Fintech Lecture 1: Intro of Key technological Trends affecting Financial World
“Technology-enabled innovation in financial services associated with new business models, applications, processes or products, all of which have a material effect, is Fintech’’ — Financial Stability Board
History
Money and ledgers were initial financial technology. It enabled financial institutions of that time to serve their customers better.
The telegraph came in 1830 and by the 1860s/70s people were sending money using this innovation. It makes the first form of digital money about 140 years old. It was the fintech of that time. The concept of digital money is not new, now it’s just wireless and quick.
Financial World
The flow of money between those who have it and those who want it is the Financial world and it is flowing in an hourglass with the number of trillions of dollars. Another type of category of these 2 types of people can be, one those who want to have risks and the other who want to get rid of them. So, if you are in this hourglass and if you have few grains of sand in your hand, it’ll be a lot and that’s what fintech companies are trying to do.
In the US 7.4% of the economy or $1.5 trillion in revenue from finance and that is the American sand in the hourglass of the financial world.
Q. What are the major technological trends materially influencing the provision of financial services?
ML/AI for identifying patterns in user data, OpenAPI for Opening up the banks ledgers and their data because data is new oil in business and NLP for robotic advisors and chatbots.
Actors of Financial World:
- Big Finance: Banks
- Big Tech: FAAMG
- Startups: Stripe, Razorpay
- Official Sectors: Government
China’s AliPay and WePay dominate 90% of retail payments in China, similar to M-Pesa in Kenya.
Why? The banking structure of these countries are less developed compared to America and Europe which gives big tech companies and startups a chance to experiment with their product and give a user-friendly solution for faster fintech(mobile payment) adoption. Because they don’t have to start from scratch and had an edge even when they came late. They made financial institutions and services easy to access.
For Example, subway systems of China and Taiwan are a little better because they came later compared to New York subway systems. They had lessons and examples to learn from.
Technology in Finance:
3 component of Big tech are DNA (Data-Network-Activities)
Mobile banking has been in the market for almost a decade but what gave fintech an edge is UI/UX. Startups made things convenient for users. Similar to starting something over the cloud rather than building your data warehouse. Technology is making everything convenient now.
Q. How much physical cash you have spent in last 2–3 days ?
This is the reality of the digital world. Fintech made payment so easy that it’s fun now which is good for the economic growth of the country as well.
Financial institutions are among those who use the least cloud. Major banks still hold their data in their data-centers. Because when it comes to money, the hardest thing to earn is Trust and no bank wants to break that. Not in the case of a startup where they have to deal with a very strong ‘Why’.
Why should I build my data-center if I can rent it because we are not keeping user’s money, Yet.
Credit cards were invented in the 1940s and that was fintech which maxed out with contactless cards in the 2000s. The next version is a conversational interface and that will Cutting edge. This application will solve customer's queries without having humans at the end.
Bank of America has ‘Erica’ and it is like Siri/Alexa of Banking.
Credit Karma, a 10 years old company, gives you a free credit score sold at $7B. They had revenue of $1B in 2019. They made money making a data stream of 105M American credit files. They made credit files even when the user is not their customer.
In the last few decades, we have shared lots of personal information that society had never seen. It can be on social media platforms or free credit scores where we are showing our financial data to companies to give us a good number if we are paying our bills on time.
Technology enabled privacy but it can take it away as well.
Public Policy in Finance
We have digitized our financial world with online and mobile banking in the last 30 years and the corona crisis pushed this change even faster. ML is the next big thing because it can extract patterns from data in a way that we couldn’t before and it can not be linear every time.
When Apple launched Credit cards and it seems like husbands were getting more credits than wives of the same household. So biases, privacy and explainability all came with AI/ML in finance.
Fair Credit Reporting ACT(1970) and Equal Credit Opportunity ACT(1974) say that you can not deny somebody’s credit because of their race, ethnicity, color, gender and other protected attributes. It also says that if you are denying credit, you should be able to explain why. That same act is required in the age of AI/ML as well because it is difficult in the case of machine learning and deep learning models.
Facebook is a free app that earns money by selling data as a service. They have 2B+ users. If they add one more activity, they’ll have even more data and they can commercialize that as well.
In the US credit cards alone are a $1T+ asset class controlled by Big finance where personal lending is $160B+ and increasing rapidly with the help of startups because Big finances are not innovating enough. Almost 50% of Fintech startup investment was for Payment and credit/lending products.
In 2020, AI/ML and OpenAPIs are more invested in finance compared to Blockchain and it’s up to us if we want to shift that. Whatever changes it may be, it’ll be bullish for some and bearish for some. Some trends are disrupting the traditional financial market, for example, DogeCoin. A cryptocurrency powered by memes and the world’s richest man as its promoter. But we never know if trends of today might become the industry of tomorrow.
Lecture 2 — AI and AI in finance —Lecture 10 — Corona Crisis and Conclusion
