Institute for Financial Services Analytics
The Institute for Financial Services Analytics (IFSA) is at the intersection of industry and education, forming and informing the emerging field of financial services analytics.
Financial services analytics typically focuses on the collection and analysis of large datasets in an effort to offer improvements to business operations, customer service, and risk management.
Our interdisciplinary efforts draw on the expertise of faculty from diverse backgrounds including business administration, economics, finance, management information systems, computer science, electrical engineering, mathematics and statistics.
Seminars and Conferences
The Institute for Financial Services Analytics hosts speakers, seminars and conferences throughout the year, providing a forum for the exploration and discussion of research, developments and challenges facing the financial services industry. These events also connect researchers and industry leaders.
Financial services analytics is the science of quantitative models and technologies designed specifically for the financial services industry. It offers improvements in risk management, enhanced customer service, customized product offerings and more efficient business operations.
As a rapidly evolving area of academic research, financial services analytics is driven by business needs ranging from credit card fraud detection to mobile customer service to risk management. The underlying business problems are unique, complicated, and intriguing, and warrant in-depth and systematic study.
We at the Institute, including our Ph.D. students, tackle many different data challenges of the financial services industry.
Sample Research Areas
1. Process Mining and Process Optimization
Process mining extracts knowledge from event logs recorded by an information system. It can be used to streamline and improve customer-facing or internal service processes using process modeling, process mining, and predictive modeling and optimization techniques.
2. Risk Management Analytics
Analytics can be used to improve a bank’s credit and fraud exposure. For example, a need to improve fraud identification in point-of-sale and online transactions would be particularly challenging because patterns within fraudulent transactions change with time. Creating advanced online and real-time learning algorithms and Artificial Intelligence methodologies can result in significant improvements in the ability to rapidly detect fraudulent transactions.
3. Consumer Analytics and Customer Service
Analyzing and anticipating customer behaviors and interactions using a framework or model can give organizations a competitive edge. Predictive analytics can anticipate when and how often a customer will contact the financial services organization, the channel or preferred medium of contact, and the basic reason for the contact. Such insight and knowledge of customer behavior allows organizations to proactively serve their customers.
Ph.D. in Financial Services Analytics
The Ph.D. in financial services analytics is a multi-disciplinary program with a scientific curriculum that builds upon the research and educational strengths of Lerner College and the College of Engineering. Graduates are researchers and professionals who play key roles in teams that bridge the financial services industry and data and operational sciences. The program provides students with the knowledge, skills, tools and tactics to turn data into value.
B.S. and M.S. in Industrial Management, University of Tehran
M.S. in Finance, Sharif University of Technology
B.S. in Computer Engineering, Sharif University of Technology
Liu, Xiang (Dennis)
M.S. in Math and Statistics, East Tennessee State University
B.S. in Psychology, Beijing Normal University
B.S. in Automation Engineering, Beihang University
M.S. in Management Science and Engineering, Tianjin University – China
B.S. in Information Management and Information Systems, Tianjin University – China
M.S. in Economics and Applied Economics, University of Delaware
B.S. in Economics, Renmin University of China
M.S. in Finance / Statistics, University of Delaware
B.S. in Economics, Beijing University
B.S. in Applied Math & Economics, Southwestern University of Finance and Economics
M.S., University of Sheffield
B.S. in Electronic Science and Technology, Xidian University
M.S. in Electrical Engineering, Virginia Polytechnic Institute and State University
B.S. in Engineering, Tsinghua University
B.S. in Statistics, Sun Yat-sen University
M.S. in Hospitality Business, University of Delaware
B.S. in Tourism Management, Fudan University, China
B.S. and M.S. in Financial Management, Xiamen University, China
B.S. in Business Administration, Esfahan University, Iran
B.S. in Electrical Engineering, University of Delaware
M.S. in Finance, Florida International University
B.S. in Civil Engineering, Beijing Jiaotong University
De La Rosa Angarita, Leonardo
B.S. in Industrial Engineering, Universidad Industrial De Santander, Colombia
M.Ec. in Finance, Beihang University
B.S. in Electronic Information Engineering, University of Electronic Science and Technology
B.S. in Business Administration, University of Electronic Science and Technology
- Outstanding Lifelong Lerner: Minghui He - Minghui He, who earned a doctorate in financial services analytics in 2016 and works as a research scientist at Amazon, spoke with Lerner about her journey to the FSAN program and what she learned through the program.
- Outstanding Lifelong Lerner: Disen Wang - Disen Wang earned a doctorate in financial services analytics (FSAN) in 2021 and currently works as a quantitative research analyst at Morningstar in Jersey City, New Jersey. Wang spoke with Lerner about his journey to the FSAN program and what he learned through the program at Lerner.
- Outstanding Lifelong Lerner: Negin Faraji - Negin Faraji earned a doctorate in financial services analytics (FSAN) in 2016 and currently works as a data scientist at Virginia Tech. Faraji spoke with Lerner about her journey to the FSAN program and what she learned through the program at Lerner.
- Blue Hens Revolutionize Precursor to Machine Learning - In 1970, then University of Delaware professor Arthur Hoerl and his colleague UD alumnus Robert “Bob” Kennard developed ridge regression, a now world-famous statistical methodology that has withstood the test of time and revolutionized statistical modeling, leading to various machine learning methods popular today.
- Lifelong Lerner Webinar: AI and Healthcare Outcomes - Join us for the next Lifelong Lerner Webinar: Artificial Intelligence and Healthcare Outcomes on Thursday, April 15 for a discussion on the growing importance of AI in the healthcare industry.
Institute for Financial Services Analytics
Alfred Lerner College of Business
Purnell Hall 350
University of Delaware
Newark, DE 19716
The Institute for Financial Services Analytics, a joint program between the Lerner College of Business and Economics and the College of Engineering, is the result of a collaboration between the University of Delaware and JPMorgan Chase.