Course Index

Pre-Program

Pre-Program runs from February until the start of Pre-Module 1 in April. During this time, the MSFT program will provide matriculated students access to the Admitted Students Website, where they will be able to get more familiar with the program administration, format, logistics, as well as access optional materials to brush up on any skills prior to the Pre-Module period.

Pre-Module

Pre-module 1 is the period from April to May before students participate in the in-person classes in Module 1. During this time, the MSFT program will distribute pre-readings, assignments, and group work so that students can complete their Module 1 course work prior to the in-person session in May.

Module 1 (In-Person)

Finance Concepts and Math
This course provides an introduction to the key concepts and the associated analytical tools that are the basic building blocks for all financial analysis. These concepts and tools are essential in order to understand the material in later courses, but they are also interesting and important in their own right. Fintech tests the laws of finance, so we revisit critical finance theories and concepts to prepare you to understand the impact of technology transformation. For example, does the “law of one price” hold up as markets electronify? Will markets remain “efficient” if meme traders influence more equity trading? Are online lending platforms making credit allocation more fair, or introducing new vectors of bias and moral hazard? Will smart contracts reshape insurance? Can fintech unicorns be valued using traditional methods? This course refreshes core finance concepts and math that underpin all financial and fintech functions and businesses.

R Programming for Data
In this course, students will learn how to program in R and how to use R for effective data analysis and visualization. The course begins with developing a basic understanding of the R working environment. Next, students will be introduced to the necessary arithmetic and logical operators, salient functions for manipulating data, and getting help using R. Next, the common data structures, variables, and data types used in R will be demonstrated and applied. Students will write R scripts and build R markdown documents to share their code with others. They will utilize the various packages available in R for visualization, reporting, data manipulation, and statistical analysis.

Students import data sets, transform and manipulate those datasets for various analytical purposes. Students will learn how to create control structures, such as loops and conditional statements to traverse, sort, merge, and evaluate data.

Databases for Business Analytics
Databases are ubiquitous in all businesses and hold a significant amount of information about the business. Every data analysis and report typically starts with an SQL query, as SQL is the lingua franca of all database systems. Therefore, SQL is a necessity for anyone who needs to analyze data as part of their job, and many tech companies consider knowledge of SQL a prerequisite for all their analysts and managers.

This database class is designed for absolute beginners and teaches students how databases are structured and how to write SQL queries that retrieve data from a database. The class is heavily hands-on, with a focus on developing the necessary skills for writing SQL queries.

Module 2 (In-Person)

Data Science and Predictive Analytics
The topics we will cover in this course begin with an Introduction to Data Science. We will also cover Predictive Analytics Framework; Fitting, Overfitting, and Complexity Control; Evaluation (model performance analytics); Analytical Engineering; and conclude with Management and Data Science.

Big Data & Dealing with Data in Finance
This course will help students learn how to program so that they can effectively retrieve, store, manipulate and visualize data.

Over the span of this course, you will develop practical programming and data manipulation skills using Python. Python is a beginner-friendly programming language that is widely-used in industry. In recent years, it has also become the de facto standard in data science.

The course is roughly split into two halves. The first half lays the foundation by teaching you general-purpose Python programming. The second half focuses on practical tasks when dealing with data. The course will cover: general programming skills (variables, statements, functions, loops, modules); obtaining data from online sources through web scraping; obtaining data from online sources via APIs; querying and storing data in SQL databases; writing and reading data to/from files (including text files, CSV files and Excel files); processing tabular data (using tabel, a modern alternative to pandas); visualizing data (using Bokeh).

Platform Strategy in Fintech
This course covers economic concepts that are key for fintech strategy. The course begins with Lock-In, Network Effects and Positive Feedback, then discusses two-sided markets (platforms). This leads to discussion of Platform Strategies, such as market entry, pricing and platform competition, focusing on how competition is different in Platform markets. The readings from the economic literature, managerial literature and popular press and blogs provide an introduction to the concepts, analysis of their implications, and a strategy toolkit. The concepts and strategies in this course are further illustrated and discussed with cases. The course concludes with a look at how blockchain technologies such as enterprise blockchains and platform-specific digital tokens can be used to implement and strategically support fintech platforms.

Ethics in Fintech
While presenting the business world with an exciting panoply of emerging models and capacities, the rapidly unfolding field of fintech necessarily confronts it with a whole new set of ethical and professional challenges. Algorithms designed to maximize profits in a data driven environment might also violate rights of non-discrimination and equal protection under the laws. Data sets obtained through social media and other means might lead to serious breaches of privacy, tarnishing the brand franchise while triggering increased regulatory oversight. The black-box nature of AI models raises serious risks of unintended collateral damage to customers and other stakeholders along with deleterious effects on diversity and inclusion. And the lingering possibility of cyberattacks, with their potential for exposing company strategies and proprietary data, hangs over all like a cloud. The senior fintech executive must understand the nature of these and other challenges, and must be capable of formulating ethically appropriate and technologically effective schemas for meeting them. This course will set these questions and issues within the context of business ethics and professional responsibility. As a result, students will finish this course with tools, frameworks, and a clear set of criteria for making nuanced decisions and setting appropriate standards in this dynamic new field.

Module 3 (In-Person)

Innovation and Disruption in Financial Services
“Fintech” refers to financial sector innovations involving technology-enabled business models that can facilitate disintermediation, revolutionize how existing firms create and deliver products and services, address privacy, regulatory and law-enforcement challenges, provide new gateways for entrepreneurship, and seed opportunities for inclusive growth.

Fintech is also the label for increasingly technological approaches to the main financial intermediation functions: payments, capital raising, remittances, managing uncertainty and risk, market price discovery, and mediating information asymmetry and incentives. In today’s fintech businesses, consumers bank via mobile apps integrated into social media, institutions trade electronically, and robo-advisers make decisions about investment portfolios.

This course will study:

How is financial innovation different than industrial innovation? How is financial innovation evolving? What are the light sides and dark sides of financial innovation?
Will traditional financial intermediaries be able to adapt?
What are the critical technology strategies and foundational technologies in fintech?
What are the core and novel sources of fintech data, how are they managed? How is data visualization evolving? What are the primary fintech data science methods and tools? How do they apply to real fintech problems and questions today?
How is fintech reconfiguring financial services business models? What are the key disruption points? What determines success in fintech?
Where are the limits, risks, and broader policy and social implications of fintech?

Regulation in Fintech
This course examines the impact of financial and other regulation on fintech. It helps students understand the drivers for and tenants of financial regulation and how it affects firms globally. It explores how regulation shapes business and technology decisions related to lending, payments, and digital assets. Regulation in fintech reviews how regulatory sandboxes have been used by fintech firms and financial institutions to test ideas and garner regulatory support. Additionally, the course compares the different countries' regulatory approaches and looks at future trends in fintech regulation. Real-world case studies are used bring to key concepts to life and relate them to practical business concerns.

Technology Strategy, Change, and Analytics
The goal of this course is to understand why organizations often fail to realize a return on their analytics investments. The course argues that, while there are clearly technical and skill-based reasons why some firms struggle, most of the impediments arise from organizational issues. Specifically, the contextual business knowledge and the analytics knowledge in most organizations are separated from one another, which creates real problems both for asking good questions and for understanding what to do with the output from analytics efforts.

To address these issues, students will seek to understand the link between strategy (the most integrative business function in most firms) and analytics. Analytics is about improving decision making, and strategy is all about making value creating decisions, so the two are a natural fit together. Students will explore how to use data – both small data and big data – to improve decision making and value creation in organizations.

The Financial Services Industry
The financial services industry touches all of our lives and has been going through a continuous transformation since the deregulation that began in the early 1970s. That evolution has accelerated in recent years as more and more pressure has been brought to bear by various stakeholders in the industry who have divergent goals and agendas. Those invested stakeholders include clients, investors, employees, regulators/politicians and the public at large. Overlaying all of this change has been rapid technological advancement that has had a direct impact on how the industry delivers its services, meets expected equity returns and manages the risk inherent in that delivery.

This course provides a broad overview of the financial service industry and of the forces that are continuing to change it worldwide. That change/evolution has resulted in a confederation of sometimes integrated products and services in a multi-product firm, or as individual stand-alone businesses either within an integrated financial firm, or as represented by boutique, limited product firm. The course focuses on four big questions: (1) Why and what kind of services are provided by participants in the industry? (2) Who develops, provides and regulates those services? (3) How are they likely to be executed or modified in the future? (4) What skills (both technical and "soft") are required for an individual to succeed in the industry? The course’s approach will be to examine each of the principal businesses in which various financial service firms have been involved, including: raising capital; financial advisory; broker/dealer positions; sales and trading; proprietary investing; managing the assets of others (both institutions and individuals) and risk management.

Throughout, there are a number of overarching themes. Among these are: the interplay of politics, regulation, globalization, and technology; the emergence of shadow banking including private equity and hedge funds as both critical clients and potential competitors for the major investment banks; the search for new, high-margin products, and whether that process has reached its limits; and the changing relationships among the different groups within a financial service firm.

Module 4 (Online)

Blockchain and Cryptocurrencies
This course introduces students to digital currencies, blockchains, and related topics in the fintech area, perhaps the most significant innovation in the financial world since the advent of double-entry bookkeeping centuries ago. The technology appears to represent an existential challenge for major parts of the finance industry. It is now commonly suggested that commercial banks and stock exchanges may no longer exist, or may become much smaller, within the next 10 to 20 years, with increasing volumes of payments and exchange taking place on a decentralized basis inspired by the 2009 launch of Bitcoin.

The course will begin with a study of the nature of money and legacy payment and banking systems. Students will then study the emergence of stateless, cloud-based digital currency systems since 2009. Further lectures will explore threats that blockchain technology poses to incumbent firms and their resulting attempts to co-opt the technology into existing business models. Students survey related issues including hacking, “smart contracts,” governance, and especially emerging regulation.

Machine Learning in Finance
This course is designed to teach students how to think about, formulate, and analyze finance problems from a machine learning perspective. The course focuses on the practical challenges that arise in formulating and implementing analytic and predictive tools for financial applications at financial institutions. Our emphasis is on decision making, where decisions are made automatically by a machine, or by a decision maker who uses the outputs of a machine’s predictions and analysis. Throughout the course, we will center our discussions on three organizing principles: (1) The first principle is that it is critical to make an assessment of the degree of predictability for a target. This varies between zero and one (zero implies randomness and one implies complete determinism). All problems lie on this spectrum. This is a key problem characteristic that we consider when formulating and analyzing any problem. (2) The second principle is that it is critical to understand the cost of error, or the consequences of being wrong. Is the error a minor nuisance or a life and death situation? These considerations tend to be central to our notions of when we can trust a model for decision making, and they may be asymmetric: not granting a loan that doesn’t default is usually less costly than granting a loan that does. (3) An auxiliary, third principle is that it is important to consider how often something “interesting” happens in the domain we are modeling (the base rate). If a phenomenon of interest occurs rarely, it tends to be difficult to predict, yet predicting it may be very valuable as a result. On the other hand, if we can predict an event well, but it very rarely takes place, it may be difficult to deploy the prediction profitably. An analysis of such problems involves a deep evaluation of the costs of being right or wrong, and calibrating systems that result in acceptable errors and so forth.

Behavioral Finance in Fintech
Finance theory has long relied on a descriptively sparse model of behavior based on the premise that investors and managers are rational. Another critical assumption is that misjudgments by investors and managers are penalized swiftly in competitive markets. In recent years both assumptions have been questioned as the standard model fails to account for various aspects of actual markets. Behavioral finance, which allows that investors and managers are not always rational and may make systematic errors of judgment that affect market prices, has emerged as a credible alternative to the standard model. This course provides an exposition of the insights and implications of behavioral finance theory showing how it can explain otherwise puzzling features of asset prices and corporate finance. Notwithstanding the inroads of the new theory the standard model retains strong support amongst many academic practitioners who make criticisms of behavioral finance that deserve serious consideration. An important challenge that we will address in this course is identifying the respective domains of each perspective and whether there are tradable opportunities. We examine behavioral finance in three stages by analyzing the basics of the behavioral finance and then applying this to centralized and decentralized fintech. Learning objectives include: (1) explore investor behavior in financial markets – from the traditional (i.e., rational, bell curve) market perspective (efficient market hypothesis) to the emerging area of behavioral finance with inputs for systematic investor biases (i.e., irrationality, non-normal distribution), (2) test select psychological biases/heuristics (“rules of thumb”) in controlled class experiments , (3) specifically examine points 1 and 2 as it relates to Centralized and Decentralized Fintech.

Data Visualization for Fintech
This course is an introduction to the principles and techniques for visualizing financial data. This course shows you how to better understand your data, present clear evidence of your findings to your intended audience, and tell engaging data stories that clearly depict the points you want to make all through data graphics.

You will learn visual representation methods and techniques that increase the understanding of complex data and models. Emphasis is placed on the identification of patterns, trends and differences from data sets across categories, space, and time.

The ways that humans process and encode visual and textual information will be discussed in relation to selecting the appropriate method for the display of quantitative and qualitative data. Graphical methods for specialized data types (times series, categorical, etc.) are presented. Topics include charts, tables, graphics, effective presentations, multimedia content, animation, and dashboard design.

Throughout the course, several questions will drive the design of data visualizations. These include: Who’s the audience? What’s the data? What’s the task? What’s the best visual display? This is a hands-on course. In this course, we will focus on using Tableau to create, edit, alter, and display your data graphics. To learn these tools, we will begin working with some very small data sets to practice and then advance to larger data sets. Since this is not a class on data analysis or models, you’ll be expected to apply your prior knowledge of statistics, data mining, and data science to the creation of beautiful data displays (using big or small data).

Module 5 (In-Person)

Venture Capital and M&A in Fintech
This course has two objectives: (1) introduce you to the financing lifecycle of high-growth new ventures (i.e., startups); and (2) introduce the key technologies, business models, and companies of the startup fintech landscape. This class is very applied and is intended for students who are potentially interested in working at, founding, or investing in early-stage startups in the fintech sector. We will roughly follow a successful startup’s path from founding through the stages of new venture finance. These include the “VC method” of valuation, the venture capital industry, and finally how entrepreneurs and investors realize returns. Entrepreneurial finance is all about making decisions in situations of substantial uncertainty, requiring a careful balance of qualitative and quantitative approaches. At the same time, we will examine the key components of the evolving fintech sector, including new instruments for financing early-stage enterprises, such as initial coin offerings and equity crowdfunding. While “financial technology” has been around since King Alyattes of Lydia minted the first coin around 600 BC, fintech today refers to the application of information technology to finance. It has become an important new sector for high-growth startups. In this class, we will consider the following subsectors, where startups are either seeking to displace incumbents or sell them their services: Bitcoin/Ethereum (and ICOs); personal finance; equity crowdfunding; lending; payments; insurance; and remittances. Blockchain, peer-to-peer platforms, and artificial intelligence (e.g., machine learning) are key technologies underlying many of the new business models.

Valuation and Unicorns in Fintech
In this course, students will: (1) develop expertise at valuing fintech businesses throughout their lifecycle, from venture-funded birth to large scale public companies, using both top down market-structure, and bottom-up business analysis frameworks applicable to all fintech businesses, for use in investment, merger and acquisitions, and project design, (2) understand the impact of spread and fee businesses and regulation on fintech valuation, (3) explore the peer-to-peer lending business as a case of disintermediation and re-intermediation, (4) apply the latest thinking about unicorn valuation to well-known fintechs.

Fintech Startup Case Studies
In this course, students will: (1) develop understanding of technology-led change in three of the five financial functions (payments, markets, and capital allocation-wealthtech) through detailed case analysis, (2) assess the technology, regulatory, partner, and customer acquisition strategy of an early-stage payments startup by analyzing a due diligence document from an angel group, (3) assess the potential for platform success in a late-stage startup automating bond trading, and determine whether market electronification undermines classic finance theories, by analyzing the impact of information provision on trading, (4) assess the value-creation and channel strategy of a mid-stage fintech startup in wealthtech by evaluating whether the company should enter a new channel, (5) visit the latest startups, meet founders, and assess their teams, strategy, and business cases based on what was learned in class, and (6) from the above, develop a tool kit for fintech startup evaluation.

Module 6 (In-Person)

Fintech in Developing Markets: Digital India / Digital China
This course will focus on fintech innovation, and investing in this newer asset class for emerging markets. In addition, the course will showcase and give students an opportunity to engage with the particulars of companies which are pioneering new business models riding a variety of forces at work re-shaping what is possible: the expansion of mobile phones, enabling lower cost distribution and customer engagement and safe, instant and cheap micro-payments; increasing penetration of the internet (including through both feature phones and, increasingly, smartphones) and social media, presenting new ways to get to know, assess and serve customers around the world; expanding pools of data, including Big Data, that can be leveraged to assess creditworthiness and customize products across a customer life-cycle; the rapid spread of biometric or digital identity in countries from India to Peru that enable much lower friction “know your customer” compliance; a range of technology and device innovations that can be leveraged to help institutions reach more customers and improve product and service quality at scale. These forces create a variety of opportunities for fintech that can radically enhance efficiency and value of financial services delivery for the masses.

Cyber Risk Management
Cyber Risk Management is a critical component of a fintech enterprise risk strategy. It is essential to understand and prepare for various types of cybersecurity and data privacy risks and vulnerabilities, attack scenarios and related civil and regulatory obligations and liabilities. Failing to properly prepare for and respond to cybersecurity and data privacy incidents can seriously harm or even destroy an enterprise and cause serious harm, if not also potential criminal liability, for business leaders. Ransomware attacks, stolen and exposed confidential business information, lost and altered data, and system outages are all-too-common problems as companies of all sizes struggle to manage cyber risk. These challenges are intensified for multi-national corporations navigating different laws and regulations regarding online privacy, data protection and security. And a remote workforce adds greater levels of complexity. It is essential that organizations and their leaders adequately understand, anticipate, prepare for, and properly respond to, cybersecurity incidents. How an organization handles security incidents is critical to its reputation, revenue and relationships with clients, customers, regulators and others. Organizations must swiftly, efficiently and effectively address security incidents. This requires thoughtful planning, action and communication, not only during an incident, but also before and after incidents. This course will address significant trends and challenges of cyber risk management; explore case studies demonstrating successful and also failed incident response situations; discuss key legal and regulatory frameworks for understanding proper cybersecurity preparedness and crisis response; and if time permits could also include one or more guest speakers and also a tabletop exercise scenario of a cybersecurity crisis response in which the class would actively participate.

Artificial Intelligence and Machine Learning in Risk Management
This class uses a blend of academic papers, research reports, popular press and real-world cases to assess and address the various sources of risk arising within organizations using artificial intelligence (AI) or machine learning (ML) technologies. We will catalogue these risks, discuss ways to mitigate them. Students will do “deep dives” on the risks associated with using AI in HR, and the use of special boards to address ethical issues arising from use of AI.

Leadership in Fintech
This course provides: (1) Frameworks to examine leadership in fintech group/organizational settings, (2) Tools and opportunities (and reasons) to act on what you learn to be a leader. A student’s ability to analyze behavior in collective settings, and willingness to skillfully act within them, help answer a number of questions: Why do some talented people succeed, while others, equally talented, flounder? Why do some people in leadership positions prove effective, while others do not? Why do some people become leaders and others do not? This course tackles these questions. The course is based on the premise that, regardless of your position within an organization, leadership opportunities and challenges present themselves every day and that it is to your advantage to recognize and make the most of these opportunities.

Module 7 (Capstone)

The MSFT Capstone is an integrative team project that gives students the opportunity to demonstrate an understanding of the core competencies taught throughout the program and apply them to real business concerns. The result is a unified and practical case presentation on a topic of the group's choosing. This is a team-based project with approximately 4-5 students per group. Capstone work runs throughout the duration of the program, starting in Pre-Module 1. Capstone includes deliverables assigned from specific courses, as well as a charter, executive summary, first draft, and final draft. Students will practice presenting Capstone materials at various stages throughout the year leading up to the final presentation in Module 7.

Master of Science in Fintech | Course Index

Use the below links to learn more about the program's unique modular structure: