Intro To FinTech: Innovations & Business Opportunities in Finance (2225.YR.011240.1)
General information
Type: |
OBL |
Curs: |
1 |
Period: |
S semester |
ECTS Credits: |
3 ECTS |
Teaching Staff:
COURSE CONTRIBUTION TO PROGRAM
A confluence of major developments including (i) the ubiquitous consumer
use of smartphones, (ii) technological advances in AI, ML, cryptography,
cloud-based services, etc., and (iii) a set of new regulations in the world of
finance, has created a situation where practically every area of finance is
being disrupted by the use of technology.
This course, Intro To FinTech, primarily focuses on understanding the
technology-based, disruptions and innovations that are transforming
multiple areas of finance including Banking, Lending, Payments,
Investments, Insurance, Finance Infrastructure, and Regulatory structure.
The main objective is to provide a foundation in the basic concepts and
frameworks that underlie these innovations and their business applications.
The course covers a broad spectrum of very current topics at the
intersection of tech and finance.
Course Learning Objectives
Upon successful completion of this course, students will have learned:
- What "FinTech? means and the varieties of FinTech businesses that have
mushroomed in the market
- How new data and tech are completely re-defining various financial services
- The basic features, technology, and, as applicable, the mathematics and
computational developments that help transform data into actionable
intelligence and sources of value
Core principles for exploiting these new sources of value, both by new
businesses and legacy financial institutions
CONTENT
1. FinTech Opportunities & Trends - Course learning objectives, structure, content, & evaluation
- Key sectors and trends: Mapping the FinTech eco-system and
opportunity spaces
Required Readings:
- FinTech Trends To Watch 2019 - CBInsights
- An Introduction To FinTech - Key Sectors And Trends 2016 - S&P |
2. AI and ML in Finance - Statistical pattern recognition
- Neural Networks & Deep Learning
Required Readings:
- Machine Intelligence And Augmented Finance ¿ Autonomous Next
- Neural Networks & Deep Learning ¿ Ch. 2 ¿ Michael Neilsen |
3. Raising Capital and Financing - Crowdfunding platforms & when to use them to raise capital
- The P2P Lending model
Required Readings:
- The Dynamics of Crowdfunding An Exploratory Study - Mollick
- The Business Models and Economics of P2P Lending ¿ ECRI |
4. Payments & Wallet Systems - Overview of mobile wallets and mobile payment systems
- Mobile payment services and gateways
Required Readings:
- PayTM Mall trusts fashion grocery to drive its growth ¿ Economic
Times
- Apple Pay Overview 2019 ¿ MacRumors |
5. Blockchain & DLT - How does DLT work?
- Transactions and Tamper-proofing
- Blockchain Applications in finance
Required Readings:
- Why Bitcoin¿s Blockchain Technology Could Revolutionize Supply
Chain Transparency ¿ SpendMatters
- How is Blockchain Revolutionizing Banking & Fin. Markets -
HackerNoon |
6. Digital Cash & Cryptocurrencies - What is digital cash?
- Intrinsic value in cryptocurrencies
Required Readings:
- Bitcoin A Peer-to-Peer Electronic Cash System
- Beyond The Bitcoin Bubble - NYT |
7. Programmable Blockchains - Ethereum, DApps, and DAOs
- Public and private blockchains
Required Readings:
- A Gentle Introduction to Smart Contracts
- Introduction To Smart Contracts - Solidity Docs
- The Ethereum White Paper ¿ Wiki |
8. Allocating Capital: Personal & Institutional Investing - The value of Robo-Advising
- Using MPT for Robo-Advising
- HF Trading Platforms
Required Readings:
- Wealthfront Investment Methodology White Paper - Wealthfront
- The Rise Of Crowdsourcing ¿ Wired |
9. Retail Banking & Text Analytics - A framework to understand the value levers of a digital bank
- Text Analytics, NLP, NLG, and ChatBots
Required Readings:
- Retail banking 2020: Evolution or Revolution ¿ PWC
- Text And Context Language Analytics In Finance: Ch 1-3 |
10. Final In-Class Quiz & Project Concept Presentations - In-Class Quiz
- Presentations |
Methodology
I have designed this class for 10 sessions. In each session, we will spend roughly
two-thirds of our time on discussing concepts, frameworks, and strategy. The
remaining portion of the time will be focused on discussing and analyzing a
specific case study that illustrates the relevant concepts.
For each session, I have set up (i) a set of reading materials, (ii) a case for in
class discussion and (iii) a set of questions related to the case. These will all be
available in the course site. Prior to every session, students should have read the
required readings and come prepared to discuss the questions related to the
case.
Student class participation is crucial for getting the most out of class sessions.
Reading the set of pre- assigned reading material prior to each class is essential,
as it serves as the foundation for the class discussions. It is important that you
come to class prepared to discuss relevant concepts and defend your analysis of
the assigned case. High quality discussions and debates in the classroom will
significantly enhance your learning experience.
There is an extensive amount of reading required for this course. Students must
be willing to dive deep into the material and approach the subject material with
analytical rigor. In addition to my lecture notes, we will use a wide array of
topical content including blogs, posts, articles, cases, and book chapters.
Suggested independent study hours: 2-3 hours prior reading prep for each
session, 1 hour at the end of each day, about 6-8 hours of independent work for
the individual assignment, and about 20 hours (total) of group work for the
group project.
Guidelines for student in-class behavior: I expect each of you to:
- attend all 10 sessions - absences will seriously hurt your contribution
grade,
- be in your seats on-time for each session,
- keep your laptop computers shut while you are in class,
- turn off your mobile phones during the class sessions, &
- sit in the same seat for the duration of the course with your name-card
prominently displayed - as this will aid my evaluation of your in-class
contributions.
Assessment criteria
Class Contribution (20%):
Individual Assignments (30%):
Final In-Class Quiz (20%)
Group Project (30%)
Assessment criteria
The course assessment breaks down into the following categories:
Class Contribution (20%): View class participation both as an opportunity to
ask questions to enhance your understanding as well to demonstrate your critical
analysis of the material. I will call on students at random in class to discuss
specific topics and questions, and will closely track meaningful class contribution.
Individual Assignments (30%): The individual assignment is designed to test
the students' understanding of specific Fintech concepts, frameworks and
techniques. The best way to ensure good performance here is by preparing for
each of the classes by reading all the required readings, understanding the class
notes, and by active contributing to the class discussions.
Final In-Class Quiz (20%): This is a quiz in the last session where you will be
tested on your overall learning of the basic concepts of digital marketing from
the previous 9 sessions.
Group Project (30%): The group project enable teams to demonstrate their
understanding of the class concepts, as well as their ability to apply these
concepts. A typical group project will revolve around developing Digital Marketing
strategy for a particular product or product-line or store. We will divide the class
into small groups during Session 01 (The final group size number will depend on
overall class size). Grading of these group assignments can potentially include
peer evaluation.
Bibliography
FACULTY LEADING THE COURSE
Lil Mohan is an entrepreneur and an academic. He brings to his work a variety of
experiences from Amazon, Intel, Motorola, Sun Microsystems, The University of
Chicago Booth School of Business, London Business School and two successful
high-tech start-up companies: Junglee and Snapstick.
In his career so far, he and his teams have successfully brought to market
several new products and services into the E and M-Commerce, Retail, Digital
Media & Entertainment, and Mobile SaaS sectors.
In addition to teaching at ESADE, Lil is Adjunct Professor of Marketing at the
University of Chicago Booth School of Business, the London School of Business,
the Indian Institute of Management, Ahmedabad, and at the Indian School of
Business where he teaches Digital & Social Media Marketing, Mobile Commerce,
New Product Innovation, and FinTech to MBA and Exec. MBA students.
He also consults independently for technology companies in the areas of product
design, product marketing, and marketing strategy. Before this, Lil was
Managing Director for Intel's 4G mobile wireless program in Emerging Markets,
prior to which, he was VP of Business Strategy at Motorola's Mobility division.
Earlier, Lil was General Manager of Amazon's mobile platform BU, where his
team built the world's first retail M-commerce application Amazon Anywhere,
enabling customers to find and buy products on their mobile devices. Lil has also
been a partner at J.P. Morgan Partners Advisors, where he co-managed a
venture fund.
Lil has a PhD in EE from Purdue University, a Post-Graduate Diploma in
Management from the Indian Institute of Management, Ahmedabad, India, and a
BS in EE from the Indian Institute of Science, Bangalore, India.
BIBLIOGRAPHY
The list of required readings is included. All class presentations, cases, articles,
blogs, book excerpts etc. will be be available on the course site. There are no
required textbooks. However, I recommend that you read the following books:
- The Truth Machine - The Blockchain and the Future of Everything, Harper
Collins, 2018
- Bitcoin and Cryptocurrency Technologies A Comprehensive Introduction,
Princeton University Press, Princeton, NJ, 2016
- Mastering Bitcoin - Unlocking Digital Cryptocurrencies, O'Reilly, 2015
- Mastering Etheruem - Building Smart Contracts & Dapps, O'Reilly, 2018
- Machine Learning For Humans - Edited by Sachin Maini, 2017
- Make Your Own Neural Network - Kindle Edition
- The Elements Of Statistical Learning - Data Mining, Inference, and
Prediction, 2nd Ed., Springer, 2016
