The American Journal of Management and Economics Innovations
74
https://www.theamericanjournals.com/index.php/tajmei
TYPE
Original Research
PAGE NO.
74-79
10.37547/tajmei/Volume07Issue04-09
OPEN ACCESS
SUBMITED
24 February 2025
ACCEPTED
28 March 2025
PUBLISHED
25 April 2025
VOLUME
Vol.07 Issue04 2025
CITATION
Pavlov Artem. (2025). Utilization of Geolocation Data for Personalizing
Financial Services. The American Journal of Management and
Economics Innovations, 7(04), 74
–
79.
https://doi.org/10.37547/tajmei/Volume07Issue04-09
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Utilization of Geolocation
Data for Personalizing
Financial Services
Pavlov Artem
Authorized Member (AMBR) at Global Expert of Development LLC
Hollywood, Florida, USA
Abstract:
This article examines the essence and specific
features of applying geolocation data for personalizing
financial services. In the context of the rapid
digitalization of this sector (as well as others), the use
of such data is becoming strategically significant both
today and in the future. Banks and financial institutions
are actively integrating spatial analytics into their
services to enhance service quality and personalize
offerings. However, along with the beneficial effects,
several critical challenges and contradictions arise,
including the interpretation of geodata, privacy
concerns, and the efficiency of processing algorithms.
The objective of this study is to analyze the ways
geolocation data is utilized to personalize financial
services, identify key advantages, highlight problematic
areas, and outline future directions for its application.
The article reviews the primary technological
approaches and emphasizes that despite the evident
benefits, large-scale implementation of geolocation
technologies requires careful consideration of personal
data protection and transparency of algorithms. The
author’s contribution lies in the comprehensive
examination of the issue, presenting a classification of
geolocation applications and identifying gaps in the
academic literature. The findings will be valuable for
financial institutions, fintech solution developers,
digital marketing specialists, and analysts working with
spatial data.
Keywords:
Banking technologies, geolocation data,
geomarketing, personal data protection, machine
learning, financial service personalization, fraud
prevention, digital transformation.
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Introduction:
Modern financial institutions actively employ digital
technologies to enhance customer service quality. To
attract more clients, marketers in the financial services
sector optimize SEO and digital advertising, while
personalizing customer interactions to increase call
conversion rates.
One of the most promising directions in this field is the
application of geolocation data for personalizing
financial services. However, integrating spatial data
into
relevant
algorithms
presents
several
methodological and ethical challenges. Key concerns
include the accuracy of location interpretation, data
privacy compliance, and the effectiveness of machine
learning models utilizing this information.
According to statistical data, 90% of loan consumers
(including mortgage borrowers), 85% of individuals
cashing checks, and 76% of respondents filing tax
returns begin their journey with an online search [10].
For many, this step serves as the initial phase in
evaluating
available
options,
highlighting
the
significance of mobile queries and geolocation in
customer acquisition.
Given these considerations, contemporary researchers
focus on examining the key aspects of geolocation data
utilization in the financial sector, analyzing its impact on
service personalization, and addressing potential risks
associated with its implementation.
MATERIALS AND METHODS
An analysis of scientific publications and industry
reports on the subject reveals that researchers
approach the problem from various perspectives,
including the adaptation of financial institutions to the
digital environment, the use of geomarketing, the
assessment of service accessibility, and the integration
of geolocation technologies into marketing strategies.
In the study by M. Fundira, E.I. Edoun, and A. Pradhan
[3], existing models of digital transformation in the
financial sector are analyzed, evaluating their potential
for personalization. The authors emphasize that the use
of geolocation data plays a key role in improving
customer interaction and creating new touchpoints
with users.
E. Nematli [9] examines the role of electronic banking
in the development of the modern financial system.
The study notes that the digitalization of services
inevitably leads to the integration of geolocation
systems into service mechanisms, including targeted
offers and enhanced user experience.
A separate study by S. Maity and T.N. Sahu [8] explores
the impact of branch accessibility on financial inclusion.
The researchers highlight that geolocation data analysis
is applied to optimize branch locations and improve
accessibility, particularly in regions with low population
density.
Several sources focus on the application of
geomarketing in financial services. T. Crisóstomo-
Berrocal, F. Sierra-Liñan, and C. Carbonell-Michael [2]
describe digital platforms based on geomarketing and
their influence on small and medium-sized enterprises.
A similar topic is addressed by A. Madleňák [7], who
analyzes location-based marketing communication in a
global context. The article presents examples of how
financial organizations leverage spatial data for
personalized advertising and targeted offers.
Industry reports provide statistical summaries and
market forecasts on geomarketing development [4],
indicating that financial institutions are actively
investing in geolocation analytics technologies to
enhance the accuracy of strategic decisions.
A separate area of literature is dedicated to security
concerns. D. Komosny [5] examines the method of
retrospective geolocation of IP addresses and its
application in geographically adapted internet services.
This approach is particularly relevant for fintech
companies engaged in cybersecurity and fraud
prevention.
The study by C.D. Au, Ph. Krahnhof, and L.
Klingenberger [1] focuses on analyzing the needs of
financial institution clients. The research demonstrates
that modern users expect a personalized approach,
including services based on their geographic location.
Additionally, industry reports provide up-to-date
summaries on the impact of geolocation strategies on
marketing campaigns [6, 10], presenting statistical data
on the effectiveness of personalized offers.
A review of literature and contemporary materials
highlights that the use of geolocation data in financial
services is considered from multiple perspectives.
However, certain shortcomings persist, including
varying approaches to data interpretation, ethical
concerns that remain superficially addressed, and a lack
of detailed descriptions of data analysis algorithms.
Most studies focus only on general principles of
geolocation usage, while specific machine learning
methods are rarely described in detail.
For this study, the following methods were used:
comparison,
statistical
data
processing,
systematization, and generalization.
RESULTS AND DISCUSSION
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Geomarketing is a field that utilizes user location data
to structure marketing initiatives, employing digital
mapping tools to organize and visualize data for
evaluation and decision-making [2, 7]. In recent years,
this market has been growing rapidly, as illustrated in
Figure 1, which includes projected values
Fig. 1. Dynamics of the geomarketing market volume (with forecast) [4]
According to 67% of marketers, the most significant
advantage of leveraging location-based services to
enhance efficiency and service quality is targeted
marketing [6]. This enables narrowing the audience to
users with a relatively high likelihood of brand loyalty.
Geolocation data refers to information about a user’s
location obtained through GPS, Wi-Fi, cell towers, or
Bluetooth technology. Its application in the financial
sector is carried out through several key mechanisms
(Figure 2).
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The American Journal of Management and Economics Innovations
Fig. 2. Areas of application of geolocation data in the financial sector (compiled by the author based on [1-3, 5-
9])
Financial institutions can identify patterns in customer
movements, allowing them to create personalized
offers. For instance, if an individual frequently visits
shopping malls, a bank may offer a credit card with
increased cashback for retail purchases.
The analysis of movement routes helps assess a
borrower’s solvency. Regular trips to business districts
or visits to high-end establishments serve as indirect
indicators of a high income level.
Geolocation data facilitates the identification of
suspicious transactions. If a card is used in two different
countries within a short time frame, the security system
blocks the transaction and prompts an identity
verification request.
The analysis of customer movements enables banks to
make informed decisions about ATM and branch
placement in areas with high user activity. Let’s
consider two hypothetical examples:
1.
A bank analyzes customer movements in
downtown New York using data from mobile
applications and transactions. Over the course of a
month, 10,000 consumers conduct transactions in the
Times Square area. On average, each customer visits
the area 15 times per month. The data shows that 40%
of customers use ATMs, while 60% visit bank branches.
The average number of cash withdrawals per customer
is two per month, with an average withdrawal amount
of $100. This means that ATMs in the area process
8,000 transactions per month, totaling $800,000. The
bank decides to install two additional ATMs, expecting
to increase customer flow by 20% and reduce the
burden on existing branches.
2.
The owner of a small regional bank analyzes
customer movements in the suburbs of Dallas. Within a
5-mile radius of an existing branch, there are 50,000
residents, 5,000 of whom are active bank customers.
Mobile data analysis reveals that 3,000 consumers pass
through a major shopping center daily, but there are no
ATMs in that location. On average, a customer uses an
ATM three times per month, with an average
withdrawal of $80. If an ATM is installed in the shopping
center, an estimated 9,000 transactions per month
could be expected, totaling $720,000. The bank decides
to install the ATM, anticipating a 10% increase in the
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number of customers due to the convenience of the
new location.
The use of spatial data unlocks new opportunities for
service personalization. Figure 3 summarizes the key
benefits.
Fig. 3. Highlighting the advantages of using geolocation data to personalize financial services (compiled by the
author on the basis of [2, 5, 7-9])
Commenting on the presented scheme, it is important
to highlight the following aspects. A financial institution
can provide customers with special offers based on
their location. For instance, if a user is near a car
dealership, they may receive an offer for an auto loan
on favorable terms. Additionally, recommendations
can include nearby bank branches, currency exchange
offices, and insurance products relevant to the user’s
current location. Let’s consider a hypothetical example.
A bank in Los Angeles utilizes geolocation data for
personalized offers. Over the course of a month, 50,000
customers are detected within a 500-meter radius of
car dealerships, with 5% of them (2,500 people) having
previously expressed interest in auto loans via the
mobile app or website. The bank sends these
consumers personalized offers with a reduced interest
rate. Assuming that 10% of recipients (250 people)
respond and take out an auto loan with an average
amount of $30,000 for a five-year term at an annual
interest rate of 5%, we can calculate the bank's profit.
With annuity payments, the monthly payment per loan
is approximately $566. The total amount paid over five
years is $33,960 per customer, generating a profit of
$3,960 per loan. For 250 customers, the total profit
amounts to $990,000 over the full loan term. Thus,
leveraging geolocation data allows the bank to increase
the volume of issued loans and generate additional
revenue while enhancing customer convenience.
In mobile banking, identity verification is partially based
on geolocation data, reducing the need for additional
checks.
Finally, personalized offers create a sense of individual
approach among customers, enhancing satisfaction
and brand loyalty. These factors are crucial for
strengthening customer retention.
Despite its clear advantages, the use of geolocation
data presents several challenges, including:
●
Privacy concerns;
●
Protection of personal information;
●
Errors in data interpretation;
●
Risks of data misuse;
●
Ethical issues.
The collection and processing of spatial data streams
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require strict compliance with data protection
regulations. Breaches can lead to severe consequences,
including financial losses for customers.
The mere presence of a user in a specific location is
insufficient for making accurate conclusions. For
example, visiting an expensive restaurant does not
necessarily indicate high solvency, as the individual may
simply be accompanying a friend.
Some unethical companies exploit geolocation data for
manipulative purposes, such as restricting access to
certain financial services based on location.
Finally, excessive intrusion into customers' personal
space can provoke a negative reaction. Maintaining a
balance between personalization and privacy requires
careful regulation.
CONCLUSION
The use of geolocation data in the financial sector
provides
extensive
opportunities
for
service
personalization, enhanced convenience, and improved
security. However, the implementation of such
technologies requires a well-thought-out approach that
carefully considers both technological and ethical
aspects.
As artificial intelligence continues to advance and data
processing techniques evolve, the applications of
geolocation in financial services are expected to expand
further. In the future, hybrid analytical models will
likely develop. The combination of geolocation data
with other sources
—
such as social media activity,
transaction history, and mobile app preferences
—
will
enhance the accuracy of personalization.
Additionally, privacy protection mechanisms will
improve. New anonymization techniques will be
introduced
to
safeguard
user
data
without
compromising service quality. Finally, financial
decision-making processes will become more
automated. AI will not only offer users discounts and
bonuses but also provide comprehensive financial
strategies tailored to their lifestyle.
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