Introduction to FinTech and AI & ML in FinTech: Foundations and Concepts

Summary

Professor Pipart from Su Denver introduces the world of Fintech, highlighting its rapid evolution from credit cards and ATMs to digital assets and cryptocurrencies. The lecture covers key areas in Fintech, including digital payments, blockchain, and AI's role in algorithmic trading and fraud detection. The future of Fintech is explored with a focus on DeFi, open banking, and regulatory changes, while AI's benefits and challenges in the financial industry are discussed, including data privacy and algorithmic bias.

Takeaways

• 💼 Fintech stands for financial technology, which integrates technology into financial services, covering a broad range from mobile banking to cryptocurrency.
• 📈 The fintech sector has seen rapid growth, with expectations that it will transform the entire finance industry.
• 🏦 Key areas in fintech include digital payments, blockchain and cryptocurrency, robo-advisors, insurance tech, and regtech.
• 🔐 Blockchain technology is characterized by decentralization, immutability, and security, with applications beyond cryptocurrency in various fields.
• 💹 AI in fintech plays a significant role in areas like algorithmic trading, fraud detection, customer service, and data classification.
• 📊 Machine learning, a subset of AI, is crucial for automating processes and enhancing decision-making in the financial industry.
• 🌐 The future of fintech includes developments in decentralized finance (DeFi), open banking, and regulatory changes, along with addressing security and market volatility.
• 🤖 The benefits of AI in finance include efficiency, accuracy, personalization, scalability, and better risk management.
• 🚀 The adoption of AI and machine learning in the financial sector is high, with client acquisition being a leading area of implementation.
• 🔑 Challenges in AI include data privacy, regulatory compliance, algorithmic bias, and the need for explainable AI models.

Q & A

What does 'fintech' stand for and what is its scope?

Fintech stands for financial technology, which refers to the integration of technology into offerings by financial services. Its scope covers a wide range of applications from mobile banking to insurance, cryptocurrency, and investment apps.

How has the fintech sector evolved over time?

The fintech sector has evolved rapidly since the 1950s, starting with the credit card and ATM, moving to electronic trading on NASDAQ in the 60s and 70s, online and tele banking in the 1980s, and more recently with the advent of digital assets and cryptocurrencies in the 2010s.

What are some key areas in fintech?

Key areas in fintech include digital payments, blockchain and cryptocurrency, robo-advisors, insurance tech, and regtech, which is technology that helps companies comply with financial regulations.

What is the definition of blockchain technology?

Blockchain technology is a decentralized, digital ledger that is immutable, meaning once a block is added to the chain, it cannot be deleted. It has applications beyond cryptocurrency, such as in supply chain management, healthcare, voting systems, and even the US Army.

What is the role of AI in fintech?

AI in fintech plays a role in areas such as algorithmic trading, fraud detection, customer service, and data classification. It enhances decision-making and automates processes, with over 80% of trades in the market being done by machines.

What are some benefits of AI in fintech?

AI in fintech offers benefits such as efficiency, accuracy, personalization, scalability, and risk management. It also helps in reducing biases and improving customer engagement.

What are the key languages and techniques used in AI and machine learning in fintech?

Key languages and techniques used in AI and machine learning in fintech include natural language processing, predictive analytics, deep learning, and reinforcement learning.

What is the future of fintech according to the lecture?

The future of fintech includes the growth of decentralized finance (DeFi), open banking, regulatory changes, increased adoption, and addressing challenges such as security concerns and market volatility.

What are the three generations of AI mentioned in the script?

The three generations of AI mentioned are narrow AI, general AI, and superintelligent AI. The current focus is moving from narrow AI to general AI, which is expected to have an accuracy of 96% as of the time of the lecture.

What are the challenges in AI that were discussed in the script?

Challenges in AI discussed in the script include data privacy, regulatory compliance, algorithmic bias, and the need for explainable AI. It also mentions the importance of emotional intelligence in relation to AI.

Outlines

00:00

💼 Introduction to Fintech and AI in Fintech

Professor Pipart introduces the lecture's focus on the foundations of AI, machine learning, and their applications in fintech. As a professor of finance and director of external affairs, he outlines the lecture's two main themes: an introduction to fintech and the role of AI and machine learning within it. Fintech, a blend of financial services and technology, covers a broad spectrum from mobile banking to cryptocurrency. The evolution of fintech is traced from the 1950s with the advent of credit cards and ATMs to the rise of digital assets and crypto in recent years. Key areas in fintech include digital payments, blockchain, and regulatory technology. The lecture also touches on the history of fintech, the importance of blockchain technology, and the future of fintech with decentralized finance and open banking.

05:02

🤖 AI and Machine Learning in Fintech Applications

This section delves into the applications of AI and machine learning in fintech. It emphasizes the significance of these technologies in automating processes, which accounts for over 80% of daily trades in the market. The lecture discusses the key applications of AI in fraud detection, credit scoring, algorithmic trading, and customer service. It also introduces the languages and techniques used in AI and machine learning, such as natural language processing, predictive analytics, deep learning, and reinforcement learning. The adoption of AI and machine learning in fintech is highlighted, with statistics showing the current and planned implementation rates. The benefits of AI in fintech, including efficiency, accuracy, personalization, and scalability, are discussed, along with the challenges of data privacy, regulatory compliance, and algorithmic bias. The lecture concludes with a look towards the future of AI, including the development of explainable AI, enhanced cybersecurity, and the greater adoption of decentralized finance.

Keywords

💡Fintech

Fintech, short for financial technology, refers to the integration of technology into offerings by financial services. It covers a wide range of applications from mobile banking to insurance, cryptocurrency, and investment apps. In the video, the professor discusses the evolution of fintech, starting from the 1950s with the introduction of credit cards and ATMs, to the current era where fintech is expected to transform the entire finance sector.

💡Blockchain

Blockchain is a decentralized digital ledger that records transactions across multiple computers in a way that ensures the security and integrity of the data. The key features of blockchain include decentralization, immutability, and transparency. In the script, the professor mentions that blockchain technology has applications beyond cryptocurrency, such as in supply chain management, healthcare, and even voting systems.

💡Cryptocurrency

Cryptocurrency is a digital or virtual currency that uses cryptography for security and operates independently of a central authority. The script mentions that the class will cover decentralized finance (DeFi), which is a financial system built on blockchain technology and cryptocurrencies, aiming to create an open, permissionless, and transparent ecosystem for financial services.

💡AI

Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think like humans and mimic their actions. In the video, AI's role in fintech is discussed, including its use in algorithmic trading, fraud detection, customer service, and credit scoring. The professor also touches on the future of AI in fintech, including the development of more advanced AI technologies.

💡Machine Learning

Machine learning is a subset of AI that provides systems the ability to learn and improve from experience without being explicitly programmed. It is used in fintech for tasks such as fraud detection, credit scoring, and algorithmic trading. The script highlights the importance of machine learning in automating processes and enhancing decision-making in the financial industry.

💡Robo-advisor

A robo-advisor is an automated platform or software that provides financial planning services with little to no human supervision. The script mentions that as part of the fintech curriculum, there will be a focus on robo-advisors, which are part of the financial technology that uses AI and algorithms to manage and provide financial advice.

💡RegTech

RegTech, short for regulatory technology, refers to the use of technology to help companies comply with financial regulations. The script mentions that RegTech is a key area in fintech, with the SEC (Securities and Exchange Commission) having tough regulations that technology can help navigate. RegTech is crucial for ensuring compliance and reducing the risk of regulatory fines.

💡Algorithmic Trading

Algorithmic trading is the use of algorithms to place financial orders with the aim of achieving high-speed transactions. It is a key application of AI and machine learning in fintech, allowing for the automation of trading strategies. The professor mentions that over 80% of the trades in the market are done by machines, highlighting the significance of algorithmic trading in the current financial landscape.

💡FICO Score

The FICO score is a credit score developed by the Fair Isaac Corporation, used by lenders to predict the likelihood that an individual will repay loans. In the script, the professor discusses how AI is being adopted in credit scoring, with companies like FICO using AI technology to provide scores for billions of people, making credit assessment more efficient and accurate.

💡DeFi (Decentralized Finance)

DeFi refers to a financial system built on blockchain technology that aims to create an open, permissionless, and transparent ecosystem for financial services. The script mentions that the future of fintech includes DeFi, which is a significant shift from traditional, centralized financial systems, offering new opportunities and challenges for the industry.

💡Data Privacy

Data privacy in the context of fintech refers to the protection of consumers' personal and financial information from unauthorized access and use. The script highlights the challenges in AI regarding data privacy, emphasizing the need for regulatory compliance and the importance of safeguarding sensitive financial data.

Highlights

Introduction to Fintech and AI in Fintech by Professor Pipart.

Fintech is the integration of technology into financial services, covering a wide range of applications.

The evolution of fintech has grown rapidly since the 1950s, with significant developments in credit cards, ATMs, and electronic trading.

Key areas in fintech include digital payments, blockchain and cryptocurrency, robo-advisors, insurance tech, and regtech.

Blockchain technology is decentralized, with key features like immutability and transparency.

Artificial Intelligence's role in fintech includes algorithmic trading, fraud detection, customer service, and credit scoring.

AI and machine learning are enhancing decision-making and automating processes in the financial industry.

61% of companies have already implemented AI and machine learning, with 19% planning to do so.

AI provides efficiency and accuracy in financial operations, with the potential for personalization and scalability.

The future of fintech includes decentralized finance (DeFi), open banking, and regulatory changes.

Machine learning, a subset of AI, is used for fraud detection, credit scoring, and algorithmic trading in fintech.

Key languages and techniques in AI and machine learning for fintech include natural language processing, predictive analytics, and deep learning.

Challenges in AI include data privacy, regulatory compliance, and the potential for biases in algorithms.

The future of AI in fintech involves explainable AI, enhanced cybersecurity, and greater adoption of decentralized finance.

The lecture concludes with a Q&A session for students to ask questions and engage with the topic.

Transcripts

hello students this is uh professor

pipart and today we will talk about AI

machine learning in fintech foundation

and Concepts I'm YF bonapart professor

in finance at Su Denver and I'm also the

uh director for

external uh Affairs my jobs to meet

people in the community and director of

Masters in

VCH um this lecture has two themes

introduction to ftech and then

introduction to Ai and machine learning

in fintech we're going to start with

introduction to fintech what is fintech

fintech is a short for financial

technology refers to integration of

Technology into offerings by Financial

Services the scope of fch it covers a

wide range of application from Mobile

Banking to insurance cryptocurrency and

investment apps

Evolution the sector has grown rapidly

and uh we expect that the entire Finance

sector will be transformed to ftech uh

the evolution of fintech in 1950s they

start with the credit card ATM mid 60s

70s NASDAQ introduced electronic trading

uh in 1980s we have online tail banking

tele Banking and then I want to go all

the way 2010 Google pay send lunches we

have a PayPal in uh late 90s uh we start

doing create Union and then we have a

digital asset and this

in the 2019 we have a crypto also in the

middle here uh ftech abs like stripes

venmo for PayPal which is a peer-to-peer

so this is the history of ftech key

areas in fch digital payments and when

you take our masters in fch you will

have a class about that blockchain and

cryptocurrency Robo advisor Insurance

Tech and R Tech R Tech is technology

that help companies comply with

financial ulation and we know that the

SEC security Exchange Commission have a

very tough regulation so Forbes has um

the in 2019 they have a figure uh

showing the

allocation uh personal finance

14% ftech distribution like from fintech

14% personal finance lending 16% lch

100% so you break it down into sectors

and real estate sorry real estate is

14% uh personal finance is 16% and

lending is 10% 27% is a

payment now there is a clause about

blockchain technology but in the

definition of blockchain is a

decentralized Leisure key feature de

centralization immunity and we're going

to talk about defy in this class immunit

ability once a block is added to the

chain it cannot be deleted an

application Beyond cryptocurrency

blockchain it using Supply Chain

management Healthcare and even now

voting system and even in the US Army

they start using uh crypto so here are

the key feature of a crypto this class

has nothing to do with the crypto but we

have a class in the program about

cryptocurrency artificial intelligence

in ftech now this is role of OI

algorithm trading and we're going to do

a lot of algorithm trading in this class

with the AI fraud detection customer

service and benefits and here is also

other application AI data classification

we're going to have a banner in the

Masters in fch we can have a ban program

certificate algorithm trading I will

talk about it in the next

class by the way accredit scoring this

is a big deal AI with the credit storing

and I scoring and I bought a company

called FICO f i and this company great

return for me just because I know that

they start adopting the AI technology

what does that mean adopting AI

technology and credit scoring uh so now

you can score every give score for every

person in award 7 billion people cheap

like this when you have the

infrastructure to have all of this data

with the AI technology it's easy to get

scored and that makes FICO to be a very

strong global

company the future of the fintech is a

defy and we're going to have a activity

about defi open banking regul regulatory

changes increase

adoption challenges in F is security

concern regularity hard and Market

volatility now we're going to turn to

introduction to AI in machine learning

in F which is our

class so AI is the simulation of human

intelligence in machine design machine

learning is a subset of AI and we're

going to talk about AI machine learning

and L relevance to ftech AI machine

learning or rization rizing

the financial industry by enhancing

decision making automating

process by the way speaking of

automating processing about I think over

80% of the trade in the market every day

over 80% done by

machine machine

learning key application of AI and

machine learning in ftech

um fraud deduction credit score and I

mention Fu algorithm trading will do in

this class customer service personalized

service

AI the key languages we use in AI

machine learning technique ftech natural

language processing anal Predictive

Analytics and that's we're going to do

this class deep learning which is a

subset of machine learning reinforc

learning which is a subset of machine

learning as

well I found this statistics about how

much we're adopting AI implementing AI

machine learning currently and planning

to implement so

61% in the client acquisition already

implemented 19 to go and in 18 not yet

so here are you can see the highest is a

client acquisition and robocall if you

heard about

that benefit of AI in

finac it gives you efficiency accuracy

now be careful there are three

generation of AI

narrow AI General Ai and super

intelligent

AI the artificial super intelligent now

we're talking about we move to from one

generation narrow AI we're going to the

second generation which is General AI so

it present accuracy at

96% as of uh today and by the way we're

talking about 1.5 billion parameters J

CH GPT has 1.5 billion parameters when

we go to 20 or 30 billion parameters

then the system will be more

accurate uh personalization scal

scalability I talked about Ai and FICO

and risk

management here is the benefit in AI

reduced biases better customer

engagement highly scalable

operation uh data privacy challenges in

AI data privacy Regulatory Compliance b

as an algorithm here it's important to

understand that people who wrote the

algorithm are are still

human EI emotional intelligence and AI

artificial intelligence we do we still

doing the machines at some point the

machine will do the machine himself but

this bias because it's done mostly by

white male so we expect we don't know

maybe there's some bias in the algorithm

that more appeal to one qu of the

population than others and

integration the future trained in Ai and

machine learning explainable AI

developing models that provide clear and

understanding decision age Computing

enhanced cyber security and greater

adoption of defi decentralized finance

and as I said we're going to have this

in this class well that's all what I

have for you for this lecture you're

going to have a questions please answer

the questions and thank you so much