The document discusses the use of artificial intelligence and machine learning in the financial industry. It covers emerging trends like increased regulations, growth of digital technologies, and the emergence of AI/ML. It also discusses key concepts like big data, different types of machine learning (supervised, unsupervised, reinforcement, deep learning), and applications in areas like portfolio management, algorithmic trading, fraud detection, and chatbots. The future of AI in finance is seen as promising with potential for more widespread use of these technologies across various business problems in finance and other industries.

1. AI in
Finance
Evolve Machine Learners

2. What is Machine Learning?

3. About Industry:
In the Financial industry we address three
primary segments:
● Capital Market Banking
● Consumer Banking
● Insurance Industry
● Stock Market

4. Emerging Trends:
● Increased risk management, requirements
and regulations
● Growth of agile, mobile and web based
technologies
● Emergence of AI, Machine Learning and
Deep Learning.

5. Financial institutions are the perfect
example of the term
Big Data
But
What is Big Data?
Big Data in Finance?

6. 3 Vs of Big Data

7. AI in Big Data:

8. Supervised Learning:

9. Unsupervised Learning:

10. Reinforcement Learning:

11. Deep Learning:

12. Deep Learning:

13. Drivers of AI in Finance:

14. Drivers of AI in Finance(Contd.):

15. Use cases of AI in Finance:
● Sentiment Indicators
● Trading Signals
● Fraud Detection
● Credit Scoring
● Insurance
● Client-Facing Chatbots
● Portfolio Management

16. Portfolio Management:

17. Algorithmic Trading:

18. Algorithmic Trading:

19. Algorithmic Trading(Contd.):

20. Fraud Detection:
● Machine Learning
○ Logistic Regression
○ Decision Trees
○ Random Forest
○ Clustering
● Deep Learning
○ Recurrent NN
○ LSTMs

21. Chatbots:
Why banking needs chatbots?
● Better Customer Experience
● Keep up with changing customer behaviour
Banking Chatbot use cases:
● Personal Banking Services
● Uninterrupted customer support
● Customer Feedback and Measurements
● Delivering Personalized Marketing
● Employee Self Service

22. World's Biggest Banks using
Chatbots:
● Bank of America(Erica)
suggests ideas how a customer can save
money, gives reports on their FICO score,
and encourages payment of bills within
the banking application
● JP Morgan(COIN)
a bot which can analyze complex
legitimate contracts quicker and more
proficiently than human lawyers. The bot
has helped JPMorgan spare more than
360,000 hours of labor.
● Capital One(ENO)

23. A peek into Machine Learning in
Square:
● Risk management and fraud
detection
● Square Capital business
lending
● Predicting best product for
sellers’ needs
● Predictive Customer support

24. Future of AI in Finance:
The uses of AI and machine
learning should continue to be
monitored. As the underlying
technologies develop further,
there is potential for more
widespread use, beyond the
use cases discussed. It will be
important to continue
monitoring these innovations
and to update this assessment
in the future.

25. ● Jobs
● Freelance
● AI Startups
How can you build your career in AI &
Machine Learning?

26. Jobs
● Artificial Intelligence jobs posted on Linkedin.
5000+
● Average AI job commands six figures.
$100,000 - $130,000 (per year).

27. AI Job Positions
Source: glassdoor.com

28. Freelance
● AI & Machine Learning jobs posted only on upwork
today.
200+
● Average per hour rate of an AI freelance engineer:
$40-$100 on upwork

30. Freelance AI Engineers on Upwork and their per
hour rates

34. AI Startups
AI will produce world’s first
Trillionaires. -Mark
Cuban

35. A New Trend
Trend is changing from
“Every business is a software business”
To
“Every business is an AI Business”

36. There is an AI solution to every
business problem
- Finance
(Safer Trading, Fraud Prevention, Personalized Investment Plans)
- Marketing
(Automating Repetitive Tasks, Sales Through Images and Videos, Content
Generation)
- Customer Support
(Handling Multiple Customers at a Time, More Effective Phone Support)
- Medicine
(Mining Valuable Information From Untapped Data, Effective Treatment Plans,
More Efficient Primary Care)
- Real Estate
(Better And Faster Communication, Automating Property Valuation, Promoting
Rental Booking, Relevant Product Recommendations, Customer Engagement)
- And More...

37. How does Evolve Machine Learners
fit in?