AI in Fintech - slides for plenary panel @ IJCAI-20

Copyright Open Insights © 2020 All Rights Reserved
1
AI in FinTech:
Challenges & Future
IJCAI-2020 Plenary Panel
Discussion
Usama M. Fayyad, Ph.D.
Chairman & Founder, Open Insights
Executive Director, Institute for
Experiential AI, Northeastern University
usama@open-insights.com
January 14, 2021
Moderated
By
Longbing Cao
UTS
Prepared for
IJCAI Plenary
Panel
U.Fayyad@northeastern.edu

2
Digital Transformation
● Financial Services has been undergoing major digital transformations for the last 7 decades
● FinTech is a term that represents the super-accelerated tech transformation of the last 2 decades
● AI has gotten a lot of attention in recent FinTech efforts
Simple Example of dramatic transformation in traditional finance: Accounting
○ Think about Accounting of 60 years ago vs today
○ Excel completely replaced many old jobs and “technologies” (like Index cards) but created many more higher value jobs
○ Production chain, supply chain, and inventory management has been completely changed

3
Unintended Consequence of digitization: Lost Customer Intimacy
Traditionally, businesses relied on employee interactions to understand (formally or informally)
Digitization of workflow è can no longer know:
How can we restore this intimacy on the digital channels?
● Are customers happy ?
● Are services and products being delivered
effectively ?
● Why are customers leaving us ?
● Where are they going ?
● What is making customers unhappy ?
● What is delighting them?

4
Moving from Transactional View to Intimate Intent Understanding
End of Story...
Illustrative Example: consider a typical Bank transaction
● Kelly deposits money into her account
● Kelly has good standing with the bank
What the bank knows about Kelly
Had the bank synthesized various
interactions and built an understanding
of Kelly and her intents -
● Kelly has student debt
● Kelly just got married
● Bank offers Kelly refinancing for her student loan to help
her save more and achieve intention to buy a house
faster
● Bank offers loan to Kelly to buy a house at appropriate
time
● Kelly is saving to buy a house
Kelly deposits a check into her account
What they could have known
What they could have inferred
What they could have done

5
AI and FinTech: Why
Much of finance digitization has focused on workflows, but little advance on
customer experience and interactions
● “Front Office”: Need to inject some of the human intelligence/understanding in customer interactions
o Understanding customer context
o Understanding intent, challenges, and issues
o Reduce costs of customer service operations (chatbots, problem understanding, problem resolution)
o X-sell, up-sell, reactivation, next-best-action, etc… at scale
● “Back Office”: still expensive and complex and needs intelligent automation
o Compliance
o Financial Crime
o “Operations” – e.g. forms into actions
● Going beyond human servicing capabilities
o E.g. step counters linked to insurance risks
o E.g. tracking safe driving with IoT
o Leveraging networks – social and otherwise, to embed financial transactions

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Fintech will create many novel use cases
This is a redefinition of how we think about financial services
● Payments: digital money – eCash, wallets, cryptocurrency
● Contracts: redefining how FS fits into the new digital economy – a huge opportunities to redefine
contracting, tracking, and settlements
● Accounting/book keeping: totally redefined and friction removed via integration with interactions
● Insurance re-invented: micro-transactions, risk reduction via IoT, buy-by-the-cup, etc…
● Instant everywhere: latency will disappear, instant becomes the standard
o Exactly what happened with email versus post (snail mail), chat versus calls, etc…
o New era for micro-contracts and transactions
● Refactoring of banking: micro-services, banking-as-a-service, micro-payments, smart subledgers,
etc.
Most use cases will be ones we have not yet imagined

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AI and FinTech: Why
A refactoring of how we think of financial services “actions”
● Refactoring of Banking/FS as we have known it traditionally: Break down traditional services into micro-
services, and enabling injecting them into transactions
o Understanding context
o Tracking and servicing
o New payment mechanisms, especially micro-payments
● Financial Advisory: Democratizing access and engagement to all consumers
o Complexity of products and options bewildering consumers and overwhelming advisors
o Much of advisory is about collecting template for context - self-serve is more efficient and more
convenient for consumers
● Modeling risk and future scenarios better-suited for algortihms – as long as algos are fair/responsible AI
o New ways of scoring risk, credit, and needs
o Leveraging all the information available publicly – e.g. LinkedIn and many other networks
o Better understanding of behavioral data

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FinTech: Why is AI necessary
Scale is a must – human processing is not scalable or feasible
● AI algorithms are only feasible approach to deal with “understanding” – of context and customer
o Leverage human judgement in delivery to build the right training data sets and KB
o Need to make sure AI algos are subject to Responsible AI criteria (often overlooked)
● Complexity of products requires “reasoning”: still a big challenge in AI
● Multiple ways of recognizing identity: vision, fingerprint, voice recognition, keystroke analysis, etc.
New capabilities tech/network require automation
● Novel risk and credit scoring opportunities
● Integration into microservices
● Leverage networks (e.g. social networks) and other viral services

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FinTech: Some of the big challenges for AI
Fairness and bias in algorithms can have big consequences
● Financial decisions are much more consequential than e.g. targeting ads
● Algorithms have little commonsense reasoning (or any reasoning) – typically all they know is data (with
almost no context)
● Modeling complex decisions and consequences is still a hard problem
● Advances in tech and data enable potentially deep intrusion on privacy and civil rights
Successful AI is heavily dependent on ML/DS, hence need good training
data: Data remains a huge challenge for most organizations
● Good training data is extremely expensive to get
● Just collecting and managing raw data is a challenge for most, challenging growing exponentially with
digitization, cloud, and IOT
● Data manipulation is very difficult, few understand unstructured data

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Digitization Produces 100x the Data Flux
But most businesses are not equipped to effectively manage data as an asset
How do we make
this Data work for
the business?
New economy of
Interactions is rich with
unstructured data
in fact, 90% of Data in
any organization is
UNSTRUCTURED
Without proper Data,
AI cannot work:
ML needs high quality and
granular training data

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What Is So Special About Machine Learning?
“We don’t have better algorithms...
We just have more data.”
-Peter Norvig, Google
85% of Big Data Projects fail
- Gartner
of data in any organization
is unstructured
90%
Most organizations are still in
“structured data only” world

12
Final Thoughts - Can You See the Big Picture?
Do you have a unified view of your customers?
(customer360, product360, etc…)
Do you have the context of your customer
interactions?
Can you provide data-driven personalized
offers/content?
Is data access democratized, secure, enabling
easy analytics?
Can your data enable intelligence in your
digital channels?

13
www.open-insights.com
ufayyad
OpenInsights-tech
@UsamaF
@OpenI_Tech
usama@open-insights.com
u.Fayyad@northeastern.edu
Assistant: Rana@open-insights.com
THANK YOU! & QUESTIONS
USAMA M. FAYYAD
Open Insights

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