There are about 7.5 billion people on the planet, give or take a few.
But that number pales in comparison to the number of connected devices
worldwide. According to Autonomous, a financial research
firm, people are outnumbered three-to-one by their smart computing devices — an
estimated 22 billion in total. And the number of smart devices will continue to
explode, with venture capital firms pouring $10 billion annually into
AI-powered companies focusing on digitally-connected devices.
For financial institutions, their slice of this massive AI pie
represents upwards of $1 trillion in projected cost savings. By 2030,
traditional financial institutions can shave 22% in costs, says Autonomous
in an 84-page report on AI in the
financial industry. Here’s how they break down those cost savings.
Front Office – $490 billion in savings. Almost half of this ($199
billion) will come from reductions in the scale of retail branch networks,
security, tellers, cashiers and other distribution staff.
Middle Office – $350 billion in savings. Just simply applying AI to
compliance, KYC/AML, authentication and other forms of data processing will
save banks and credit unions a staggering $217 billion.
Back Office – $200 billion in savings. $31 billion of this will be
attributed to underwriting and collections systems.
These numbers align with what other analysts and research firms have
forecast. Bain & Company has pegged the savings at around $1.1 trillion,
while Accenture estimates that AI will add $1.2 trillion in value to the
financial industry by 2035.
In the U.S. banking sector, 1.2 million employees have already been
exposed to AI in the front-, middle- and back office, with almost
three-quarters of workers in the front office using AI (even if they don’t know
it). If you include the investment and insurance industry, there are 2.5
million U.S. financial services workers whose jobs are already being directly
impacted by AI.
Use Cases for AI
Autonomous sees three primary ways in which artificial intelligence will
transform the banking industry:
- AI technology companies such as Google and Amazon will
add financial services skills to their smart home assistants, then leveraging
this data + interface via relationships with traditional banking providers.
- Technology and finance firms merge/collaborate to build
full psychographic profiles of consumers across social, commercial, personal
and financial data (e.g., like Tencent coupling with Ant Financial in China).
- The crypto community builds decentralized, autonomous
organizations using open source components with the goal of shifting power back
AI-enabled devices are already using vision and sound to gather
information even more accurately than humans, and the software continues to get
“Not only can software understand the contents of inputs and categorize
it as scale,” Autonomous explains, “it has exhibited the ability to generate
new examples of those inputs. Artists are now as endangered as lawyers and
But AI still has a way to go before a computer will become the next van
Gogh or Pollock. Today’s AI is “narrow,” meaning that the machines are built to
react to specific events and lack general reasoning capability. That said,
there are plenty of practical applications for AI that banks and credit unions
are taking advantage of today.
The most mature use cases are in chatbots in the front office, antifraud
and risk and KYC/AML in the middle office, and credit underwriting in the back
Financial institutions can use AI to power conversational interfaces
that integrate financial data and account actions with algorithm-powered
automatic “agents” that can hold life-like conversations with consumers.
Bank of America has announced that it is aggressively rolling out Erica,
its virtual assistant, to all of its 25 million mobile banking consumers. Using
voice commands, texts or touch, BofA customers can instruct Erica to give
account balances, transfer money between accounts, send money with Zelle, and
schedule meetings with real representatives at financial centers.
Biometrics and workflow and compliance automation are other strong use
cases for AI. To improve the consumer experience, AI can allow a bank or credit
union to authenticate a mobile payment using a fingerprint or replace a
numerical passcode with voice recognition.
In the middle office, AI can perform real-time regulatory checks for
KYC/AML on all transactions rather than rely on more traditional methods of
using batch processing to analyze only samples of consumers.
Perhaps the most promising application, says Autonomous, is using AI to
incorporate social media, free text fields and even machine vision into the
development of lending, investment and insurance products.
Surgery? No Problem…
But Financial Advice? Hold Up There!
Consumers remain wary of AI applications, particularly in banking. In
all honesty, consumers aren’t even sure what AI is, so perhaps they are simply
afraid of the unknown. Sadly, only 44% of consumers in a survey from SAS said they could
explain AI to someone else. And they aren’t convinced that personal data used
in AI situations is being protected, with only 35% saying they were confident.
Consumers are fine with AI in healthcare settings, but are notably less
comfortable with banks and credit unions using AI. More consumers say they
trust healthcare providers to use AI to perform surgery or suggest medical
treatment than they would trust banking providers using AI to provide financial
guidance. AI in retail also gets the nod of approval from consumers: almost
half say they are comfortable with retailers using computers and drones to fill
and deliver orders.
The only area that consumers are comfortable with banks and credit
unions using AI is in monitoring threats such as fraud, with 59% of consumers
saying that using AI was okay. The least popular use of AI was for analyzing
consumer credit history to make a credit card recommendation.
Consumer trepidation with AI in financial services could be attributed to
a lack of understanding in how AI could improve consumer experiences or make
their financial lives more convenient and even healthier. Just like banks and
credit unions face a steep AI learning curve, the same is true for consumers.
It certainly doesn’t help that every major motion picture that
incorporates AI into the plot casts the computer/robot as a heartless,
diabolical villain out to destroy mankind. One of the earliest films, 2001:
A Space Odyssey, sparked the public’s fear of AI when the computer “HAL”
turned on the crew, killing them off one by one. Hollywood blockbusters like
the Terminator series and The Matrix trilogy have trained
movie-goers worldwide to distrust any machine capable of learning. Other films
like Ex Machina and Her where female bots aspire to world
domination compound the cinematically-induced AI hysteria.
The Rise of the
Despite the public’s paranoia towards AI-powered conversational
interfaces, developers in all sectors continue to plow forward. Major advances
are being made using multiple machine learning techniques including speech
recognition and natural language processing systems that turn spoken words into
data. From this “raw material”, AI-powered platforms can extract meaning,
detect emotion, interpret context, and ultimately frame an appropriate
Such conversational interfaces are natural extensions of today’s mobile
web and, not surprisingly, Millennial consumers are more comfortable than older
generations in contacting their bank or credit union without having a
conversation with an actual human. 90% of the Silent Generation (born
1925-1945) have a preference towards human service over the phone, while only
12% of Millennials prefer phone, with nearly all others looking for chat,
social or text channels.
Finn AI is a virtual banking assistant that uses natural language to
understand what users are asking. The technology leverages multiple data
sources to extract information including data aggregators (such as MX),
core banking systems (like Temenos), credit bureaus, card networks and others.
Finn AI uses machine learning and natural language processing to create user
insights so bank customers can receive personalized advice based on their
characteristics, sentiments, and financial profiles.
Bank of Montreal has already announced that its partnering with Finn AI
to create a personal banking chatbot for consumers dubbed BMO Bolt. Bolt can
answer 250 common questions and will learn to answer additional questions as
the number of interactions it has with people increases.
And with open banking gaining traction, you can expect to see more
financial institutions roll out conversational interfaces that improve the
consumer experience in ways similar to Finn AI. Banks like Barclays with
its API Store and HSBC who has a developer
portal are encouraging third-parties to build new apps and
create more innovative platforms.
The Power to Disrupt
The focus of most fintech companies using AI is to improve the consumer
experience, but Autonomous predicts the real transformative power of AI will be
in areas tied to product development. Banks and credit unions will be able to
apply qualitative as well as quantitative data to manufacture financial
products. For example, Upstart, a fintech lending platform, employs AI and
machine learning to underwrite loans using alternative data such as schools
attended, work experience and the consumer’s web behavior while applying for
the loan online. Upstarts claims $1.9 billion in loans originated.
Upstart offers its platform for online applications,
underwriting, verification and servicing to banks and credit unions.
Digital-only BankMobile is using Upstart’s software to determine the
creditworthiness of consumers with thin or no credit files.
Click here for the original article from The Financial Brand.