Practical approaches in leveraging AI in startups

1. Practical approach to
leveraging AI for
Fintech Startups
Vidhya Chandrasekaran
AGBI - Greatlakes

2. Contents
Evolution
Economics
What is AI and what is not
Applications in Fintech
Key Ingredients
Approaches
Gotchas

3. AI is the new Electricity
• Digitalization
• Mobile Networks
• Cheaper Hardware ( through Cloud )
• Tools that simplifies/Nullifies development cost and time

4. Economics
• $13 Trillion by 2030
• 15% of the Startups are in AI
• 76% of companies are already in AI
• 26% GDP growth by 2030
• 72% growth in US Venture capital
funding in one year
* McKinsey, Adobe

5. What is AI and what is not

6. • Right understanding helps establish Trust with
customers
• Stand alone Big Data Processing and Analytics
is different from AI
• 40% of the 2840 AI startups surveyed did not
use AI

7. Ingredients for an AI Cake
Idea
Data
Skills

8. AI in Fintech

9. Machine Learning for
Product
Recommendation
• Online Recommendation in
webpages and mobile apps
• Offline Recommendations
in campaigns
• Recommendations Based
on past products and
present behavior and
products by other similar
users

10. Bots
• Simple to complex operations can be performed
• Customer service queries to balance enquiry to fund transfer
• Easy API integrations from popular Cloud Service providers
• Bank of America’s Erica has completed 35 million requests in
one year

11. Some are more equal – Lead scoring
• Take right action at the right time for the
right customer
• Harness Real time streaming, Power of Big
Data and Top-notch models
• Integrations of multiple work-flows is the
keys

12. Risk Scoring
• Risk scoring for informed decision making
• Past data of the customer and data of similar profiles

13. Connect the
dots
• Building the pipeline to other
systems
• Data transport between decision
making systems

14. Gotchas

15. Interpretations
Hmm? Which weight of
which neuron in which
layer skewed my
results?

16. Ethics and compliance
• Not all the customer data can be used in a model – Beware of
Regulations
• Is the model providing fair results
• Explicit and Inexplicit bias go undetected in most cases
Recent regulatory actions involving fintech

17. Easy does it
• Step by Step approach – Start with simpler problem
statements and solutions and slowly build upon that
• Complex solutions needs high expertise for maintenance
which comes with experience
• Early winning helps keep and build momentum
• Building scalable solutions for smaller problems helps in
long run

18. Tools
Photo by Ephramac / CC BY-SA 4.0

19. Tools
Tool
Development Framework and Libraries Sklearn, Tensorflow, Pytorch, Pandas, Numpy
Language Python, R, Scala
Auto ML H20, Google
Chatbot Development IBM Watson, Google Dialogflow
Deployment/Dev tools Amazon Sagemaker, Google ML Engine
Cloud GPU’s Google Colab, Paperspace, Amazon P2
Learning Coursera, Edx, fast.ai, khan Academy, Udacity
Cloud Providers Amazon, Microsoft ,Google
API( NLP, Computer vision ) Google, Amazon
** List not Exhausive

20. Thank you