www.mx.com Open in urlscan Pro
2600:9000:20eb:4400:1c:b603:b980:93a1  Public Scan

Submitted URL: https://leap.mx.com/NDcxLUZLVy0zODkAAAGT-hcXUdbbdiIJavBSeqnvxvTru9OxnWGmfxNH8JpTHpwcazio4AicbOmgYCHBEpU1FedhZqg=
Effective URL: https://www.mx.com/guides/risks-rewards-ai/?mkt_tok=NDcxLUZLVy0zODkAAAGT-hcXUc-CWkRiqYJlf9_Ew-6HitKFX8XhfxhyRIomTYz...
Submission: On June 27 via api from US — Scanned from DE

Form analysis 2 forms found in the DOM

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      <div class="mktoFieldWrap mktoRequiredField"><label for="FirstName" id="LblFirstName" class="mktoLabel mktoHasWidth" style="width: 100px;">
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      <div class="mktoFieldWrap mktoRequiredField"><label for="Title" id="LblTitle" class="mktoLabel mktoHasWidth" style="width: 100px;">
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          <option value="Chad">Chad</option>
          <option value="Chile">Chile</option>
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          <option value="Christmas Island">Christmas Island</option>
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          <option value="Congo, the Democratic Republic of the">Congo, the Democratic Republic of the</option>
          <option value="Cook Islands">Cook Islands</option>
          <option value="Costa Rica">Costa Rica</option>
          <option value="Cote d'Ivoire">Cote d'Ivoire</option>
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          <option value="Cuba">Cuba</option>
          <option value="Curaçao">Curaçao</option>
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          <option value="Denmark">Denmark</option>
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          <option value="Ecuador">Ecuador</option>
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          <option value="El Salvador">El Salvador</option>
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          <option value="Falkland Islands (Malvinas)">Falkland Islands (Malvinas)</option>
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          <option value="French Guiana">French Guiana</option>
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          <option value="Gabon">Gabon</option>
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          <option value="Georgia">Georgia</option>
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          <option value="Gibraltar">Gibraltar</option>
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          <option value="Heard Island and McDonald Islands">Heard Island and McDonald Islands</option>
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          <option value="Qatar">Qatar</option>
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The Risks and Rewards of AI in Banking






THE RISKS AND REWARDS OF AI IN BANKING

Turn on the television. I bet you can find at least one movie about artificial
intelligence’s potential impact on the world — both for good and for bad. The
good includes everything from Disney’s WALL-E to Marvel superheroes. The bad
ranges from iRobot to Skynet in the Terminator movies. Even J.Lo battled and
worked with AI in her most recent Netflix film. 

While we’re a far cry from Skynet, we’re closer than ever to a reality where AI
is a pervasive part of our everyday lives. You might have even used AI to help
you find a movie about AI. Or, maybe you asked Google or Alexa to “play a movie
about AI.”

Over the past decade, we’ve seen the world evolve from basic chatbots to a few
niche users of ChatGPT to a widespread use of artificial intelligence across
organizations in nearly every industry. Primary examples of AI usage today range
across nearly every industry. 

The retail industry leverages AI to deliver personalized ads and shopping
recommendations to customers based on browsing and purchase histories. In
healthcare, AI is leveraged to help analyze medical images and detect conditions
like cancer, heart disease, and more. In the automotive industry, AI is used to
plot travel routes and now, even drive the car for you with advances in
autonomous vehicles. The list of examples goes on — agriculture, manufacturing,
entertainment, utilities, etc. 

Within the financial industry, the vast majority of financial services companies
(91%) are either assessing AI or already using it in production, according to
NVIDIA’s annual State of AI in Financial Services report. 

Like other industries, financial services leaders are looking to AI to help them
deliver new operational efficiencies, reduce costs, automate repetitive tasks,
improve customer experiences, and drive new product and services innovations.
And, many financial providers are already seeing results across use cases like
fraud detection, customer service, and risk management. 

However, financial services organizations face a two-fold challenge when it
comes to leveraging business data and consumer-permissioned financial data. 

First, intelligence locked away is intelligence wasted. A recent commissioned
survey of 150 financial services industry leaders and decision makers in the
U.S. and Canada from Forrester Consulting states:

“...most financial institutions struggle considerably to utilize financial data
today. In part, this stems from an inability — or unwillingness — to evolve
their data programs. More than 60% of respondents say their organization largely
still uses data the same way they always have. This creates challenges with
delivering personalized experiences at scale (60% say this is an issue), wasting
data (52% say they have substantial amounts of data they are not using), and
using their consumer data to make better products, services, and experiences
(38% wrestle with this).”

What’s more, nearly half (44%) of financial services data leaders say they have
lost customers due to poor data usage. This wasted or misused data is a huge
opportunity for AI to help companies more effectively use the data at their
fingertips. But that brings us to the second challenge ...

Artificial intelligence relies on the data used to feed its system. If that data
is inaccurate, confusing, or outdated, financial providers are left making
decisions, running operations, or serving customers based on bad information. In
the case of AI, intelligence poorly managed is intelligence made dumb. 

Regardless of the use case, the biggest challenge — and opportunity — that the
majority of AI models and financial services companies face is being able to tap
into and leverage data in a way that delivers true value. 


AI RISKS: WHEN DATA IS DUMB, DEFICIENT, OR DECEITFUL


RISK 1: DATA QUALITY 

AI is only as good as the data that fuels it. Poor data quality, incomplete data
sets, and disjointed information can be costly for businesses. For instance, a
recent survey from Vanson Bourne and Fivetran found that models trained on
inaccurate, incomplete, and low-quality data caused misinformed business
decisions that impacted an organization's global annual revenue by 6%, or $406
million on average.

So how many companies are struggling with this premise of “bad data in, bad
decisions out?” The vast majority (77%) of organizations have data quality
issues, and 91% report this impacts their company’s performance, according to a
2022 survey of 500 data professionals from Great Expectations. And, a recent
report from Informatica points to data quality as the top issue preventing
companies from succeeding with generative AI initiatives. 


RISK 2: AI HALLUCINATIONS AND MISINFORMATION

Earlier this year, the World Economic Forum stated that “false and misleading
information supercharged with cutting-edge artificial intelligence … is the top
immediate risk to the global economy.” According to NewsGuard, an organization
that tracks misinformation, websites hosting AI-created false articles have
increased by more than 1,000% from May 2023 to December 2023. 

Whether AI is intentionally misled by malicious actors or simply making stuff up
to satisfy human prompts and questions, the risk is real. Primary examples of
misinformation and AI hallucinations (incorrect or misleading results that AI
models generate) include everything from Air Canada chatbot lies to made-up
court cases invented by ChatGPT for a U.S. District Court case. 

Bottom line? Verify what you read. In fact, when it comes to AI hallucinations,
popular AI models like ChatGPT and Google’s PaLM make up facts anywhere from 3%
to 27% of the time, depending on the tool. And, 89% of machine learning (ML)
engineers who work with generative AI say their models show signs of
hallucination. 


RISK 3: DATA-GENERATED BIAS 

While it’s clear AI can be easily misled by inaccurate information, it can also
be led astray by the same stereotypes and inherent biases that humans struggle
to combat. This can be due to the data used to train the AI model, the
algorithms, or cognitive bias from the creators of an AI model. Gartner
estimates that 85% of AI projects provide false results due to bias built into
the data or the algorithms, or that exist in the professionals managing those
deployments.

In addition, a team of researchers from the University of Southern California’s
Information Sciences Institute found bias in up to nearly 39% of data in two AI
databases. One often used example of bias is seen among AI-generated images —
with race and gender biased when AI was asked to create images of people in
specialized professions. 

For financial services, bias becomes a real risk — so much so that the Consumer
Financial Protection Bureau (CFPB) has expanded its definition of “unfair” acts
and practices to include discriminatory conduct, even by AI. Bias in AI use
cases for financial services could impact credit and loan decisions, financial
advice and investments, customer service and more. Last year, the Center for
Financial Inclusion identified harmful gender bias within financial advice for
women versus men within popular AI tools. 


RISK 4: DATA PRIVACY AND SECURITY

Privacy and security are already top concerns related to data — the use of AI
paints an even bigger spotlight on the need for strong data privacy and security
protocols and tools. The risks are two-fold: 

 * Protecting against AI-powered cyberattacks, threats, and fraud
 * Educating on proper AI usage and establishing policies to protect against
   data breaches and leaks

When it comes to protecting against AI attacks, a survey of 1,800 security
leaders and practitioners from Darktrace found 60% believe their organizations
are inadequately prepared to defend against AI-powered threats and attacks. For
financial services, the American Banker Journal reports just over half of
financial institutions say they lost between $5 million and $25 million to
AI-based threats in 2023. 

The other primary risk is related to how companies and its employees leverage
AI. According to a 2022 Gartner report, 40% of organizations experienced an
AI-related privacy breach. Of those, only 1 in 4 was considered malicious. This
means the majority of those breaches were likely caused by irresponsible
internal practices. For example, Cisco’s 2024 Data Privacy Benchmark study found
48% of organizations are entering non-public information about their company
into GenAI apps. Another 38% say they’ve put customer information into them as
well! 

Beyond putting restrictions and policies in place, companies — particularly
those with access to personal information like a consumer’s finances — have work
to do to educate employees and establish the right protocols to protect against
inadvertent harm. 


RISK 5: MISTRUST AND REPUTATION RISK 

Could using AI hurt your reputation and damage customer trust? Maybe… but it
depends on the company and situation. At a high level, a recent Harvard Business
Review study found most people (57%) don’t trust AI. Additionally, a survey from
KPMG and the University of Queensland found that 61% are wary about trusting AI
systems.

While the majority don’t trust AI today, consumers are starting to trust AI to
take the place of human interactions in certain cases. For example, PWC found
that half of consumers said they would trust GenAI to collate product
information before they make a purchase (55%) or provide product recommendations
(50%). However, there is still room to grow in gaining trust with AI for more
complex topics — only 23% trust GenAI to assist them with legal advice, and just
27% trust it to execute financial transactions. 

As trust in AI grows, financial services providers have opportunities to
leverage AI responsibly to augment activities like customer service, financial
advice, and automated insights or notifications. 

However, this means protecting against the other 4 risks outlined above to
ensure any AI missteps or mishaps don’t damage consumer trust or have a negative
impact on the company’s reputation. According to the 2024 Edelman Trust
Barometer, when innovations like AI are mismanaged, trust and enthusiasm for
these technologies wanes. When AI is managed well, Edelman found that 38% of
consumers would embrace it. On the flip side, when it is managed poorly, this
drops by more than 10 points — just 26% of global respondents would embrace AI. 


AI REWARDS: WHEN DATA IS INTELLIGENT, INTEGRATED, AND INCLUSIVE


REWARD 1: INTELLIGENCE AMPLIFIED

Financial services providers and most businesses face an increasingly difficult
challenge: how to capture, analyze, and take action on a growing mountain of
data. A single financial transaction generates dozens of data points. All of
these data points across the millions of transactions occurring each day can
provide valuable insights into consumer behaviors, product adoption, and
engagement. However, more than half (52%) admit they have substantial amounts of
consumer financial data they are not using, according to a commissioned survey
from Forrester Consulting. 

Artificial intelligence can augment and enhance current data analysis processes
and tools to enable smarter, faster insights based on large sets of data. It can
also help uncover hidden patterns and previously unrealized opportunities that
can drive significant value for organizations. McKinsey & Company calls out that
AI’s potential to create value for financial services organizations is one of
the largest across industries, potentially unlocking $1 trillion of incremental
value for banks annually.

By automating data analysis with AI tools, what would previously take an
individual hours to do can be reduced to minutes or even seconds. In fact, IBM’s
2022 Global AI Adoption Index found 65% of businesses are using AI to reduce
manual or repetitive tasks. And, this takes us to our next key benefit. 


REWARD 2: OPERATIONAL EFFICIENCIES

According to IBM, 54% of organizations are seeing cost savings and efficiencies
from using AI in IT, business, or network processes. For the financial industry,
McKinsey’s 2023 banking report estimates generative AI could enhance
productivity in the banking sector by up to 5% and reduce global expenditures by
up to $300 billion.

These operational efficiencies can take the form of everything from automating
repetitive tasks and reducing manual errors to filling labor and skills
shortages and enabling chatbots to handle less complex customer service
inquiries. With the productivity gains possible with AI, financial providers can
speed up innovation and transform how they create and maintain sustainable
growth. 


REWARD 3: FINANCIAL INCLUSION

While the risk of AI-generated bias in financial advice is real, AI can also
break down barriers to financial access for unbanked and underbanked communities
in a number of ways: 

 * Democratizing access to financial products and advice 
 * Empowering better money management with personalized insights and tools
 * Expanding credit access to those with little or no credit history

> “Technology drives inclusion in financial services. It facilitates widespread
> education, accessibility, and affordability that were previously reserved for
> a wealthy few, opening doors and breaking down barriers worldwide.” — World
> Economic Forum

Democratized Access: Not only can operational efficiencies gained from using AI
help financial providers bring down the cost of delivering financial services,
it also opens up new ways of delivering products and services to consumers that
can meet them where they are. For instance, AI can make it easier for consumers
with disabilities or language barriers to gain access to financial tools and
advice. 

Better Money Management: AI-powered insights embedded into the digital and
mobile banking apps consumers already use can help them better understand and
manage their money. In addition, AI-powered chatbots and virtual financial
advisors can provide a free option for consumers who want advice on how to
create a budget, manage an investment portfolio, and more. And, consumers are
interested in using AI tools to help them manage their money. A Forbes Advisor
survey found more than 40% of consumers would use artificial intelligence to get
financial advice. 

Expanded Credit Access: The U.S. Chamber of Commerce says that “more inclusive
financial services cannot happen if institutions are not equipped to handle
increased applications and subsequent decisions. One way to efficiently work
with this additional data is to utilize artificial intelligence and machine
learning.” For instance, PWC reports that lenders using advanced credit modeling
techniques are “already seeing 15% to 30% increases in credit approvals with no
change in loss rates. On the financial inclusion side, these approvals often
include once overlooked borrowers who can now access credit from the formal
banking system.”


REWARD 4: FRAUD DETECTION

A survey from Mastercard and Fintech Nexus reveals that “increased fraud
detection” ranks as the primary driver (63%) behind AI investment among
financial institutions. AI can improve fraud detection and prevention by
analyzing patterns and anomalies in transaction data more quickly and
efficiently. In fact, a study by IBM found that AI-powered fraud detection
systems will reduce false positives by up to 70%, resulting in significant cost
savings and improved customer experiences. 

And, organizations are already seeing the results. J.P. Morgan has been using
the underlying AI-powered large language models for payment validation screening
for more than two years. It also speeds up processing in other ways by reducing
false positives and enabling better queue management. The result has been lower
levels of fraud and a better customer experience, with account validation
rejection rates cut by 15% to 20%. 


REWARD 5: PERSONALIZATION AT SCALE

Eighty percent of consumers say customer experiences should be better
considering all the data companies collect, according to Salesforce’s State of
the Connected Customer report. However, it’s difficult to truly understand
customers and deliver personalization at scale when most financial providers
only see a small picture of a consumer’s financial life. 

Consumers have money in more places than ever before, averaging at least 5 to 7
different financial accounts, and as a result, often don’t have a holistic view
of their financial lives. AI can help financial providers deliver personalized
experiences at scale by helping to automate and personalize support, as well as
translate financial data points into actionable intelligence. 

A Capgemini report on GenAI found that 67% of executives said that generative AI
can improve customer service through automated and personalized support. In
addition, Salesforce’s research found 68% of service professionals use AI to
create and personalize service communications with customers.


CONCLUSION: IT’S ALL ABOUT THE DATA

Across all risks and rewards that AI can bring to financial services, it starts
with being able to utilize consumer-permissioned financial data and business
intelligence to mitigate the risks and maximize the opportunities. As financial
services companies embrace AI for a multitude of use cases, they must also
become a truly data-driven organization. 

> Consumer financial data programs are vital to continued success, but all too
> often, organizations are limited by their inability to turn data into
> actionable intelligence.” —Opportunity And Growth In Financial Services:
> Turning Data Insights into Actionable Intelligence




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