Financial Technology Major with General Concentration | Worcester Polytechnic Institute Undergraduate Catalog

Educational Objectives

Educational Objectives of the Financial Technology Major are:

Key technologies of the FinTech industry, including artificial intelligence (AI) & machine learning (ML) & natural language processing (NPL), blockchain & cryptocurrency, and smart contracts.
Relevant programming skills.
Recent innovations in the financial services industry, including payment, credit, capital markets, insurance, and real estate.
The limitations and challenges of FinTech (e.g., inclusion, regulation).
Emerging areas for entrepreneurial opportunities in the FinTech sector
The operational and strategic goals of financial institutions and intermediaries, and how technology is reshaping not only the traditional areas of finance (e.g., alternative lending ("alt- finance")) and wealth management but also in related fields such as insurance ("Insur-tech") and real estate.
The information and communication tools, technologies, and standards integral to consumer, merchant, and enterprise services in the payments and financial service sectors. These technologies span messaging, communication networks and gateways, core processing, mobile and online software, and application program interfaces (APIs).
The basic legal frameworks of the U.S. banking and securities sectors, how new technologies ("FinTech") are transforming legacy banking and securities business models, and how new information technologies may be able to deliver financial services to communities historically excluded from the banking and/or securities sectors.

Program Distribution Requirements for FinTech Major

Mathematics and Science

Mathematics (5/3 units)

Mathematics must include 2/3 units Calculus, 2/3 units Statistics and 1/3 unit from MA 2071 Matrices and Linear Algebra I, MA 2072 Accelerated Matrices and Linear Algebra I, or MA 2073 Matrices and Linear Algebra II.

Item #

Title

Units

MA 1021

Calculus I

1/3

MA 1022Calculus IIUnitsDepartment

This course provides an introduction to integration and its applications. Topics covered include: inverse trigonometric functions, Riemann sums, fundamental theorem of calculus, basic techniques of integration, volumes of revolution, arc length, exponential and logarithmic functions, and applications of integration to engineering.

1/3

MA 2071 or MA 2072 or MA 2073 +

MA 2071 or MA 2072 or MA 2073

MA 2071

Matrices and Linear Algebra I

1/3

MA 2072

Accelerated Matrices and Linear Algebra I

1/3

MA 2073

Matrices and Linear Algebra II

1/3

MA 2611Applied Statistics IUnitsDepartment

This course is designed to introduce the student to data analytic and applied statistical methods commonly used in industrial and scientific applications as well as in course and project work at WPI. Emphasis will be on the practical aspects of statistics with students analyzing real data sets on an interactive computer package. Topics covered include analytic and graphical representation of data, exploratory data analysis, basic issues in the design and conduct of experimental and observational studies, the central limit theorem, one and two sample point and interval estimation and tests of hypotheses.

1/3

MA 2612

Applied Statistics II

1/3

Minimum Units

5/3

Computer Science (1/3 Unit)

Computer Science – CS 1004, CS 1101 or CS 1102 is recommended. (CS 2022 and CS 3034 are not accepted)

Item #

Title

Units

CS 1004 or CS 1101 or CS 1102 +

CS 1004

Introduction to Programming for Non-Majors

1/3

CS 1101

Introduction to Program Design

1/3

CS 1102

Accelerated Introduction to Program Design

1/3

Minimum Units

1/3

Basic Science (2/3 Unit)

Biology, Chemistry, Geology, Physics

Minimum Units

2/3

Social Science (2/3 unit)

ECON 1110 and ECON 1120 required.

Item #

Title

Units

ECON 1110

Introductory Microeconomics

1/3

ECON 1120

Introductory Macroeconomics

1/3

Minimum Units

2/3

FinTech Courses

Business Foundation Courses (4/3 Units)

Item #

Title

Units

ACC 2060

Financial Statements for Decision Making

1/3

BUS 2020

The Legal Environment of Business Decisions

1/3

FIN 2070

Risk Analysis for Decision Making

1/3

OBC 1010 or OBC 3354 or OBC 4367 +

OBC 1010 or OBC 3354 or OBC 4367

OBC 1010

Leadership Practice

1/3

OBC 3354

Organizational Behavior and Change

1/3

OBC 4367

Leadership, Ethics, and Social Responsibility

1/3

Minimum Units

4/3

FinTech Foundation Courses (3/3 Unit)

The Fintech component of the curriculum is designed to provide foundational financial courses that will give students the financial preparation needed for the technical areas of the program. The Fintech core courses also introduces the student to technologies (FIN 3300) and analytical tools (FIN 3310) that firms in the financial services industry can use to disrupt the traditional delivery of financial services.

Item #

Title

Units

FIN 3300

Finance & Technology (FinTech)

1/3

FIN 3310

Financial Markets and Digital Currencies

1/3

Minimum Units

3/3

Technical Courses (3/3 Unit)

The Technical component equips students with a working knowledge of Artificial Intelligence (AI – MIS 3730), Machine learning (ML – MIS 3787), Blockchain technologies (MIS 2300), and an understanding of how these technologies can revolutionize finance. Banks and firms can use AI and ML in the rapidly growing FinTech industry to predict customer behavior or habits via Apps that integrate various AI and ML techniques. These courses also discuss ethical uses of these technologies and tools so that students learn to use them appropriately. These tools allow the user to gain detailed insights into their customers.

Item #

Title

Units

MIS 2300

Business Applications of Blockchain

1/3

MIS 3720

Business Data Management

1/3

MIS 3787 or CS 4342 +

MIS 3787 or CS 4342

CS 4342

Machine Learning

1/3

MIS 3787

Business Applications of Machine Learning

1/3

Minimum Units

3/3

Analytical Courses (3/3 Unit)

The Analytical core introduces students to popular data analysis tools for decision-making. The Analytical component equips students with the applied statistical and modeling techniques needed to analyze and make predictions and inferences from complex real-world financial data. The empirical tools developed in the Analytical core - regression, classification/clustering; sampling methods (bootstrap and cross-validation); and decision tree learning will be vital skills needed for the respective concentrations.

Item #

Title

Units

BUS 2080 or OIE 2081 +

BUS 2080

Data Analysis for Decision Making

1/3

OIE 2081

Introduction to Prescriptive Analytics

1/3

MIS 4084

Business Intelligence