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Separating Hype from ROI:
A Banking AI Deep Dive
Daniel Faggella
CEO at Emerj Artificial Intelligence Research

2.
Presentation Outline
● Background in brief
● Research overview
● An overview of AI investments and banking AI
initiatives
● (Takeaway 1 + 2)
● Vendor geo analysis
● An overview of AI claims and hype
● (Takeaway 3)
● /end

3.
At Emerj, we have a singular focus:
Mapping the applications of AI to help
leaders execute on winning AI strategies.
Global organizations trust us to support their AI
goals and strategies with critical data and insight
(World Bank, global healthcare firms, etc).
Presenting our AI Research at
United Nations HQ, NYC
Emerj Artificial Intelligence Research

4.
emerj.com @danfaggella
Emerj Research Services

5.
emerj.com @danfaggella

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Research Overview
● 6 months of research
● ~80 AI in banking vendor companies
● ~50 AI use-cases within global banks
● 80+ pages of insight, graphs, analysis
● 48+ executive and researcher interviews
● Research advisors for this report include two
AI PhDs, and AI experts, and leaders from:

7.
What are the data,
talent, and money
requirements for the
application?
Ease of
Deployment
The particular ability
granted to a business
through the use of an AI
technology or tool (Ex:
Conversational
interfaces)
How credible is the ‘AI
talent’ in a company?
Expertise and
Funding
The business department
or divisions into which
the AI capability is being
applied.
Does this application
deliver a financial
return, or is it
speculative?
Evidence of
Returns
The kind of technical AI
approach is behind the
product offering (Ex:
Natural language
processing).
AI Approach Capability Functions Type of
BankingTT
AI Vendor Segmentation
What is the extent of
real-world product
adoption for each AI
technology and
vendor?
Evidence of
Adoption
Ex: Retail banking,
investment banking,
commercial banking
Type of Banking

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emerj.com @danfaggella
(Claimed AI initiatives of top banks)

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emerj.com @danfaggella
(Actual funds raised
by banking AI
vendor function)

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● Banking communications and press releases show
conversational interfaces accounting for 38.87% of the
AI use-cases at banks.
● Emerj's AI in Banking Vendor Scorecard and Capability
Map found conversational interface vendors score lowest
in terms of funding and the AI talent they employ (2.4 out
of 4.0).
● Compliance, Risk Management, Fraud and
Cybersecurity AI applications account for the vast
majority of banking funds raised by AI startups, despite
few claimed use-cases within banks.
Misconception

11.
● There is nothing more hyped in the AI in banking
landscape than chatbots.
● Chatbots are easy to deploy in a “sandbox”, and banks
want to brag about customer-facing applications.
● This is what we call “The Lens of Incentives”: Enterprises
talk about AI applications that make them look good in
the eyes of their customers and investors, not the AI
applications where they are actually investing in most.
Takeaway 1:

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● The low-hanging fruit for near-term AI ROI in banking
can be summed up as: Anomaly detection
● Finding stand-out patterns in a sea of noise is the perfect
use-case for machine learning. Fraud, cybersecurity and
compliance-related tasks fit this bill perfectly, and they
are the best-funded AI functions in banking today.
● This is often the best place for a bank to begin an AI
initiative, because IT teams, IT budgets, and existing
automation initiatives are often strong within the
compliance and fraud function.
Takeaway 2:

13.
● 61% of AI vendors are in USA
● UK accounted for 15.5%
● Other countries nominal
AI Vendor - Geo Analysis
● Of the $2.5 billion dollars raised by
US banking AI vendors
● 56% raised by companies in CA
emerj.com @danfaggella

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emerj.com @danfaggella
(Actual funds raised
by banking AI
vendor function)

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AI Vendor - Geo Analysis
emerj.com @danfaggella
(Actual funds raised
by banking AI
vendor function)

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● Newer and smaller AI banking vendors are the most
likely to exaggerate their AI claims.
● This is for two primary reasons:
○ Unestablished firms have less to lose by lying and
exaggerating
○ Smaller firms with less experience drastically
underestimate the barriers to AI adoption in banking
● Lesson: Unless you want to be a guinea pig, work with
companies that have a track record and existing
use-cases in banking (not in other sectors). Look for firms
who have raised $30MM or more).
Takeaway 3:

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That’s All, Folks
Free sample download:
www.emerj.com/b1
Research inquiries:
research@emerj.com