Ph.D. in Financial Services Analytics (FSAN) Hero Image

Leading and Defining the Research Area of Financial Services Analytics

Big data is reshaping the way we live and the way businesses operate. Its impact is especially profound in the financial services industry, where abundant data comes from many sources. Financial services analytics is the science of quantitative models and technologies designed for the financial services industry. It offers improvements in risk management, customer service, customized product offerings and business operation efficiency.

The financial services analytics (FSAN) program at the University of Delaware is the first of its kind, developing fundamental FSAN theories, creating new data-driven decision-making tools and training researchers and professionals. The interdisciplinary program brings data analytics methods and non-traditional data sources to bear on issues important to the financial services industry, which differentiates it from programs in finance or financial engineering.


As a Lerner FSAN student, you’ll:

  • Collaborate with thought-leading faculty from multiple disciplines including business, engineering and computer science.
  • Work with your advisor to choose your research and dissertation topics.
  • Attend conferences and research seminars that provide access to provocative thinkers from industry and academia.


Corporate-sponsored internships offer you the opportunity to apply analytics tools to real-world challenges on teams in wealth management, machine learning, data architecture, investment banking, global finance and business management.


Our state-of-the-art facilities are designed to foster collaboration among students, faculty and industry; these include the JPMorgan Chase Innovation Center, the Geltzeiler Trading Center and the Harker Interdisciplinary Science and Engineering Laboratory.


To be granted a doctorate in FSAN, you must successfully complete all FSAN coursework with a 3.0 cumulative GPA. You have up to five years (if you have a previous master’s degree) or seven years (if you have a bachelor’s degree) to complete all degree requirements.

Course Requirements (45-54 credits)

The courses marked with an asterisk (*) below are courses listed by two departments.

BUAD840 Ethical Issues in Domestic and Global Business Environments
FSAN815/ELEG815 Analytics I: Statistical Learning*
FSAN820 Foundation of Optimization
FSAN830 Business Process Management Innovation
CISC683 Introduction to Data Mining
FINC841/FSAN841 Financial Services Firms and Markets*
FINC842/FSAN842 Financial Services Risk Analytics*
FSAN850 Financial Services Analytics Seminar (6 credits)
FSAN860 Current Research Topics (0-9 credits)
FSAN969 Doctoral Dissertation (9 credits)

Electives: Choose 3 courses from:
FSAN817/ELEG817 Large Scale Machine Learning*
MISY831/FSAN831 Enterprise Information Systems
FINC843/FSAN843 Financial Services Regulation*
ACCT804 Database Design, Networks and Implementation
ACCT806 Systems Analysis, Design and Implementation
ACCT817 Information Technologies Audit
ACCT820 Financial Statement Analysis
CISC681 Artificial Intelligence
CISC684 Introduction to Machine Learning
CISC886 Multi Agent Systems
ELEG630 Information Theory
ELEG636 Statistical Signal Processing
ELEG657 Search and Data Mining
ELEG655 High-Performance Computing with Commodity Hardware
ECON801 Microeconomics
ECON803 Applied Econometrics I
ECON810 Mathematics for Economists
ECON861 Industrial Organization and Regulation
FINC855 Financial Institutions and Markets
FINC856 Financial Engineering and Risk Management
FINC870 Theory of Financial Decision Making
FINC871 Workshop in Finance: Seminar
MATH612 Computational Methods for Equation Solving and Function Minimization
MATH630 Probability Theory and Applications
MATH631 Introduction to Stochastic Processes
MATH672 Vector Spaces
MATH829 Topics in Mathematics
APEC802 Operations Research Applications
APEC807 Math Programming with Economic Applications
STAT601 Probability Theory for Operations Research and Statistics
STAT602 Mathematical Statistics
STAT611 Regression Analysis
STAT615 Design and Analysis of Experiments
STAT617 Multivariate Methods
STAT620 Nonparametric Statistics
STAT674 Applied Database Management
STAT675 Logistic Regression

Examinations and Dissertation

A qualifying exam and research paper are required at the end of the first year. The candidacy exam must be completed by the end of the third year, upon successful completion of which and at the recommendation of the dissertation committee, you may be admitted to Ph.D. candidacy. The dissertation exam includes the approval of your written dissertation and an oral defense. Full exam and dissertation details can be found on myLerner.

A Look Into The Program

Career Opportunities

FSAN graduates are researchers and professionals who play key roles in teams that bridge the financial services industry and data and operational sciences. FSAN graduates find employment in positions such as data analyst, data scientist and financial analyst.

“UD was able to merge finance, data mining, statistics and other areas to create the FSAN program. This is also reflected in the diversity of the students. We come from different fields, and it is wonderful how we are able to complement each other in so many different ways.”

– FSAN student Leonardo De La Rosa Angarita

Learn More

Contact Dr. Bintong Chen

Explore the doctorate in FSAN by viewing a recent FSAN information session.

Admissions Information

The application deadline for the next cohort (beginning fall semester 2018) is February 1, 2018. We accept students every other year. Highly-qualified students may receive research scholarships to cover all educational expenses.

Learn more about financial services analytics Ph.D. admissions requirements, deadlines, tuition and financial aid available to you.

Admissions & Financial Aid