courses from the following (at least 1 course must be DATA): | 9 | |
| Programming Fundamentals for Business Analytics | ||
| Leading with Analytics and AI | ||
| Data Engineering for Business Analytics | ||
| Statistical Modeling for Business Intelligence | ||
| Business Intelligence | ||
| AI Systems for Business | ||
| Investment Analysis | ||
| Portfolio Management | ||
| Global Financial Markets and Institutions | ||
| Derivative Securities | ||
| Financial Risk Management | ||
| Total Credits | 30 | |
Analytics
DATA 5400 Applied Business Statistics 3 Credits
Using spreadsheet software, this hands-on course teaches a variety of quantitative methods for analyzing data to help make decisions. Topics include: data presentation and communication, probability distributions, sampling, hypothesis testing and regression, and time series analysis. This course uses numerous case studies and examples from finance, marketing, operations, accounting, and other areas of business to illustrate the realistic use of statistical methods. Previously BUAN 5400.
DATA 5405 Programming Fundamentals for Business Analytics 3 Credits
This course introduces the fundamentals of programming with a focus on core computational concepts and AI-assisted coding techniques that apply across general-purpose programming languages. Students will begin by exploring program structure, control flow, and data manipulation, while learning to harness AI tools to enhance problem-solving, debugging, and code development. Through concise lessons, quizzes, and interactive coding challenges, the course demonstrates how programming languages such as Python and R, combined with AI-augmented development, are utilized in contemporary Business Analytics environments. The course concludes with a comprehensive project that showcases both programming competency and the effective integration of AI-driven coding support in real-world analytical workflows.
DATA 5410 Analytics Programming for Business 1.5 Credits
This course focuses on quantitative modeling and analyzing business problems using spreadsheet software, such as Excel and its add-ins. Topics include descriptive analytics, visualizing and exploring data, predictive modeling, regression analysis, time series analysis, portfolio decisions, risk management, and simulation. Business models relevant to finance, accounting, marketing, and operations management are set up and solved, with managerial interpretations and "what if" analyses to provide further insight into real business problems and solutions. Open to MS Management students only. Previously BUAN 5410.
DATA 6100 Fundamentals of Analytics 3 Credits
This is an introductory level graduate course focusing on spreadsheet modeling to analyze and solve business problems. Topics include descriptive analytics, data visualization, predictive modeling, time series analysis, and data mining. Contemporary analytical models utilized in finance, marketing, accounting, and management are set up and solved through case studies. Previously ISOM 6500.
DATA 6500 Leading with Analytics and AI 3 Credits
This course provides a broad overview to the analytics profession, with a focus on data driven leadership, hands-on skills in analytics and AI, and ethics. Starting with a foundation of analytical framing, the course moves on to more advanced topics like data visualization and summarization, descriptive and inferential statistics, spreadsheet modeling for prediction, linear regression, risk analysis using Monte-Carlo simulation, linear and nonlinear optimization, decision analysis, and data storytelling. The course includes an introduction to prompt engineering and other AI basics, as well as discussions on ethics in the profession. The course culminates with a group research project using curated big data datasets, as well as individual exercises in problem framing intending to be a component of a capstone experience. Previously BUAN 6500.
DATA 6505 Data Engineering for Business Analytics 3 Credits
This course introduces fundamental techniques for collecting, preprocessing, and transforming data for business analytics and AI applications. Students develop hands-on skills in modern data processing and data mining, with an emphasis on feature engineering—both traditional and multimodal. The course explores how to convert raw data into meaningful, analyzable forms that power advanced analytical and machine learning models. Key topics include handling diverse data types, integrating information from multiple sources such as text, images, and videos, and applying methods for data acquisition and cleaning. By the end of the course, students will understand how effective data preparation and feature design serve as the foundation for predictive, descriptive, and AI-driven analytics introduced in more advanced courses.
DATA 6510 Data Design and Visualization 3 Credits
This course introduces data design and management. Starting from the traditional relational data model and database fundamentals, the course offers a hands-on introduction to Structured Query Language (SQL) for defining, manipulating, accessing, and managing data, accompanied by the basics of data modeling including entity relationship modeling and diagrams. The course also offers an introduction to NoSQL and data warehousing approaches to handling Big Data. The course further touches upon how artificial intelligence is shaping the field, including query generation and database design from natural language questions. The course concludes with a comprehensive data warehousing project that gives each student the opportunity to integrate and apply the new knowledge and skills learned from this class. Previously BUAN 6510.
DATA 6515 AI Ethics and Governance 3 Credits
This course focuses on governance frameworks for AI systems, including transparency, accountability, data privacy, consent, fairness, and social impact. Students will critically analyze AI governance frameworks drawn from sources such as the EU AI framework, NIST, and explainable AI (XAI) literature. Topics include assessment of transparency and risk, and the formulation of policy and governance recommendations. The course includes weekly readings, quizzes, case studies, and hands-on workshops. The final project centers on designing an AI governance framework for a real or realistic AI deployment.
DATA 6520 Analytics Consulting and Strategy 3 Credits
Prerequisite(s): DATA 6500.
With the rise of analytics for cutting-edge business innovation, the industry needs business leaders who can solve an organization’s most important problems by asking and answering questions using data. These business consultants need to bridge both the data analytics and business fields. This class tries to provide a “real world” consulting experience through a project-centric experiential approach, in addition to case studies of analytics consulting and business problem solving using descriptive, predictive and prescriptive analytics. When possible, class projects will be client-driven using community partners. Students work in teams using analytics to answer the client’s current and important business questions using data. The students will approach these as business analytics consultants by using effective project management to gathering requirements, using continuous client engagement to deepen understanding of the problem, suggesting ways in which to explore the question and its possible solutions through data, running different data models to approach the solution, working with clients to come up with effective analytics strategies, making business presentations based on findings, incorporating the inevitable changes that come with real world projects, and recommending strategic solutions based on their findings.
DATA 6530 Statistical Modeling for Business Intelligence 3 Credits
Prerequisite(s): DATA 5400 or placement exam.
This course introduces analytical techniques used for decision-making under uncertainty. Topics include time series and other forecasting techniques, such as Monte Carlo simulation, to assess the risk associated with managerial decisions. Specifically, we will cover data collection methods, time dependent models and analysis, advanced solver, time series techniques, exponential smoothing, moving averages, and Box-Jenkins (ARIMA) models. Application examples include financial models - stock prices, risk management - bond ratings, behavior models - customer attrition, customer likes/dislikes, buying patterns - propensity to buy, politics - identify swing voters, and sales. Previously BUAN 6530.
DATA 6535 Advanced Sports Analytics 3 Credits
Sports analytics is transforming the way teams, leagues, players, coaches, referees, and fans perceive and appreciate their favorite pastimes and games, including major team sports such as baseball, basketball, football, soccer, cricket, and rugby, more individualized sports like tennis and golf, and brand-new innovations such as e-sports. In this course, students will gain experience in framing analytical questions in sports, discover and evaluate cutting-edge research and findings in sports analytics, develop hands-on skills in using and implementing sports analytics solutions, and learn how to communicate findings to a non-analytical audience in an impactful and actionable way. This course culminates in a scholarly sports analytics research paper.
DATA 6540 Business Intelligence 3 Credits
Prerequisite(s): DATA 6510.
Modernly, business intelligence has become far more interactive. This course provides an advanced application and overview of the new techniques for building interactive dashboards and tools now prevalent in this profession. Additionally, with data overload happening on every level, the importance of good data storytelling has soared. Using programming languages and environments such as Tableau and R, this course introduces students to the business intelligence profession and teaches the skills necessary to develop and deploy cloud-based interactive apps to assist in data and analytical storytelling, including insights into user interface design (UI) and user experience design (UX). The course concludes with a comprehensive project. Previously BUAN 6540.
DATA 6545 AI Systems for Business 3 Credits
Prerequisite(s): DATA 6505.
This course provides an advanced understanding of the principles and practices of data science and AI engineering, with a special focus on business applications. It covers the design, development, deployment, and maintenance of AI-powered systems across their full lifecycle. Emphasizing practical relevance, the course applies AI and data-driven tools to business analytics. Programming tutorials and case studies introduce topics such as AI lifecycle management, foundation models, and AutoML. Students are expected to actively participate in the course deliverables through independent assignments, lab work, and group projects. The course culminates with a final project in predictive analytics, as well as individual exercises in modeling and interpretation intending to be a component of an analytics capstone experience.
DATA 6550 Big Data Management and Data Ops 3 Credits
This course introduces the fundamentals of Big Data management and its implementation in the public cloud. Topics include classic theories of data architecture, dimensional database design, data pipelines, and data governance, supplemented with the latest developments in the emerging field of DataOps. The theory is grounded with hands-on experience building databases and data pipelines with the Modern Data Stack.
DATA 6560 Sports Analytics 3 Credits
Sports analytics is transforming the way teams, leagues, players, coaches, referees, and fans perceive and appreciate their favorite pastimes and games, including major team sports such as baseball, basketball, football, soccer, cricket, and rugby, more individualized sports like tennis and golf, and brand-new innovations such as e-sports. In this course, students will gain experience in framing analytical questions in sports, discover and evaluate cutting-edge research and findings in sports analytics, develop hands-on skills in using and implementing sports analytics solutions, and learn how to communicate findings to a non-analytical audience in an impactful and actionable way. This course culminates in a scholarly sports analytics research paper.
DATA 6570 Generative AI Applications in Business 3 Credits
Artificial intelligence is becoming far more prevalent in the business and analytics worlds, yet many analytics professionals are excluded from participating in this new wave because they lack the strong coding foundations that are typically needed to implement this new technology from scratch. However, recent advances in AI/ML have coincided with desktop and cloud tools that can be deployed far more easily to generate new models without complicated coding requirements. This course will teach students how to discover, use, and daisy-chain such tools to solve real-world business problems in ways that would otherwise be impossible.
DATA 6575 Autonomous and Agentic AI for Business 3 Credits
Prerequisite(s): DATA 6545.
This course introduces students to autonomous AI applications including agents. Fundamental knowledge, such as the architectures of the deep neural networks, extraction of high-level features representing unstructured data, backpropagation, and stochastic gradient descent, are also reviewed. Additionally, students get hands-on experience building autonomous AI models. Topics covered in this class can include model building and optimization, image classification, natural language processing, generative models, and so forth. These topics cover the foundations and the latest developments in the field of autonomous AI.
DATA 6900 Contemporary Topics Seminar 3 Credits
This course draws from current literature and practice on information systems and/or operations management. The topics change from semester to semester, depending on student and faculty interest and may include: project management, e-business, management of science with spreadsheets, e-procurement, executive information systems, and other socioeconomic factors in the use of information technology. Previously ISOM 6900.
DATA 6990 Independent Study 3 Credits
This course provides an opportunity for students to complete a project or perform research under the direction of an Information Systems and Operations Management (ISOM) faculty member who has expertise in the topic being investigated. Students are expected to complete a significant project or research paper as the primary requirement of this course. Enrollment by permission of the ISOM Department Chair only. Previously ISOM 6990.
DATA 6999 Capstone: Business Analytics Applications 3 Credits
Prerequisite(s): 18 or more credits of DATA courses at the 5000-level or higher.
This capstone course for the MS Business Analytics program is to be taken in the last term before graduation. The purpose is to apply and integrate knowledge and skills learned in the program (statistics, modeling, data management, data mining, etc.) to a live data analytics project. The course is project-based, with students collaborating on their work under the guidance of faculty members. Application areas and format of the projects may vary, depending on faculty, dataset, and budget availability. However, the work should be rich enough to demonstrate mastery of business modeling and technology, with each student making a unique, demonstrable contribution to completion of the work. Previously BUAN 6999.
Finance
FNCE 5400 Principles of Finance 3 Credits
This course examines the fundamental principles of modern finance that are helpful in understanding corporate finance, investments, and financial markets. More specifically, the course examines the time value of money; the functioning of capital markets; valuation of stocks, bonds, and corporate investments; risk measurement; and risk management. Students learn to use sources of financial data and spreadsheets to solve financial problems.
FNCE 6500 Stakeholder Value 3 Credits
Prerequisite(s): FNCE 5400.
This course examines business decision-making with the aim of creating and managing value for stakeholders. Accordingly, students learn how to lead and manage a business in a competitive environment. This involves the formulation of corporate objectives and strategies, operational planning, and integration of various business functions leading to greater stakeholder value. Topics include investment and strategic financial decision-making. A business simulation facilitates the learning process.
FNCE 6530 Corporate Finance 3 Credits
Prerequisite(s): FNCE 5400.
This course provides an exploration of theoretical and empirical literature on corporate financial policies and strategies. More specifically, the course deals with corporate investment decisions, capital budgeting under uncertainty, capital structure and the cost of capital, dividends and stock repurchases, mergers and acquisitions, equity carve-outs, spin-offs, and risk management.
FNCE 6540 Investment Analysis 3 Credits
Prerequisite(s): FNCE 5400.
This course examines the determinants of valuation for bonds, stocks, options, and futures, stressing the function of efficient capital markets in developing the risk-return trade-offs essential to the valuation process.
FNCE 6545 Portfolio Management 3 Credits
Prerequisite(s): FNCE 6540.
Students examine how individuals and firms allocate and finance their resources between risky and risk-free assets to maximize utility. Students use an overall model that provides the sense that the portfolio process is dynamic as well as adaptive. Topics include portfolio planning, investment analysis, and portfolio selection, evaluation, and revision.
FNCE 6555 International Financial Management 3 Credits
Prerequisite(s): FNCE 6530.
The globalization of international financial markets presents international investors and multinational corporations with new challenges regarding opportunities and risks. This course examines the international financial environment of investments and corporate finance, evaluating the alternatives available to market participants in terms of risk and benefits. Topics include exchange rate determination, exchange rate exposure, basic financial equilibrium relationships, risk management including the use of currency options and futures, international capital budgeting and cost of capital, and short-term and international trade financing.
FNCE 6560 Global Financial Markets and Institutions 3 Credits
This course examines financial markets in the context of their function in the economic system. The material deals with the complexity of the financial markets and the variety of financial institutions that have developed, stressing the dynamic nature of the financial world, which is continually evolving.
FNCE 6565 Derivative Securities 3 Credits
Prerequisite(s): FNCE 6540 (concurrency allowed).
This course offers in-depth coverage of financial derivative securities, such as options futures and swaps. The course focuses on the principles that govern the pricing of these securities as well as their uses in hedging, speculation, and arbitrage activities.
FNCE 6570 Fixed Income Securities 3 Credits
Prerequisite(s): FNCE 6540.
This course deals extensively with the analysis and management of fixed income securities, which constitute almost two-thirds of the market value of all outstanding securities. The course provides an analysis of treasury and agency securities, corporate bonds, international bonds, mortgage-backed securities, and related derivatives. More specifically, this course provides an in-depth analysis of fixed income investment characteristics, modern valuation, and portfolio strategies.
FNCE 6575 Capital Budgeting 3 Credits
Prerequisite(s): FNCE 6530.
This course examines the decision methods employed in long-term asset investment and capital budgeting policy. The course includes a study of quantitative methods used in the capital budgeting process: simulation, mixed integer programming, and goal programming. Students use these techniques and supporting computer software to address questions raised in case studies.
FNCE 6580 Financial Risk Management 3 Credits
Prerequisite(s): FNCE 6540.
This course focuses on the evaluation and management of corporate and portfolio risk. More specifically, this course examines the methods of evaluating and managing risk with the objective of contributing to value maximization. Risk assessment methodologies such as value-at-risk (VaR) and cash-flow-at-risk (CaR) are analyzed and used extensively.
FNCE 6595 Research Methods in Finance 3 Credits
Prerequisite(s): FNCE 6540.
This course, open to MS in Finance students only, deals extensively with applied research methods in finance, a highly empirical discipline with practical relevance in the models and theories used. The central role of risk distinguishes research methodology in finance from the methodology used in other social sciences, necessitating the creation of new methods of investigation that are adopted by the finance industry at an astonishingly fast rate. For example, methods of assessing stationarity and long-run equilibrium, as well as methods measuring uncertainty, found a home in the finance area. This course covers traditional and new research methods that are directly, and in most instances, solely applicable to finance problems.
FNCE 6900 Contemporary Topics Seminar 3 Credits
This course presents recent practitioner and academic literature in various areas of finance, including guest speakers where appropriate. Topics vary each semester to fit the interests of the seminar participants.
FNCE 6990 Independent Research Seminar 3 Credits
Prerequisite(s): FNCE 6595.
This course, open to MS in Finance students only, provides participants with the opportunity to explore a financial topic of interest in depth, immersing students in detailed investigations requiring substantial research and analysis.
FNCE 6991 Blockchain and Digital Assets 3 Credits
The sudden rise in the value of Bitcoin and other digital assets focused the world's attention on cryptocurrencies as a means of payment. Blockchain technology powers Bitcoin and has been hyped as the next new, transformative technology. This class will first discuss the technical underpinnings of blockchain and review key concepts such as decentralization and consensus algorithms. The class will then discuss practical applications of blockchain technology. It will then then examine blockchain as an asset and review the dynamics of the cryptocurrency markets. It will conclude with the discussion of the future of blockchain.
FNCE 6992 Decentralized Finance 3 Credits
Prerequisite(s): FNCE 6991.
Decentralized finance (DeFi) allows parties to trade in a peer-to-peer, decentralized manner by replacing financial institutions and other intermediaries with blockchain-based smart contracts and by replacing traditional, physical currencies (e.g. U.S. dollars) with cryptocurrency (e.g. stable-coins pegged to a physical currency). This course will examine how FinTech companies are disrupting the traditional financial services industry and assess the pros and cons of these new technologies. Students in this course will also survey relevant aspects of banking and securities law, with a focus on current regulatory issues pertaining to DeFi and considerations of the future regulatory landscape.
FNCE 6993 Algorithmic Trading 3 Credits
This class introduces the necessary background knowledge and processes to design and implement algorithmic trading models including an introduction to financial markets, mechanics, participants, order types and execution, microstructure, and more. The course walks students through the process of generating trading strategies, quantifying the trading process, risk-based modeling concepts, back-testing and optimization techniques, technology and infrastructure, regulatory compliance, and key metrics of algorithmic trading model performance evaluation.
FNCE 6994 Ethical Considerations in and Regulation of FinTech 3 Credits
Prerequisite(s): FNCE 6992 (concurrency allowed).
While FinTech provides the world of finance with exciting new opportunities and innovations, they come with a new set of ethical considerations and potential new regulations. Ethical issues include potential breach of privacy of the data obtained through social media and other means. Artificial intelligence and machine learning and the use of large datasets of proprietary data could unintentionally lead to discrimination and adverse effects on diversity and inclusion efforts. Since much of the FinTech applications are linked to the internet, avoiding cyberattacks poses a large risk to successful implementation of any models. Successful leaders in the field of FinTech must understand ethical considerations associated with FinTech. It is also crucial for the manager to understand current regulation of FinTech and anticipate possible new regulation. This course will consider these and other ethical and legal considerations associated with FinTech.
A dual graduate business degree program allows students to pursue two graduate degrees, combining a Master of Business Administration (MBA) with a specialized Master of Science (MS) graduate degree in a specific field, or combining two specialized MS programs. The goal is to provide a broader skill set, enabling graduates to apply business knowledge in specialized industries or roles.
The advantage of dual degree programs is that they can be completed in less time than pursuing the degrees separately. These programs are ideal for individuals looking to expand their expertise across multiple disciplines, enhance career prospects, and increase their versatility in the job market.
Interested students will contact the Program Directors of each program to develop their dual degree plan of study.
The dual degree options include:
MBA/MS Dual Degree
Students will complete the seven core MS courses, five MBA subject area courses, and four MBA concentration courses. The MBA concentration will be in a different discipline than the MS program. The MBA concentration courses will count as MS electives. A minimum of 16 courses/48 credits is required.
Any prerequisite course for either the MBA or any of the MS programs will be required.
MS/MS Dual Degree
Students will take the seven core MS courses from the first program, the seven core courses from the second program, plus an elective. A minimum of 15 courses/45 credits is required.
Any prerequisite course for either MS program will be required.
The Dolan Career Development Center provides professional development services that enrich graduate students’ academic experiences and inspire tomorrow’s business leaders. For more information, reference the Career Development section of this catalog.