Graduate Certificate in Financial Technology (FinTech)
UConn's Graduate Certificate in FinTech is a dynamic, 12-credit program. The FinTech certificate consists of a mix of analytics, technology, and business courses designed to meet the unique needs of experienced professionals, managers, and executives who have significant work experience in their field and want to enhance their skills in Finance Technology (FinTech).
FinTech Certificate at a Glance
UConn Graduate Business Learning Center
100 Constitution Plaza, Hartford, CT 06103
UConn Stamford Campus
1 University Place
Stamford, Connecticut 06901
Five courses (12 credits)
Fall, Spring, Summer
Core Classes (Required)
|FNCE||5710||Introduction to Financial Models||3 cr.|
|FNCE||5711||Foundations of Fintech||3 cr.|
This course is a quantitative introduction to time, risk, and arbitrage valuation models used in equity, credit, and derivatives markets. Covered models include discounted cash flow models, equity valuation models, asset pricing models, term structure models, binomial trees and other derivatives models. Other covered topics include: the theory of active portfolio management, portfolio performance evaluation, elements of financial risk management, the efficient market hypothesis, the behavioral finance critique, and technical analysis. Students new to Finance are encouraged to complete the online Bloomberg’s BML very early in the course or, preferably, before taking the course, for an introduction and overview of financial markets and institutions. (no pre-req.)
This course provides an overview of Fintech. It consists of three modules. Module A: Fintech’s four thematic areas: Paying for goods and services, Savings and investment products, Credit and loan products, and Managing risk. Module B: Fintech’s four enabling technologies: Distributed computing, AI and big data, Cryptography and Blockchain. Module C: Fintech’s four perspectives: The disruptive companies, The incumbent financial institution, Societal effects and regulatory responses, and the private equity investor. (no pre-req.)
This course examines the foundations of blockchain technology from multiple perspectives, including engineering, law, and economics. The course will cover blockchain technologies, distributed ledger technology, cryptocurrencies (e.g., Bitcoin), and their applications, implementation, and security concerns. Students will learn how these systems work; analyze the security and regulation issues relating to blockchain technologies, and understand the impact of blockchain technologies on financial services and other industries. The student will get a detailed picture of blockchain business networks' components and structures, such as ledgers, smart contracts, consensus, certificate authorities, security, roles, transaction processes, participants, and fabrics. (no pre-req.)
Electives (Choose one)
|FNCE||5353||Financial Modeling with C#||3 cr.|
|FNCE||5712||FinTech Economics & Business Models||3 cr.|
|FNCE||5721||Blockchain Applications||3 cr.|
|OPIM||5603||Statistics in Business Analytics||3 cr.|
|OPIM||5604||Predictive Modeling||3 cr.|
FNCE 5353 - Financial Modeling with C# (3 credits)
The goal of this course is to introduce the student to financial models within the framework of a C# deployment. It is meant to fully prepare the student for a work environment. This course will cover the gamut of C#, giving the student fluency in programming financial applications. It will introduce modelling and data structures as well. Financial applications begin with simple interest rate calculations and progress through option pricing models. Applications focus on numerical methods. All code will be written in C#. (Pre-req. FNCE 5710, OPIM 5604)
FNCE 5712 - Fintech Economics & Business Models (3 credits)
This course addresses the economics within the Fintech ecosystem, its various business models, and value creation with emphasis on the competitive landscape in Payments, Wealth management, Crowdfunding and Lending. Topics include contract theory and game theory. (pre-req. FNCE 5710 and 5711)
FNCE 5721 - Blockchain Applications (3 credits)
This course expands on PKC, data structures, Consensus algorithms, data structures – Merkle trees Consensus Algorithms. Explores uses of blockchain as a GPT technology. (pre-req. FNCE 5711, 5720, OPIM 5513).
OPIM 5603 - Statistics in Business Analytics (3 credits)
Advanced level exploration of statistical techniques for data analysis. Students study the concepts of population and sample; discuss the difference between population parameters and sample statistics, and how to draw an inference from known sample statistics to usually unknown population parameters. Topics will focus on rigorous statistical estimation and testing. Prepares students with the skills needed to work with data using analytics software. (no pre-req.)
OPIM 5604 - Predictive Modeling (3 credits)
Introduces the techniques of predictive modeling in a data-rich business environment. Covers the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate and implement predictive models for a variety of practical business applications. Predictive models such as neural networks, decision trees, Bayesian classification, and others will be studied. The course emphasizes the relationship of each step to a company's specific business needs, goals and objectives. The focus on the business goal highlights how the process is both powerful and practical. (Co-req. OPIM 5603)
Applications to the FinTech program are accepted on a rolling basis and reviewed by the admissions committee.
Applicants must fulfill all of the following requirements:
- Completed application for admission.
- Official transcripts from all colleges and universities at which the applicant has completed course or degree work.
- Completion of a one-semester college-level calculus or statistics course with a grade of "C" or better.
- An undergraduate degree (B.S. or B.A.) from a 4-year program at an accredited university or college.
- A minimum undergraduate grade-point averages (GPA) of 3.0 for at least the last 2 years.
For more information, contact: