Institute for Financial Services Analytics Hero Image

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 the 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.

We offer research seminars on a regular basis and host annual conferences that connect researchers and industry leaders.

Seminars and Conferences

The Institute for Financial Services Analytics hosts speakers, symposia and conferences throughout the year, providing a forum for the exploration and discussion of research, developments and challenges facing the financial services industry.

IFSA Research

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. Students

Cohort 2

Faraji, Zahra
B.S. and M.S. in Industrial Management, University of Tehran

Hadavi, Seyedhossein
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

Wang, Disen
B.S. in Automation Engineering, Beihang University

Yin, Kexin
M.S. in Management Science and Engineering, Tianjin University – China
B.S. in Information Management and Information Systems, Tianjin University – China

Zhao, Xiaohang
M.S. in Economics and Applied Economics, University of Delaware
B.S. in Economics, Renmin University of China

Gong, Mingxing
M.S. in Finance / Statistics, University of Delaware
B.S. in Economics, Beijing University

He, Minghui
B.S. in Applied Math & Economics, Southwestern University of Finance and Economics

Shi, Huang
M.S., University of Sheffield
B.S. in Electronic Science and Technology, Xidian University

Wu, Nan
M.S. in Electrical Engineering, Virginia Polytechnic Institute and State University
B.S. in Engineering, Tsinghua University

Zhang, Haici
B.S. in Statistics, Sun Yat-sen University

Cohort 1


Ji, Xin
M.S. in Hospitality Business, University of Delaware
B.S. in Tourism Management, Fudan University, China

Lin, Fanghua
B.S. and M.S. in Financial Management, Xiamen University, China

Riasi, Arash
B.S. in Business Administration, Esfahan University, Iran

Kilgallon, Sean
B.S. in Electrical Engineering, University of Delaware

Ma, Lan
M.S. in Finance, Florida International University
B.S. in Civil Engineering, Beijing Jiaotong University

Rosa Angarita, Leonardo De la
B.S. in Industrial Engineering, Universidad Industrial De Santander, Columbia

Wang, Deshen
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


Contact Information:

Denise Heldorfer
Office Phone: 302-831-6526
Email: dtheldor@udel.edu
 

Bintong Chen
Office Phone: 302-831‐2756
Email: bchen@udel.edu

Institute for Financial Services Analytics
Alfred Lerner College of Business
Purnell Hall 350
University of Delaware
Newark, DE 19716
302-831-6526 (w)
about-fsan@udel.edu

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.