THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
13
https://www.theamericanjournals.com/index.php/tajet
PUBLISHED DATE: - 02-12-2024
https://doi.org/10.37547/tajet/Volume06Issue12-03
PAGE NO.: - 13-23
USING GENAI TO TRANSFORM DIGITAL
PRODUCT DEVELOPMENT AND DIGITAL
SALES IN THE BANKING SECTOR
Soloveva Varvara
Engagement Manager, McKinsey & Company, San Francisco, California, USA
INTRODUCTION
Generative Artificial Intelligence (GenAI) is
fundamentally transforming the operations of the
banking
industry,
offering
unprecedented
opportunities to redefine traditional approaches to
product development, customer interactions, and
the delivery of financial services. According to the
McKinsey Global Institute, the banking sector
stands out among various industries with one of
the largest opportunities to generate additional
value from GenAI, estimated at $200 billion to
$340 billion annually
—
driven primarily by
application in software engineering (digital
product development), retail banking and
corporate banking [1, 2]. Consequently, there is a
growing academic interest in analyzing key areas
where GenAI technology is applied in this sector
and examining its potential for the industry's
transformation.
RESEARCH ARTICLE
Open Access
Abstract
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
14
https://www.theamericanjournals.com/index.php/tajet
Figure 1. Value created by AI at stake by segment and function in the banking industry [2]
Companies, including banks, have already begun
capturing value from generative AI, reflecting its
transformative potential across industries. Recent
empirical analysis indicates a significant increase
in GenAI adoption, with 65% of surveyed
organizations now reporting regular use
—
double
the rate observed in the previous year [3]. This
trend is particularly pronounced in the financial
sector, where banks are actively exploring and
experimenting with generative AI, underscoring its
growing appeal and adaptability to a wide range of
business processes.
Despite this progress, banks continue to face
challenges in meeting the evolving expectations of
their customers. The research problem lies in the
fact that existing methods of digital banking
services and sales do not fully address modern
demands for personalization, speed, and service
quality. As the banking sector undergoes a rapid
digital transformation, the development of digital
products has become a critical focus area.
However, because digital product development is
not traditionally a core competency for most
banks, their methodologies are often outdated and
lack agility. Digital tools, such as chatbots for
customer interaction, often require lengthy
development cycles before being introduced to the
market and frequently demonstrate limited
efficiency in practice. Moreover, traditional sales
and service channels, such as bank branches and
call centers, require significant operational costs
and do not provide 24/7 service availability. This
research aims to explore the potential of
generative artificial intelligence to overcome these
challenges and to transform processes related to
digital product development and digital sales, as
well as propose an innovative framework for
identifying and prioritizing key applications of
GenAI within these areas.
Current Scholarly Perspective
The preparation of this article involved
comparative analysis, systematization, synthesis,
and generalization, with a focus on reviewing
recent scholarly works on the topic.
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
15
https://www.theamericanjournals.com/index.php/tajet
In the literature, various aspects of applying
generative artificial intelligence in the banking
sector are actively explored. For instance, J. Bellens
and T. Mogi [4] highlight key priorities for the
successful implementation of GenAI in this sector,
emphasizing the need for a systematic approach to
transforming business processes. Expanding on
this idea, U. Noreen and co-authors [9] present the
concept of Banking 4.0, where AI serves as a
central element in the technological evolution of
commercial banking services.
Of particular interest are studies on user
acceptance of new AI developments. S.S. Bharti and
colleagues [5] utilize the PLS-SEM method to
analyze factors influencing customer perceptions
of AI technologies in digital banking. Ja.N. Sheth
and co-authors [12] focus on the potential for AI-
driven service personalization in emerging
markets, proposing a comprehensive model for
experience assessment.
In more specialized areas, Ch. Dietzmann and
colleagues [6] investigate the potential of robotic
advisors, while S. Dimitrieska [7] examines the
possibilities of generative AI in banking
advertising and marketing communications.
Regional aspects of GenAI integration are analyzed
by T. Maheswari and colleagues [8] through case
studies, and by M. Sharma [10], who explores the
specifics of AI adoption in the banking sector in the
Middle East.
Contemporary research publications reveal
differing perspectives on the optimal pace of GenAI
technology integration: some researchers [4, 11,
13] advocate for rapid, large-scale transformation,
whereas others [5, 12] emphasize the importance
of a gradual transition in line with customer
readiness.
Several aspects remain insufficiently covered, such
as methodologies for evaluating the effectiveness
of GenAI solutions in banking, issues of ethics and
transparency in AI-driven decision-making, the
long-term socioeconomic impacts of widespread
AI adoption in banking, and challenges related to
data security and privacy in implementing these
innovations. Further, detailed research is
necessary to fill these gaps and provide a
comprehensive understanding of GenAI’s role in
transforming the banking sector.
Theoretical foundation of GenAI integration
into business workflows
The integration of Generative Artificial Intelligence
(GenAI) into business and technological workflows
is grounded in the principles of foundation models
and transformer architectures. GenAI is
characterized by its ability to create unstructured
content, such as text, images, and videos, through
models trained on extensive datasets across
various domains. This versatility enables it to be
adapted to diverse tasks, transcending the
limitations of traditional AI systems that were
predominantly designed for structured data
interpretation and limited applications. (Fig. 2)
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
16
https://www.theamericanjournals.com/index.php/tajet
Figure 2. Elements of the GenAI conceptual framework [4, 7, 13, 14, 17]
Foundation models, which form the core of GenAI
systems, utilize vast datasets encompassing
internet crawls, literary repositories, and domain-
specific corpora. For example, the training dataset
for GPT-3 consisted of 45 terabytes of data,
including over 250,000 books, Wikipedia, and Web
content. The underlying architecture leverages
transformer models with billions of parameters
(e.g., GPT-4 with 175 billion parameters), trained
using attention mechanisms to predict sequential
data patterns [18]. This training process,
equivalent to decades of computation on singular
GPU systems, results in a generalized model
capable of diverse text-generation tasks, such as
writing articles, code generation, and emotional
sentiment analysis.
E
lem
en
ts
Deep learning models (neural networks, transformers)
Natural Language Processing
Data generation (texts, images, code, etc.)
Training on big data
Self-learning, adaptation based on feedback
Pattern recognition, data structuring
Creating predictive models
Creativity, empathy in user interaction
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
17
https://www.theamericanjournals.com/index.php/tajet
What distinguishes GenAI from traditional AI
paradigms is its capacity to generate novel outputs
while simultaneously excelling at interpreting
unstructured data. By enabling better data labeling
and context understanding, GenAI facilitates more
efficient workflows. Furthermore, its integration
within industry-specific processes allows for
reimagined end-to-end operations, moving beyond
fragmented point solutions to holistic automation.
By integrating these capabilities, GenAI not only
automates isolated processes but also drives
systemic efficiency and innovation across
industries. Its theoretical foundation, rooted in
foundational modeling and advanced data training
methodologies, provides the basis for its expansive
adaptability and transformative potential.
GenAI impact on Digital Product development
and Digital Sales within the banking sector
Generative AI has the potential to revolutionize the
entire digital product development lifecycle
(PDLC) within the banking sector, addressing
critical challenges in efficiency, agility, and
customer-centric innovation. By integrating
generative AI across all stages of the PDLC
(ideation, development, deployment), banks can
overcome
the
limitations
of
traditional
development methods, accelerate timelines, and
create superior digital solutions tailored to
evolving customer needs [2]. As banks increasingly
digitize their operations, generative AI offers a
transformative pathway to modernize their
approaches and drive value creation in a highly
competitive and regulated industry.
In the ideation phase, generative AI can enhance
the breadth and depth of idea generation by
analyzing customer behavior, market trends, and
operational data to identify unmet needs and
emerging opportunities. For example, banks can
leverage AI to design personalized financial
products, such as dynamic savings plans or
tailored lending solutions, by converting raw data
into actionable user stories. Furthermore,
generative AI can streamline the creation of
requirements, translating abstract business goals
into detailed functional specifications. This
accelerates the validation of ideas and ensures
alignment with customer expectations while
reducing the time required to move from concept
to execution.
In the development phase, generative AI
transforms how banks approach coding, testing,
and system design. AI can assist in the automated
generation of secure and context-aware code
based on predefined requirements, ensuring
compliance with regulatory standards and
industry best practices. Additionally, generative AI
can optimize testing processes by identifying
potential vulnerabilities, generating test cases, and
adapting to iterative changes in requirements. This
capability is especially valuable in the banking
sector, where robust testing is critical to
maintaining the reliability and security of systems
that handle sensitive financial transactions.
Generative AI also enables more adaptive and
efficient system architecture, automatically
adjusting to new business needs and regulatory
changes, further enhancing development agility.
The application of GenAI is particularly
transformative in the domain of digital sales [4],
where effective communication and tailored
recommendations are critical. This technology
offers a transformative approach to customer
engagement,
enabling
high
levels
of
personalization and interaction that surpass the
limitations of traditional methods. Particularly,
GenAI allows banks to transition from segmented
approaches
to
individualized,
real-time
interactions at scale.
The evolution of personalization in banking can be
conceptualized as a progression through several
stages. Initially, banks lacked any form of
personalization,
delivering
uniform
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
18
https://www.theamericanjournals.com/index.php/tajet
communications to all customers irrespective of
their unique needs or preferences. Over time,
simple personalization emerged, incorporating
basic data elements such as customer names or
account details into messages. This approach
subsequently evolved into segment-driven
personalization, wherein customers were grouped
based on broad demographic or behavioral
characteristics, and communications were tailored
accordingly. While these approaches provided
incremental improvements, they were inherently
limited in their ability to address the nuanced
preferences of individual customers.
GenAI marks a significant advancement in
personalization by enabling hyper-personalized,
one-to-one interactions at scale. Unlike traditional
methods that rely on static templates, GenAI
dynamically generates bespoke communications,
adapting content, tone, and delivery channels to
align with the specific preferences and behaviors
of each customer. For example, generative AI can
analyze customer financial histories and
behavioral patterns to craft tailored product
recommendations, such as personalized credit
card offers or investment advice [6]. This
capability moves beyond mass segmentation,
delivering communications that resonate with
individual customers on a granular level.
In addition to personalized communication, GenAI
impacts the area of after-sales service, where its
capabilities significantly outperform traditional
call centers. GeaAI-powered virtual assistant can
instantly process requests without creating
queues and operate around the clock, maintaining
consistently high-quality service (regardless of
time, system load, or other factors). Additionally, a
single virtual assistant can simultaneously serve
thousands of clients, offering each one
personalized attention and maintaining the
dialogue context. GenAI bots manage a wide range
of tasks (Fig. 3). By analyzing customer data in
real-time, these systems can identify relevant
cross-selling opportunities, offering personalized
product or service recommendations that align
with customer needs and behaviors. Such
advancements not only strengthen customer
satisfaction and loyalty by ensuring prompt and
tailored interactions at every touchpoint but also
create new avenues for revenue generation
through targeted cross-sell initiatives.
Figure 3. Multitasking of GenAI bots [10, 12, 15, 16]
Informing about account status, transactions
Assistance in setting up banking services
Dispute resolution
Advice on investment strategies
Assistance in processing insurance claims
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
19
https://www.theamericanjournals.com/index.php/tajet
At its most sophisticated stage, GenAI enables end-
to-end personalized conversational experiences
across multiple digital channels. Customers can
initiate and seamlessly continue interactions
through mediums such as chat, email, or voice,
with GenAI responding in real time to provide
contextual, human-like engagements. For instance,
a customer inquiring about recent transactions
might receive a detailed response accompanied by
tailored suggestions for relevant financial
products or services, such as savings plans or
investment options. This transition from static,
transactional exchanges to dynamic, responsive
conversations
fosters
deeper
customer
engagement and builds trust.
Impact assessment framework and strategic
implications for banks
Table 1 provides a comprehensive framework and
original
perspective
for
analyzing
the
transformative impact of GenAI across critical
stages of digital product development and digital
sales within the banking sector. Developed
through original analysis and enriched by review
of existing scientific publications [1, 4, 6, 7, 11], it
systematically categorizes key applications of
GenAI alongside their respective impacts, offering
a structured and data-driven perspective on how
this
technology
can
address
prevailing
inefficiencies, enhance customer engagement, and
foster innovation.
The framework delineates specific use cases across
ideation, development, deployment, sales, and
customer service, illustrating how GenAI can
optimize
processes
such
as
automated
requirement
drafting,
hyper-personalized
customer interactions, and real-time operational
monitoring. Furthermore, it identifies the tangible
outcomes of these applications, including
enhanced product quality, increased customer
satisfaction, operational efficiency, and revenue
growth through targeted cross-selling. This
framework not only underscores the strategic
potential of GenAI in banking but also provides a
foundation
for
future
research
and
implementation strategies aimed at leveraging its
capabilities to meet evolving industry demands.
Table 1. Framework for defining core applications and impact across critical stages of digital
product development and digital sales.
Domain
Category
GenAI Core Applications
Impact
Digital
Product
Develop
ment
Ideation and
Requirements
Analyze customer and market data to
identify unmet needs.
Faster product innovation.
Draft detailed user stories and
requirements automatically.
Improved alignment with
customer needs.
Validate ideas faster through predictive
customer scenarios.
Reduced time to validate
product ideas.
Development
and Testing
Generate secure, compliant, and context-
aware code.
Enhanced product quality.
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
20
https://www.theamericanjournals.com/index.php/tajet
Automate test creation and vulnerability
detection.
Reduced development time
and cost.
Optimize system architecture
dynamically for evolving needs.
Improved agility in adapting
to requirements.
Deployment
and
Monitoring
Predict and resolve operational issues
proactively.
Improved system reliability.
Analyze feedback to introduce updates
and optimizations.
Increased operational
efficiency.
Streamline deployment processes with
real-time insights.
Faster and more efficient
product deployment.
Digital
Sales
Hyper-
Personalization
in Sales
Generate dynamic, individualized
communications.
Higher engagement and sales
conversion rates.
Provide real-time, contextual
interactions across channels.
Improved customer
satisfaction and loyalty.
Recommend products using behavioral
and financial data to enhance cross-
selling.
Increased revenue through
targeted cross-selling.
After-Sales
Service
Deliver scalable, 24/7 virtual assistance
with personalized responses.
Enhanced customer support
experience.
Maintain consistent service quality
regardless of demand.
Increased retention and
customer trust.
Identify cross-selling opportunities
during customer interactions.
Boosted revenue through
personalized
recommendations.
GenAI offers banks a transformative opportunity
to modernize their product development
processes, addressing longstanding inefficiencies
and aligning with the demands of the digital era.
Historically, banks have relied on rigid, resource-
intensive development methodologies that need
help to keep pace with rapidly changing customer
expectations. GenAI introduces flexibility and
efficiency into the PDLC, enabling banks to
significantly accelerate the development of digital
products and time to market.
The application of GenAI in digital sales and
customer engagement carries profound strategic
implications for the banking sector. By delivering
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
21
https://www.theamericanjournals.com/index.php/tajet
hyper-personalized, real-time interactions, GenAI
not only enhances customer satisfaction but also
drives
higher
engagement
and
loyalty.
Furthermore, its ability to combine digital sales
and after-sales services into a seamless, AI-driven
continuum positions banks as trusted financial
partners rather than mere transactional service
providers. This shift not only improves the overall
customer experience but also enables banks to
capitalize on more effective cross-selling and up-
selling opportunities within their digital
ecosystems.
The potential applications of GenAI in the banking
sector remain vast. While one of the largest
strategic impacts of GenAI in banking is anticipated
in digital product development and digital sales, its
broader implications extend to key areas such as
operations, compliance, and talent management.
By automating complex workflows, enhancing
fraud detection and risk monitoring, and
streamlining workforce optimization, GenAI
equips banks to achieve greater operational
efficiency, strengthen regulatory compliance, and
improve organizational agility. These applications
not only address current challenges but also
position banks to innovate and adapt in a rapidly
evolving
market,
ensuring
sustained
competitiveness
and
long-term
strategic
resilience.
CONCLUSIONS
GenAI represents a transformative opportunity for
the
banking
sector,
addressing
critical
inefficiencies and enabling innovation in a highly
competitive and regulated industry. By leveraging
GenAI, banks can enhance agility, streamline
operations, and deliver superior customer
experiences. Its applications span multiple
domains, with some of the most impactful ones
being digital product development and digital
sales.
In the domain of digital product development,
GenAI revolutionizes the product development
lifecycle (PDLC) by enabling faster, more efficient
processes at every stage, from ideation to
deployment. During prototype creation and
product backlog planning, the tools optimize ideas
and prioritize tasks, focusing on the key features
most important to clients. In the development
phase, GenAI reduces the workload on developers,
accelerating coding and testing, which helps
shorten the time to market. Ultimately, this results
in a smoother and more successful product launch,
backed by valuable data-driven recommendations.
In
digital
sales,
GenAI
drives
hyper-
personalization,
transforming
customer
engagement and communication strategies. By
transitioning from segmented messaging to
individualized, real-time interactions, GenAI
fosters deeper customer connections and
enhances satisfaction. Its ability to analyze
customer behavior and financial patterns allows
for tailored product recommendations, increasing
both engagement and sales conversion rates.
Additionally, GenAI's role in after-sales service
ensures 24/7 availability, personalized assistance,
and
cross-selling
opportunities,
further
strengthening customer loyalty and driving
revenue growth.
In summary, it is crucial to emphasize that GenAI
enables banks to make the product development
and digital sales processes more targeted, agile,
and data-driven, thereby strengthening their
competitive position in a rapidly changing
environment. Achieving its full potential requires
both technological readiness and a fundamental
transformation of business processes within
banks, enabling a new standard of service
excellence.
REFERENCES
1.
The economic potential of generative AI //
URL:
https://www.mckinsey.com/~/media/mckin
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
22
https://www.theamericanjournals.com/index.php/tajet
sey/business%20functions/mckinsey%20digi
tal/our%20insights/the%20economic%20pot
ential%20of%20generative%20ai%20the%2
0next%20productivity%20frontier/the-
economic-potential-of-generative-ai-the-next-
productivity-frontier.pdf
(accessed
11/05/2024).
2.
Capturing the full value of generative AI in
banking
//
URL:
in
https://www.mckinsey.com/industries/finan
cial-services/our-insights/capturing-the-full-
value-of-generative-ai-in-banking (accessed
11/05/2024).
3.
The state of AI in early 2024: Gen AI adoption
spikes and starts to generate value. URL:
https://www.mckinsey.com/capabilities/qua
ntumblack/our-insights/the-state-of-ai
(accessed 11/05/2024).
4.
Bellens J. Five priorities for harnessing the
power of GenAI in banking / J. Bellens, T. Mogi
//
URL:
https://www.ey.com/en_us/insights/banking
-capital-markets/five-priorities-for-
harnessing-the-power-of-gen-ai-in-banking
(accessed 11/05/2024).
5.
Bharti S.S. Customer acceptability towards AI-
enabled digital banking: a PLS-SEM approach /
S.S. Bharti, K. Prasad, Sh. Sudha, V. Kumari //
Journal of Financial Services Marketing.
–
2023.
–
Vol. 28.
–
No. 4.
–
Pp. 779-793.
6.
Dietzmann Ch. Implications of AI-based robo-
advisory for private banking investment
advisory // Ch. Dietzmann, T. Jaeggi, R. Alt //
Journal of Electronic Business & Digital
Economics.
–
2023.
–
Vol. 2.
–
No. 1.
–
Pp. 3-23.
7.
Dimitrieska S. Generative artificial intelligence
and advertising / S. Dimitrieska // Trends in
Economics, Finance and Management Journal.
–
2024.
–
Vol. 6.
–
No. 1.
–
Pp. 23-34.
8.
Maheswari T. A implementation of ai
technology in banking sector based on
Coimbatore city / T. Maheswari, E. Karthika,
K.R. Anusrii // ComFin Research.
–
2023.
–
Vol.
11.
–
No. 1.
–
Pp. 32-38.
9.
Noreen U. Banking 4.0: artificial intelligence
(ai) in banking industry & consumer’s
perspective / U. Noreen, A. Shafique, Z. Ahmed,
M. Ashfaq // Sustainability.
–
2023.
–
Vol. 15.
–
No. 4.
–
Pp. 36-82.
10.
Sharma M. A study: how AI is incorporated in
the Middle East banking / M. Sharma // Journal
for Research in Applied Sciences and
Biotechnology.
–
2023.
–
Vol. 2.
–
No. 3.
–
Pp.
202-208.
11.
Shaw A. AI based transformation on real
banking system 4.0 / A. Shaw, A. Sen, A. Das, Ch.
Roy, D. Bhattacharjee, A. Das // American
Journal of Business and Management Research.
–
2023.
–
Vol. 4.
–
No. 3.
–
Pp. 1-10.
12.
Sheth Ja.N. AI-driven banking services: the next
frontier for a personalised experience in the
emerging market / Ja.N. Sheth, V. Jain, G. Roy,
A. Chakraborty // The International Journal of
Bank Marketing.
–
2022.
–
Vol. 40.
–
No. 6.
–
Pp.
1248-1271.
13.
Shkurdoda A. GenAI in Banking: 5
Transformative Use Cases / A. Shkurdoda, M.
Dobosz
//
URL:
https://neontri.com/blog/genai-applications-
banking/ (accessed 11/05/2024).
14.
Liu Yu. Demystifying GenAI for creative
workers: a mixed-method investigation of
firsthand experience / Yu. Liu, S. Chen, X. Xie //
Academy of Management Proceedings.
–
2024.
–
No. 1.
15.
Cano-Marin E. Transformative potential of
generative artificial intelligence (GenAI) in
business / E. Cano-Marin // ESIC Market.
–
THE USA JOURNALS
THE AMERICAN JOURNAL OF ENGINEERING AND TECHNOLOGY (ISSN
–
2689-0984)
VOLUME 06 ISSUE12
23
https://www.theamericanjournals.com/index.php/tajet
2024.
–
Vol. 55.
–
No. 2.
–
P. 333.
16.
McFee I. Feature article: how GenAI will change
the world economy / I. McFee // Economic
Outlook.
–
2024.
–
Vol. 48.
–
No. 3.
–
Pp. 39-46.
17.
Kido T. The challenges for GenAI in social and
individual well-being / T. Kido, K. Takadama //
Proceedings of the AAAI Symposium Series.
–
2024.
–
Vol. 3.
–
No. 1.
–
Pp. 365-367.
18.
Thompson A. What is my AI. A Comprehensive
Analysis of Datasets Used to Train GPT-1, GPT-
2, GPT-3, GPT-NeoX-20B, Megatron-11B, MT-
NLG,
and
Gopher
//
URL:
https://s10251.pcdn.co/pdf/2022-Alan-D-
Thompson-Whats-in-my-AI-Rev-0b.pdf
(accessed 11/05/2024).
