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NEARLY 80 PERCENT OF FINANCIAL FIRMS USE AI TO IMPROVE SERVICES, REDUCE FRAUD

NVIDIA’s “State of AI in Financial Services” survey details the growth of AI
adoption in capital markets, retail banking and investment firms.
January 27, 2022 by Kevin Levitt

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From the largest firms trading on Wall Street to banks providing customers with
fraud protection to fintechs recommending best-fit products to consumers, AI is
driving innovation across the financial services industry.

New research from NVIDIA found that 78 percent of financial services
professionals state that their company uses accelerated computing to deliver
AI-enabled applications through machine learning, deep learning or high
performance computing.

The survey results, detailed in NVIDIA’s “State of AI in Financial Services”
report, are based on responses from over 500 C-suite executives, developers,
data scientists, engineers and IT teams working in financial services.


AI PREVENTS FRAUD, BOOSTS INVESTMENTS

With more than 70 billion real-time payment transactions processed globally in
2020, financial institutions need robust systems to prevent fraud and reduce
costs. Accordingly, fraud detection involving payments and transactions was the
top AI use case across all respondents at 31 percent, followed by conversational
AI at 28 percent and algorithmic trading at 27 percent.

There was a dramatic increase in the percentage of financial institutions
investing in AI use cases year-over-year. AI for underwriting increased
fourfold, from 3 percent penetration in 2021 to 12 percent this year.
Conversational AI jumped from 8 to 28 percent year-over-year, a 3.5x rise.

Meanwhile, AI-enabled applications for fraud detection, know your customer (KYC)
and anti-money laundering (AML) all experienced growth of at least 300 percent
in the latest survey. Nine of 13 use cases are now utilized by over 15 percent
of financial services firms, whereas none of the use cases exceeded that
penetration mark in last year’s report.

Future investment plans remain steady for top AI cases, with enterprise
investment priorities for the next six to 12 months marked in green.

Top Current AI Use Cases in Financial Services (Ranked by Industry Sector)

Green highlighted text signifies top AI use cases for investment in next six to
12 months.


OVERCOMING AI CHALLENGES

Financial services professionals highlighted the main benefits of AI in yielding
more accurate models, creating a competitive advantage and improving customer
experience. Overall, 47 percent said that AI enables more accurate models for
applications such as fraud detection, risk calculation and product
recommendations.

However, there are challenges in achieving a company’s AI goals. Only 16 percent
of survey respondents agreed that their company is spending the right amount of
money on AI, and 37 percent believed “lack of budget” is the primary challenge
in achieving their AI goals. Additional obstacles included too few data
scientists, lack of data, and explainability, with a third of respondents
listing each option.

Financial institutions such as Munich Re, Scotiabank and Wells Fargo have
developed explainable AI models to explain lending decisions and construct
diversified portfolios.

Biggest Challenges in Achieving Your Company’s AI Goals (by Role)

Cybersecurity, data sovereignty, data gravity and the option to deploy on-prem,
in the cloud or using hybrid cloud are areas of focus for financial services
companies as they consider where to host their AI infrastructure. These
preferences are extrapolated from responses to where companies are running most
of their AI projects, with over three-quarters of the market operating on either
on-prem or hybrid instances.

Where Financial Services Companies Run Their AI Workloads


EXECUTIVES BELIEVE AI IS KEY TO BUSINESS SUCCESS

Over half of C-suite respondents agreed that AI is important to their company’s
future success. The top total responses to the question “How does your company
plan to invest in AI technologies in the future?” were:

 1. Hiring more AI experts (43 percent)
 2. Identifying additional AI use cases (36 percent)
 3. Engaging third-party partners to accelerate AI adoption (36 percent)
 4. Spending more on infrastructure (36 percent)
 5. Providing AI training to staff (32 percent)

However, only 23 percent overall of those surveyed believed their company has
the capability and knowledge to move an AI project from research to production.
This indicates the need for an end-to-end platform to develop, deploy and manage
AI in enterprise applications.

Read the full “State of AI in Financial Services 2022” report to learn more.

Explore NVIDIA’s AI solutions and enterprise-level AI platforms driving the
future of financial services.

Categories: Deep Learning
Tags: Artificial Intelligence | Conversational AI | Cybersecurity | Financial
Services



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