10 ways artificial intelligence is transforming banking

9/13/2019 10 ways artificial intelligence is transforming banking
https://medium.com/@usmsystems23/ai-bankability-10-ways-artificial-intelligence-is-transforming-banking-b13e57f9b55c 1/9
10 ways arti cial intelligence is
transforming banking
usm systems
Jul 7 · 9 min read
With plenty of post-recession anti-banking sentiment still lingering, it’s common to see
fintech and traditional banks framed in oppositional terms. There’s some truth to that,
especially with disruption-minded digital-only banks, but technological innovations
have transformed banking of all stripes — and nowhere is that clearer than with
artificial intelligence.
AI has impacted every banking “office” — front, middle, and back. That means even if
you know nothing about the way your financial institution uses, say, complex machine
learning to fend off money launderers or sift through mountains of data for fraud-
related anomalies, you’ve probably at least interacted with its customer service chatbot,
which runs on AI.

Read on to learn how else AI is transforming the way banks operate, from investment
assistance and consumer lending to credit scoring, smart contracts and more.
Customer Support & Front Office
Customer Support & Front Office
Like fabric softener and football, banks — or at least banks as physical spaces — have
been cited as yet another industry that’s being killed by those murderous Millennials.
Indeed, nearly 40 percent of that generation don’t use brick-and-mortar banks for
anything, according to Business Insider. But consumer-facing digital banking actually
dates back decades, at least to the 1960s, with the arrival of ATMs.
Since then, clients’ customer support expectations haven’t really changed in terms of
what they expect, but how they expect them is another story. Artificial intelligence has
clearly impacted this landscape, with AI-enabled chatbots and voice assistants now the
norm at major financial institutions. We’re also seeing AI impact biometric authorization
and, for those who enjoy the occasional throwback visit to a physical bank, AI-enabled
robotic help.
Below are a few of the players having an impact on this field.
Kasisto
Kasisto
Industry: Conversational AI
Location: NYC
How it’s using AI: Digital-first banks — sometimes dubbed “challenger banks” or “neo-
banks” — have been making headlines and attracting major investors in certain parts of
the globe, especially the UK, over the last several years. They’ve yet to flourish in the
States as they have elsewhere, but Kasisto is one of the companies that’s done the most
to midwife the rise, and it’s based here in the States.
Kasisto’s major contribution is its conversational AI platform, KAI, which banks can use
to build their own chatbots and virtual assistants. It’s rooted in AI reasoning and natural-

language understanding and generation, which means it can handle sophisticated
questions about finance management that other bank customer-service digital assistants
— Bank of America’s Erica, for example — can’t.
Kasisto has so far backboned AI assistants for several prominent banking institutions
(including the UAE-based digital bank Liv., DBS Bank, Standard Chartered Bank, and
TD). The bank’s KAI-based bot walks customers through how to make international
transfers, block credit card charges and transfer you to human help when the bot hits a
wall.
Affectiva
Industry: Artificial Intelligence, Software
Location: Waltham, Mass.
How it’s using AI: One of the world’s most famous robots, Pepper is a chipper maître d’-
style humanoid with a tablet strapped to its chest. Debuting in 2014, Pepper didn’t
incorporate artificial intelligence until four years later, when MIT offshoot Affectiva
injected it with sophisticated abilities to read emotion and cognitive states. Following
that upgrade, HSBC introduced it on bank floors — including, last year, at HSBC’s
flagship branch on Fifth Avenue in New York. It has since been rolled out at Miami and
Beverly Hills locations as well.
Pepper primarily handles hosting duties for HSBC — benign greeter basics like teaching
customers how to open accounts, cracking jokes, relaying credit card details and more.
Still, if an emotion-reading and -mimicking humanoid sounds like a prelude to the robot
apocalypse, skeptics can take heart in Pepper’s still very evident limitations. In a recent
video, above, Pepper repeats a truly bizarre response whenever it’s confusing: It
recommends a taco.
HooYu
HooYu
Industry: Software

Location: London
How it’s using AI: Biometrics has long since graduated from the realm of sci-fi (think,
Blade Runner ‘s iris scanners) into real-life security protocol. Chances are, with
smartphone fingerprint sensors, one form is sitting right in your pocket or purse. At the
same time, biometrics like facial and voice recognition are getting increasingly smarter
as they intersect with artificial intelligence, which draws upon huge amounts of data to
fine-tune authentication.
The security boons are self-evident, but these innovations have also helped banks with
customer service. One notable recent example is NatWest, which in June became the
first major U.K. bank to allow customers to open accounts remotely with a selfie. AI-
powered biometrics — developed with software partner HooYu — the match in real-
time an applicant’s selfie to a passport, government-issued I.D. card or other official
photo identification document.
That’s a standard operating procedure for the digital, mobile-only upstart banks that
have popped up in the last few years, but its arrival on high street proves that users’
desire to untether even the application process from brick-and-mortar branches is no
niche request.
Simudyne
Simudyne
Industry: Investment Banking, Simulation
Location: London
How it’s using AI: Automation hit investment banking earlier than other bank sectors —
and it hit hard. Probably the most famous example of that is this: In 2000, there were
600 traders at the Goldman Sachs U.S. cash equities trading desk. In 2017, only two
remained. But the result wasn’t a gutting so much as a shift: The firm has added
thousands of computer engineer jobs.
That shift also hit another massive banking institution, Barclays, which has doubled
down on advanced technology — specifically AI. The firm led a recent $6 million

funding round for Simudyne, a tech provider that uses agent-based modeling and
machine learning to run millions of market scenarios.
Simudyne’s platform allows financial institutions to run stress test analyses and test the
waters for market contagion on large scales. The company’s chief executive Justin Lyon
told the Financial Times that the simulation helps investment bankers spot so-called tail
risks — low-probability, high-impact events.
Fraud Protection & Middle Office
Fraud Protection & Middle Office
While artificial intelligence hasn’t dramatically reshaped customer-facing functions in
banking (at least relative to other service industries), it has truly revolutionized so-called
middle office functions.
The middle office is where banks manage risk and protect themselves from bad actors.
That includes fraud detection, anti-money laundering initiatives, and know-your-
customer identity verification. And sometimes that means incorporating AI into legacy,
rules-based anti-fraud platforms.
But some of the most innovative and secure countermeasures are other, from-the-
ground-up models, built by companies like the ones below.
Ayasdi
Ayasdi
Industry: Artificial Intelligence, Fintech
Location: Palo Alto, Calif.
How it’s using AI: Up to $2 trillion is laundered every year — or five percent of global
GDP, according to UN estimates. The sheer number of investigations coupled with the
complexity of data and reliance on human involvement makes anti-money laundering
(AML) very difficult work. It’s also expensive. AML compliance costs shot up more than
50 percent between 2015 and 2018.

Ayasdi ‘s AI-powered AML incorporates three key advancements: intelligent
segmentation, or optimizing the data-sifting process to produce the fewest number of
false positives; an advanced alert system, which auto-categorizes alert priorities; and
advanced transaction monitoring, which uses machine learning to spot suspicious
anomalies.
Case in point: Ayasdi’s AML AI was able to process hundreds of data points (rather than
just the usual 20 or 30 transaction categories) for Canada’s Scotiabank and for Italian
banking group Intesa Sanpaolo, purportedly resulting in a massive drop in false-positive
alerts.
Socure
Socure
Industry: Artificial Intelligence, Fintech
Location: NYC
How it’s using AI: “Know your customer” is pretty sound business advice across the
board. It’s also federal law. Introduced under the Patriot Act in 2001, so-called KYC
checks comprise a host of identity-verification requirements intended to fend off
everything from terrorism funding to drug trafficking. They’re also commonly done in
tandem with anti-money laundering efforts.
Socure ‘s identity verification system, ID+ Platform, uses machine learning and artificial
intelligence to analyze an applicant’s online, offline and social data to help clients meet
strict KYC conditions. The system runs predictive data science on information such as
email addresses, phone numbers, IP addresses and proxies to investigate whether an
applicant’s information is being used legitimately.
DataVisor
DataVisor
Industry: Big Data, Machine Learning, Fraud Detection
Location: Mountain View, Calif.

How it’s using AI: Even though most banks implement fraud detection protocols,
identity theft and fraud still cost American consumers billions of dollars each year.
As cyber-cheats become increasingly sophisticated (manipulating identity information
through account takeovers, exploiting cloud server IP addresses), financial institutions
look to AI for help. DataVisor ‘s machine learning uses big data and so-called clustering
algorithms in real-time to counteract application and transaction fraud. The company
touts a 94 percent fraud detection rate and claims a top 15 U.S. bank among its clients.
Lending & Risk Management
Lending and Risk Management
A study published in May by U.C. Berkeley researchers titled “Consumer-Lending in the
FinTech Era” came to a good-news-bad-news conclusion. The good news? Fintech
lenders discriminate less than lenders overall by about one-third. The bad news? They
still discriminate. So while things are far from perfect, AI holds real promise for more
equitable credit underwriting — as long as practitioners remain diligent about fine-
tuning the algorithms.
Beyond credit scoring and lending, AI has also influenced the way banks to assess and
manage risk and how they build and interpret contracts.
ZestFinance
ZestFinance
Industry: Artificial Intelligence, Big Data, Credit Underwriting
Location: Los Angeles
How it’s using AI: Redlining, the illegal denial of credit or home loans because of race,
stands as one of America’s great post-war shames. But lending practices are often tainted
by bias even when explicit discrimination isn’t so apparent, like when high-cost loans
notoriously and disproportionately affected minorities during the subprime mortgage
crisis. As ZestFinance founder and former Google CIO Douglas Merrill told Forbes,
“[Credit] models are by nature very biased. The ability to make decisions that are biased
is an epidemic.”

ZestFinance’s AI-based software purportedly generates fairer models, essentially by
downgrading credit data that it has “learned” results in unfair decisions, thus lessening
the weight of some traditional (but not entirely reliable) metrics like credit scores.
Of course, artificial intelligence is also susceptible to prejudice, namely machine learning
bias, if it goes unmonitored. As Merrill recently said in testimony to the House Financial
Services Committee Task Force on Artificial Intelligence, “lenders put themselves,
consumers and the safety and soundness of our financial system at risk if they do not
appropriately validate and monitor ML models.”
JPMorgan Chase
JPMorgan Chase
Industry: Investment Banking
Location: NYC
How it’s using AI: If you’ve accepted a job offer, inked an apartment lease or signed any
other kind of contract in the last few years, there’s a good chance you used an electronic
signature platform that either incorporated AI or was on its way to doing so. (See
DocuSign, perhaps the most ubiquitous provider, which is boosting its AI integration to
help parties find buried risks hiding within agreements.)
Banks have latched on, too. JPMorgan Chase in 2016 unleashed unsupervised machine
learning on its internal legal documents to quickly collect important data and extract key
clauses. “In an initial implementation of this technology, we can extract 150 relevant
attributes from 12,000 annual commercial credit agreements in seconds compared with
as many as 360,000 hours per year under manual review,” the company wrote in its
2016 annual report.
The next frontier? AI-powered smart contracts. They’re still a relatively new
development, but one that will evolve significantly as more institutions — like JPMorgan
— dip their toes into cryptocurrency.
Feedzai

Feedzai
Industry: Artificial Intelligence, Risk Assessment, Risk Management
Location: San Mateo, Calif.
How it’s using AI: In the age of instant payments, the idea of waiting for a purchase to
“clear” will one day seem as antiquated as an abacus. Increasingly, consumers expect
their accounts to immediately reflect when they’ve bought something. At the same time,
there are cybercriminals working tirelessly to find the newest, most effective way of
swiping someone’s identity and sensitive information.
In an attempt to combat this, more and more banks are using AI to improve both speed
and security. Take data science company Feedzai, which uses machine learning to help
banks manage risk by monitoring transactions and raising red flags when necessary. It
partnered late last year with Citibank, introducing AI technology that watches for
suspicious payment behavioral shifts among clients before payments are processed.
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10 ways artificial intelligence is transforming banking

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10 ways artificial intelligence is transforming banking