Customer experience vs data security in AI deployments in the financial
industry
Bachelors Thesis – Proposal
Ankit Kumar Sharma
Ankit.sharma@stud.hslu.ch
Supervisor:
Sophie Hundertmark
sophie.hundertmark@hslu.ch
This Bachelor Thesis Proposal was submitted as part of the requirements for
the BSc in International Business Administration at the School of Business,
Lucerne University of Applied Sciences and Arts.
December, 2024
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Table of Contents
1. Introduction .......................................................................................... 3
o
Background and Problem Statement
o
Research Gap and Relevance
o
Research Questions and Objectives
2. Theoretical Background ...................................................................... 9
o
AI in the Financial Sector
o
Finance Sector and Customer Experience
o
Legal Framework for Data Protection and Privacy
o
How to Prioritize between Customer Experience and Data Security
3. Methodology ....................................................................................... 13
o
Research Design
o
Empirical Design
o
Data Gathering
o
Data Analysis
o
Attention to Ethics
o
Limitations of the Study
4. Bibliography ........................................................................................ 17
5. Initial Draft of Bachelor’s Thesis …………….................................. 20
6. Annotated Bibliography ..................................................................... 21
7. Action Plan ……………....................................................................... 27
8. Reflection Report ……………………................................................. 29
9. Declaration of Sole Authorship .......................................................... 30
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1. INTRODUCTION
1.1 Background & Problem Statement
Artificial Intelligence (AI) has become the front-and-center topic for various groups of
individuals regardless of their profession, age or geographical location. In the past few decades, not
only has digital technology outpaced our ability to catch up, but so has the emersion of newer
technologies and tools such as AI. In order to better understand the meaning of AI, Aldoseri et al.,
(2024) describe artificial intelligence as an ability for systems to comprehend, develop and solve
complex tasks set forth. The same article also suggests that there is no singular definition for AI and
variances include the ability to utilize and analyze data without requiring to program such convoluted
responses. Currently, the combination of AI and digital technologies have become essential to an
extent where a company deploying such tactics benefit heavily from enhanced customer relations and
experiences (Huang et al., 2021). AI has therefore grown at an alarming rate where firms with
applications of AI have advanced farther than several juggernaut academic research institutions in
terms of research and development (Färber & Tampakis, 2024, p.38).
However, focusing on the field of finance and banking, AI has been increasingly being adopted
by Swiss financial institutions, banks and insurance companies (Swiss Financial Market Supervisory
Authority [FINMA], 2023). These applications usually range from chatbot integrations to claims
processing and distribution, fraud detection systems and pricing models. For instance, Swiss banks
like UBS are harnessing the capabilities of AI to power their chat bot systems and utilizing AI
projections to grant immediate credit (Reuters, 2024). FINMA (2023) also detailed several challenges
arising out of these applications which generally surround areas such as accountability, reliability,
robustness, and transparency. It has become apparent that customers do tend to revel in convenience
and enjoy added benefits and features of AI. Aleksandrova et al. (2023) explore how several
individuals in the field of finance may view AI as a threat to their livelihood and career, however,
they emphasize that AI acts as a tool rather than a replacement for employees. Institutions
implementing the use of AI in financial control, insurance and risk management procedures often
replace repetitive non-cognitive tasks with the use of AI while enabling human work to be much more
cognitive. Financial services companies are rapidly adopting AI as the customer trends point in the
direction of integration of such tools into their day to day client interactions.
Aleksandrova et al. (2023) point out to how banks, insurance companies and firms in the FinTech
(Financial Technology) field implement AI into their operations, services and customer support
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departments. An article written by Chung et al., (2020) on Mckinsey & Company argue that
customers have now come to expect integration of AI into their financing and banking needs as a
necessity. These users tend to utilize the personalization features offered by the models,
recommendations or suggestions and consistency offered by AI. For instance, AI-powered tools can
seamlessly bridge the gap between switching from web browsers or similar platforms to a banking
application downloaded on the user mobile for convenient transition. Alternatively, approval of loans
and credits done instantly by the AI models online or assistance and support of resolving transaction
issues can be facilitated by AI at the benefit of end consumers.
However, the rapid growth and advancement of such applications could raise concerns regarding
data protection and consumer security. In order to provide convenient services and lightning-fast
response times, AI models have to be fed an incredible amount of sensitive and private user data to
enable them to automatically recommend, feature useful resources or grant instant loans or credits
(Ridzuan et al., 2024). Additionally, unfair practices or biases may also arise out of these solutions
that may lead to unintentional incidents such as discrimination on loan approval for applicants based
on genders or race or opaque decision making procedures that would be difficult to explain to end
consumers. Certain other non-consumer related challenges might include the lack of expertise in data
scientists that are well-equipped and experienced with sifting through AI-processed data. Sriram et al.
(2023) also add to the expertise aspect of AI-powered tools by elaborating how information and data
fed to these tools might be interpreted falsely or in a way that is not intentional. They discuss the use
of a tool for processing insurer policy data which challenges the user with population of criteria and
datasets as intended by the institution.
Svetlova (2022, p. 720) illustrates that the inter-twined nature of large financial institutions and
systems paired with opaque AI decision-making tools may not only lead to flawed risk assessment
when it comes to granting individual loans but may also trigger similar outcomes in other firms and
institutions. Simultanesouly, during a bad economic cycle, data provided to these models may
interpret such cycles as an ordinary situation and may lead to negative outcomes for end consumers.
This could lead to loss of trust from consumers and at the same time might risk predatory practices
from less reputable institutions that target such consumers. Behavior profiling and group-based
discrimination standout as one of the most prevalent forms of ‘AI attacks’ when it comes to data
processing (Mühlhoff, 2023). However, data protection regulations with regards to AI data processing
is yet another topic that Switzerland takes seriously.
According to Butt (2024, p. 13), the GDPR (General Data Protection Regulation) of 2016
imposes several challenges for firms and institutions implementing the use of AI technology in their
daily operations. For instance, GDPR requires establishments to only process necessary data useful
for carrying out their operations. This might contradict the existence and impact the development of
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AI based on behavioral learning and pattern recognition. Simultanesouly, GDPR guidelines requires
the firms to provide consent from the end user to submit their data for processing. The line is thus
blurred when it comes to processing large amounts of data that might be unintentionally fed by the
end user to these systems which is then gathered, sorted and analyzed by the AI systems. Data
retention is yet another aspect of the GDPR guidelines that restrict companies from storing data that is
unnecessary after the completion of a transaction or service provision. However, for AI systems to
detect fraudulent instances or to improve their algorithms for future scenarios, data processing,
refining, and re-processing is a core element. This leads to conflicting and contradicting applications
of AI when abiding by the GDPR guidelines.
1.2 Research Gap and relevance
In a Mckinsey study by Anant et al. (2020), customers are highly distrustful of their personal data
being packaged, sold and re-sold by numerous data harvesting and data capture points. Although
personalization of services remains the focal point, several customers tend to distrust firms that lack
transparency and act out of capitalistic endeavors. The same study cites that 87% of customers would
not conduct any business with an institution or firm that misuses their data or if the customers feel
unsafe sharing their data with them. This dilemma creates a natural fork between the choice of data
protection or utmost convenience for customers. In such a situation, most customers tend to lean on
the side of convenience and are even willing to share their information provided that they trust the
firms in question. This delivers an opportunity for firms in various industries that plan on utilizing AI
models while catering to client needs by ensuring data protection and garnering a perception of
trustworthiness.
In the book by Leenes et al. (2017), the authors explain the importance of taking into
consideration consumer trust factor, data protection and privacy prior to harvesting such information.
It states that failure to provide securitized and privatized data capturing techniques leads to erosion of
trust amongst the customers and lack of transparency further exacerbates the situation by pushing
them away. For instance, the book discusses about the implications of the Cambridge Analytica
scandal where the political firm harvested countless amounts of data to create ‘personas’ that were
used to influence the 2016 election and the Brexit referendum. There was an enormous commotion
from the public directed towards Facebook which reduced their trust in the firm and led to several
users deleting their profiles. Simultaneously, Cambridge Analytica faced equal backlash which had
several legal, financial, business and industry-wide repercussions. Therefore, it is critical for firms in
the financial sectors to explicitly indicate their data harvesting and processing practices prior to
onboarding customers.
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Despite all these studies and research papers, there is little concrete evidence to suggest that
customers prioritize data protection over experience based on AI features that engage in mass data
harvesting and profiling. Anant et al. (2020) indicate in their study that customers prefer protecting
their personal information and generally permit healthcare or finance sector firms to openly utilize
them however, it is not clear whether these opinions are shared in other regions of the world.
Additionally, the reasoning or rationale behind the consumers to express such opinions is not
characterized by age, tech-savviness, convenience, cultures, or other such attributes.
The researcher conducted in-depth analysis of several articles and books published of scientific
orientation and failed to retrieve excerpts detailing the logic and rationale of end consumers with
regards to the topic of data protection vs. customer experience in an AI-oriented world within the
financial sectors. Several studies pointed out to the rules and regulations laid down by GDPR and how
AI learning and data processing algorithms requires mass data processing and re-processing which
conflicts with an institution operating within EU without legal risks. However, most studies do not
factor in the consumer perspective and what factors drive them towards prioritizing or selecting one or
the other. This depicts the need for research from a different point of view where numerous attributes
are weighted and calculated when assessing customer preferences in the financial sector. Additionally,
these studies lacked emphasis on situations or scenarios relating to the prioritization of the two
elements. For example, the Mckinsey study conflates healthcare and financial sectors as highly
trustworthy from the consumer perspective whereas other sectors are not considered as trustworthy.
Questions regarding whether situations within the financial sector with regards to loan approval,
mobile banking or other such primary and secondary activities would result in similar outcomes
arises. Experiences of customers with regards to predictive conversations or immediate fraud
detection systems also require further research. A small pocket for behavioral economics also exists
when evaluating the dilemma, a customer is involved in amongst data protection or convenience.
However, the researcher will not include these dynamics for this thesis due to time constraints and
scope of the paper.
These gaps in available scientific materials leads the researcher to conduct thorough research and
analysis of customers encompassed within the financial sector on routine basis. Additionally,
quantifying and measuring these two parameters with the help of attributes relevant to the general
public can provide all the stakeholders involved with insightful data and information regarding the
criteria that customers deem valuable over others. Demographics often play a key role in determining
motivational factors in decision-making for customers. Hence, such gaps in scientific research exist
which are to be covered by this thesis.
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1.3 Research Question and Objectives
The aim of this study revolves around understanding customer preferences and what drives them
towards selecting AI-driven customer experience or data protection as a top priority within the
constraints of financial applications and channels. The research question answered by this thesis is:
What is the focal point for customers in an AI-driven finance sector of Switzerland: customer
experience or data protection?
This research question is to be answered in tandem with several objectives that encapsulate the
core of this research thesis. In order to address the core dilemma, the decision-making factors and
utility generated out of individual experiences is to be quantified vividly. The following objectives
help the researcher reach a conclusion that provides practical implications and action-oriented
outcomes and scenarios that would be applicable to firms and institutions within the finance industry
in Switzerland:
-
When engaging with AI-directed financial services and products, what key factors play
a decisive role in persuading customers to opt for customer experience over data
protection
-
Which specific scenarios illustrate an outlier when benefitting from financial services
where customers might prioritize one of the parameters over the other?
-
Do demographics such as age, wages, gender, or education levels sway their decision or
do other attributes such as tech-savviness or being an early adopter of new technology
play a crucial role in shaping their decisions?
-
Under what circumstances are the customers open to compromising their beliefs or
opinions in between the two parameters?
-
Do customers believe that the legal and regulatory framework together with the policies
and regulations implemented by the financial institutions align with their own standards
of data protection?
Each of the above objectives provide detailed information and substantive reasons that help
answer the research question. Additionally, the outcomes of the aforementioned objectives could
shape institutional policies and regulations when it comes to data protection and customer experience
curation. The results of this research thesis are expected to conclude with precise details stating the
factors involved in decision-making as well as the situations that may set the parameters to flip in
specific scenarios.
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1.4 Significance of the Research
This research thesis aims at contributing to numerous facets of society. From an academic
standpoint, the outcomes of this thesis provide educational institutions with detailed study on how an
ever-evolving world catches up to the rapid changes of technological advances all the while ensuring
legal and ethical standards. AI ethics are likely to establish itself as a core branch of ethical studies
where this study will contribute positively towards future research and studies. Additionally, this
thesis also bridges the literature gap currently present with regards to this topic.
From an industry perspective, the outcomes of this thesis aim to provide practical implications
for firms and institutions in the finance sector with concrete and substantive evidence. The results
arising out of this thesis are also set to garner trust among customers and institutions which would
impact society in a positive manner. Additionally, following the developments from leading financial
and fintech companies re-writing their framework for AI ethics and customer experience, policy
makers would also benefit from the application of these measures in a business environment. This
aims to persuade the policy makers to revise and re-write the ethical guidelines and regulations with
respect end consumers in mind.
Therefore, the researcher believes that topic allocated is of crucial importance and is set to impact
social, legal, business and ethical frameworks in a positive manner. It encompasses one of the most
sought-after technology and ensures that customers have deep-rooted trust with the firms in question
which would be backed by substantive study.
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2. THEORETICAL BACKGROUND
2.1 AI in Finance Industries
According to Remolina and Gurrea-Martinez (2023), AI in the finance and banking sector began
with the adoption of simpler programs and software that automated repetitive tasks such as cash
withdrawal from an ATM or payment processing networks. This quickly took over other crucial
aspects of banking industry such as chatbots, detecting and flagging fraudulent activities, and sorting,
analyzing user data to provide sound recommendations to firms. Similar story plays out in adjacent
sectors as well such as stocks trading and insurance. Algorithmic trading includes very simplistic AItools that follow a set of rules and instructions which have been in play since the 1990s. Gradually,
this evolved to high-frequency trading devices that interpret real-time data for immediate execution as
well as several portfolio management tools such as Wealthfront. AI applications within the insurance
industry involved the usage of statistical data for risk management, processing of customer claims
reports and eventually offering a direct point of contact in the form of chatbots. Remolina and GurreaMartinez (2023) discuss the Robo-Advisers case study from More Wealth where the firm’s
implementation of AI-powered systems allowed them to reduce costs by increasing efficiencies,
provided scalability, and enhanced user experience via personalized customizations. However, these
applications came with their own set of challenges particularly with compliance and unbiased
outcomes arising out of machine learning models. Consumer trust was seen as yet another key issue
especially when the decision-making was not deemed transparent. Yilmaz et al. (2023) also expand on
the previously mentioned benefits but add to portfolio management use cases for creating a much
more diversified investment cluster using algorithms and data processing. In the absence of market
stability, reinforcement learning has assisted certain firms to price and risk assess numerous
portfolios, execute trades based on those risks analyzed and calculate tax policies to lower tax burdens
to the minimum.
Zheng et al. (2023, p.65) highlight the numerous challenges that AI face in FinTech, specifically
transparency. They argue that the application of blockchain systems and networks are far more
reliable from a customer standpoint due to their decentralized nature. However, when it comes to AI
applications, the trust erodes quickly. Citing the case of Industrial and Commercial Bank of China, a
ransomware attack was detected in 2023 where a single click from a customer of the bank led to
large-scale data extraction of sensitive information. These challenges are exacerbated when numerous
technologies and systems are in use.
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2.2 Finance Sector and Customer Experience
In a study consisting of 226 participants, Tulcanaza-Prieto et al. (2023) examine the impact AIdriven tools and services have on them in the banking sector. The outcomes indicated that consumer
trust in these solutions is high provided that the institutions offering such services are trusted by the
public and their data is securely managed. However, the services being offered have the obligation to
work at the customer’s convenience and at their whim in order to instill a feeling of trustworthiness.
Access to information at lightning fast-speed is now an expectation for customers and their
willingness to adopt such technologies also resides in the fact that the users are well-educated in
general and are aware of AI systems. Noreen et al. (2023) also emphasize the importance of education
and general awareness about technological advancements for customers interacting with AI-powered
tools and services. Certain customers inherently accept the amalgamation of AI into finance simply
due to the fact that such countries, for example China, exhibit a strong digital space with advanced
tools and software designed for convenience. The presence of advanced digital infrastructure
automatically provides a heightened sense of data security for these customers especially when
coupled with stringent regulations and legal framework surrounding data protection. On the contrary,
cultural differences, such as in Saudi Arabia or Iran, illustrate a pessimistic view of AI as they are
deemed to be the competitors of human work which would eventually drive them out of jobs.
The capacity for AI to deliver personalized experiences that are curated to an individual’s
personal preferences and desires is described as one of the most impactful factors when interacting
within the finance sector (Sanodia, 2024, p.253). Moreover, other favorable sentiments expressed by
users depict the responsiveness and duration required for executing tasks or solving queries.
Predictive analytics that emphasize on investments or savings plan based on customer data is valued
highly as well. However, the customers also share their concerns with the author where, excluding
trust and data security, lack of human touch within these systems is approached as a negative
characteristic of AI-driven tools. Another key challenge expressed was the familiarity and the innate
ability of these services to overwhelm the consumer upon changing the layouts of the platform.
Sanodia (2024, p.255) suggests that education and perhaps crash-courses might assist in resolving
these minor issues. Another study also re-affirmed the findings mentioned prior regarding lack of
human touch (Shaikh et al., 2024, p.177). The author concluded that despite benefitting enormously in
terms of speed and convenience, the end customers preferred human interaction, implying that AI
tools should be used as a complement to conventional means rather than a replacement. Trust, data
security and the legal and regulatory framework involving AI ethics remained at the forefront of
customer concerns as implied by numerous studies and research papers previously.
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2.3 Legal Framework for Data Protection and Privacy
The Swiss Data Protection Act (DPA) of 1992 is a legal and regulatory framework set by
Switzerland to ensure compliance from all institutions and firms registered and operating in
Switzerland with respect to privacy, data harvesting, and usage (Federal Office of Justice, n.d.). This
framework was further revised in 2020 to keep up with advancing technologies and integrating
blockchain as well as AI driven products and services. Specific data security and technical control
protocols are established in the revised Federal Act on Data Protection (FADP) which further
strengthens trust in these institutions for resident of Switzerland. Moreover, failure to comply with
these standards, particularly in an event where data is transferred outside of the country, results with
fine and penalties and in severe cases sanctions.
GDPR or General Data Protection Regulation is an expansive data protection law which entered
effect in May 2018 for nations situated in the European Economic Area or part of the European Union
(GDPR-Info, n.d.). Principles of focus include integrity and confidentiality, storage restrictions,
access only to necessary data, limited or restricted data accumulation, and accuracy of the collected
data. The GDPR law grants substantial control in the hands of individuals as they command the right
to data access, erasure, portability, and objection to its usage for marketing purposes. The law holds
organizations and establishments accountable for data breaches where penalties may lead to fines of
up to 20 million Euro or 4% of annual global revenue.
Revolut is a UK-based financial technology business that offers banking services to most of
Europe, currency exchange, remittances etc. (Robinson, 2019). The company’s AI enhanced tools
targeted the prevention of fraud and money laundering, however, their implementation and execution
of the AI led to several legal and ethical complications for the business. For example, the tool detected
numerous transactions as fraudulent and flagged them in a false-positive setting. In order to vet the
information and data incoming, Revolut requires the ability to process massive amounts of data which
raised concerns over its compliance to the GDPR. Yahoo Finance (2023) highlights the lengthy
application process and ability to acquire a UK banking license leads to wider public and legal
scrutiny regarding the compliance to data protection laws in UK and EU. Although Revolut’s
jurisdiction is within the UK, it is undeniable that the firm operates essentially in Europe and
therefore, failure to comply by UK data protection laws raised serious questions over the ability to
comply with GDPR, especially after establishing GDPR law as one of the world’s most strict data
protection law (Bloomberg Law, n.d.). Such allegations dissolve the firm’s trust amongst the general
public and leads to a negative perception of the use of AI in finance sector as untrustworthy.
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Ridzuan et al. (2024) illustrate how firms struggle to balance between providing AI-driven
innovative services while complying with data protection laws in their respective states. Naturally, the
benefits of integrating these technologies are mandatory in a world where AI-powered firms display a
distinct advantage. However, skilled teams are necessary to monitor, adjust and pivot when bias,
unfairness or breach of data is detected. Ridzuan et al. (2024) also point how Europe and Switzerland
are taking strides in encouraging and fostering innovation within these sectors with Basel Committee
of Banking Supervision and European Insurance and Occupational Pensions Authority promoting the
use of AI in an ethical context.
2.4 How to prioritize between Customer Experience and Data Security
Ahmed et al. (2024) examine the influence AI has on the financial sector with respect to customer
interactions. It is suggested that innovation and customer experience must remain at the forefront of
development and progression of primary applications all the while ensuring that data security
standards are upheld. Certain concrete measures such as two-factor authentication (2FA) as well as
highly protected (via encryption) data storage systems are recommended. Following such a customercentric path, cumulative developments rather than monumental leaps in AI-driven systems is
recommended. Prakarsh et al. (2024) effectively communicate the balance by suggesting the ability
for users to monitor and control their data, all the while being able to ‘pull the plug’ from such
services if they deem their privacy is being violated. In such a context, transparency from the firms is
of critical value and assists in balancing innovation and customer experience with data protection and
legal compliance. The researchers in that study illustrate the example of Clearview AI, an application
for law enforcement authorities used by coupling facial recognition technology with the publicly
available images of individuals on social media platforms. Although the application enhanced the
performance and reduced inefficiencies for law enforcement authorities, it created a whole new array
of privacy concerns including legal compliance with GDPR and unfair algorithmic biases. This case
exemplifies the requirement for such firms to transparently communicate their data harvesting
practices combined with legal compliance.
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3. METHODOLOGY
Methodological design of a research thesis is an integral part of understanding and rating the
quality of research and analysis conducted. Substantive and concrete analysis often lead to practical
implications that could be applied on mass industry wide.
3.1 Research Design
For the researcher to better understand the forces driving a customer’s decision towards engaging
or disengaging with technologies supportive of AI systems and tools within the financial sector, a
descriptive segment of research is deployed for substantive outcomes (Erickson, 2017). These consist
of closed-ended questions that target the rationale of customers in a quantifiable manner, particularly
through the use of surveys or questionnaires. For instance, a typical question answered by this aspect
of research include ‘What percentage of users feel the need to utilize financial services that integrate
AI-powered tools?’ or ‘What demographic subsets discourage customers from sharing their private
information with firms within the finance sector utilizing AI-driven systems?’.
Erickson (2017) further adds the implementation of exploratory research whereby, due to lack
of existing research or data, the combination or addition of open-ended questions help delve deeper
into the subject matter. With respect to this research thesis, exploratory aspect of the research design
will be fruitful for the researcher in narrowing down the themes, topics and factors that motivate
customers to prioritize data privacy over customer experience. Cross-referencing these outcomes with
those from the descriptive segment of the research, the researcher can provide concrete evidence
regarding the research question.
Figure 1 below portrays a decision tree for a variety of research studies based on the
requirements and tasks at hand (Centre for Evidence Based Medicine, n.d.). Although this article
focuses on medicinal research, the overarching ideas can be applied to traditional research.
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Figure 1
Evidence-based research tree. Adapted from Study designs, by Centre for Evidence-Based Medicine,
n.d., University of Oxford (https://www.cebm.ox.ac.uk/resources/ebm-tools/study-designs).
3.2 Empirical Design
As per Davies and Hughes (2014), qualitative research revolves around problem statements
arising out of theoretical frameworks and concepts which demand inductive ideology while
quantitative research generally takes quantitative data to be paramount in a deductive approach. The
target population is primarily customers in Switzerland benefitting from financial services or tools
that integrate artificial intelligence features. The researcher categorizes the population surveyed
primarily via key demographic groups, particularly the young customers – aged 18 to 30, the frequent
customers – aged 31 to 50, and the experienced customers – aged 51 and above. These 3 groups could
illustrate certain difference with regards to digital literacy, however, Buckingham (2020) argues that
digital literacy is generally arising out of educational background, social and cultural norms, and other
such factors. Another focal point for the researcher includes the types of services utilized by
customers with regards to the research question and whether a specific set of services imply a
different standard or perspective for end customers in their dilemma of prioritizing data protection
over customer experience and innovation. All such data sets assist the researcher in generating sound
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analysis of the problem statement at hand and further enables him to understand various customer
attitudes.
The researcher is set to deploy probability sampling techniques, specifically the stratified
sampling. In this method of sampling, the researcher can target specific groups of the population that
interact with AI-driven financial sectors. This is further divided into people of varying age groups,
digital literacy, annual incomes, and similar characteristics useful for this thesis.
3.3 Data Gathering
Initiating with a background knowledge and understanding of existing studies or similar studies,
secondary data will be compiled and analyzed. These sources would ideally include case studies,
industry analysis and scientifically acceptable academic literature studies. Sources would include the
official departments and associations of Switzerland such as FINMA, Swiss Bankers Association, EU
GDPR authorities, and encompass independent research firms and organizations as well such as
Mckinsey or similar Swiss counterparts.
Primary data sources will essentially be in the form of surveys sent mostly online or in some cases
on paper. These surveys aim at gathering quantitative data in the form of Likert scale or rating
questions as well a handful of qualitative data points via the addition of open-ended questions mostly
requiring the participants to articulate trade-offs or opinions regarding this matter. The method of
transmission is still under consideration; however, popular platforms and channels include survey
monkey and Google forms.
3.4 Data Analysis
For questions emphasizing the qualitative aspects of the survey, themed responses will be
analyzed and structured in an organized manner through manual coding. The keywords will dictate
the outcomes of these responses in a transparent manner where evident-based analysis would illustrate
the reasons or factors for customer choices. With regards to quantitative analysis, simpler statistics
would be an integral part of the study, such as mean, medians, and standard deviations. Likert rating
scale coupled with demographic attributes assist the researcher in pin-pointing the factors and key
characteristics of the participants surrounding the subject matter. Additionally, regression analysis
will also be a vital component of data analysis to unravel the dependence of customer’s experience vs
data protection preferences.
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3.5 Attention to Ethics
Given that this research thesis is part of an ethical study in of itself, the researcher aims to carry
out strict measures of data protection and data capture via surveys distributed. Undoubtedly, the
research must comply with the Swiss Data Protection act and only capture relevant data useful for
this study. Storage, handling and masking the information, specifically the personal details, of
participants is of utmost priority which will adhere to the standards set forth as per the laws.
Consent of participants together with the assurances of their confidentiality are a given prior to
engaging with them.
3.6 Limitations of the Study
Understandably, this research thesis recognizes the vast amount of challenges embedded in a
study of this scope and magnitude. Initially, the survey might represent some bias from participants
due to certain social expectations of being open-minded and accepting towards newer technologies
and innovation. To counter such scenarios, the researcher will emphasize on the anonymity and
invisibility aspects of the survey. This enables the participants to express honest perspectives. Another
bottleneck might include access to data from large institutions that might withhold such information
for business reasons. However, the researcher aims to access a vast amount of information from
public sources that often cite such sources in their studies and dossier. Time is yet another limitation
in this study. Since the submission deadline of this thesis affords the researcher only 4 months’ worth
of time, the scope of the study will be limited. Additionally, the study is restricted specifical to the
geographical region of Switzerland, implying that the outcomes might not be applicable to other
regions or nations of the world.
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Butt, J. (2024). The General Data Protection Regulation of 2016 (GDPR) meets its sibling the
Artificial Intelligence Act of 2024: A power couple, or a clash of titans? Acta Universitatis Danubius.
Juridica, 20(2), 7–52.
Centre for Evidence-Based Medicine. (n.d.). Study designs. University of Oxford. Retrieved
November 16, 2024, from https://www.cebm.ox.ac.uk/resources/ebm-tools/study-designs
Chung, V., Gomes, M., Rane, S., Singh, S., & Thomas, R. (2020, October 13). Reimagining customer
engagement for the AI bank of the future. McKinsey & Company. Retrieved from
https://www.mckinsey.com/industries/financial-services/our-insights/reimagining-customerengagement-for-the-ai-bank-of-the-future
17
Davies, M., & Hughes, N. (2014). Doing a successful research project: Using qualitative or
quantitative methods (2nd ed.). Palgrave Macmillan.
Erickson, G. S. (2017). New methods of market research and analysis. Cheltenham, England;
Northampton, MA: Edward Elgar Publishing.
Färber, M., & Tampakis, L. (2024). Analyzing the impact of companies on AI research based on
publications. Scientometrics, 129(1), 31–63. https://doi.org/10.1007/s11192-023-04867-3
GDPR-Info. (n.d.). General Data Protection Regulation (GDPR). Retrieved November 16, 2024,
from https://gdpr-info.eu/
Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C. (2021). A survey on AI-driven digital twins in
Industry 4.0: Smart manufacturing and advanced robotics. Sensors, 21(19), Article 6340.
https://doi.org/10.3390/s21196340
Leenes, R., van Brakel, R., Gutwirth, S., & de Hert, P. (Eds.). (2017). Data protection and privacy:
The age of intelligent machines. Oxford, UK; Portland, OR: Hart Publishing.
Mühlhoff, R. (2023). Predictive privacy: Collective data protection in the context of artificial
intelligence and big data. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231166886
Noreen, U., Shafique, A., Ahmed, Z., & Ashfaq, M. (2023). Banking 4.0: Artificial intelligence (AI)
in banking industry and consumer’s perspective. Sustainability, 15(4), Article 3682.
https://doi.org/10.3390/su15043682
Prakarsh, P., Mansi, M., & Vardhan, H. (2024). Balancing innovation and privacy: Assessing the legal
implications of artificial intelligence in the context of privacy rights and data protection. International
Journal for Multidisciplinary Research, 6(5). https://doi.org/10.36948/ijfmr.2024.v06i05.26746
Remolina, N., & Gurrea-Martinez, A. (Eds.). (2023). Artificial intelligence in finance: Challenges,
opportunities and regulatory developments. Cheltenham, UK; Northampton, MA: Edward Elgar
Publishing.
Ridzuan, N. N., Masri, M., Anshari, M., Fitriyani, N. L., & Syafrudin, M. (2024). AI in the financial
sector: The line between innovation, regulation and ethical responsibility. Information, 15(8), Article
432. https://doi.org/10.3390/info15080432
18
Robinson, H. (2019, March 1). Revolut faces FCA probe over compliance failing. The Telegraph.
Retrieved November 16, 2024, from https://www.telegraph.co.uk/technology/2019/03/01/revolutfaces-fca-probe-compliance-failing/
Sanodia, G. (2024). Enhancing CRM systems with AI-driven data analytics for financial services.
Turkish Journal of Computer and Mathematics Education, 15(2), 247–265.
https://doi.org/10.61841/turcomat.vl5i2.14751
Shaikh, A. A., Kumar, A., Mishra, A., & Elahi, Y. A. (2024). A study of customer satisfaction in
using banking services through artificial intelligence (AI) in India. Public Administration and Policy,
27(2), 167–181. https://doi.org/10.1108/PAP-05-2023-0060
Sriram, V., Fan, Z., & Liu, N. (2023). ECLIPSE: Holistic AI system for preparing insurer policy data.
Risks, 11(1), Article 4. https://doi.org/10.3390/risks11010004
Svetlova, E. (2022). AI ethics and systemic risks in finance. Big Data & Society, 2(1), 713–725.
https://doi.org/10.1177/20539517231166886
Swiss Financial Market Supervisory Authority (FINMA). (2023). Artificial intelligence in the Swiss
financial market. Retrieved November 16, 2024, from
https://www.finma.ch/en/documentation/dossier/dossier-fintech/kuenstliche-intelligenz-im-schweizerfinanzmarkt-2023/
Tulcanaza-Prieto, A. B., Cortez-Ordoñez, A., & Lee, C. W. (2023). Influence of customer perception
factors on AI-enabled customer experience in the Ecuadorian banking environment. Sustainability,
15(16), Article 12441. https://doi.org/10.3390/su151612441
Yahoo Finance. (2023, March 3). Exclusive: Revolut audit queries spark skittish investors. Retrieved
November 16, 2024, from https://uk.finance.yahoo.com/news/exclusive-revolut-audit-queries-skittish203742350.html
Yilmaz, A. E., Rhyner, U., & Ankenbrand, T. (2023). Quantum computing and artificial intelligence
in finance. https://doi.org/10.5281/zenodo.12759147
Zheng, X., Gildea, E., Chai, S., Zhang, T., & Wang, S. (2023). Data science in finance: Challenges
and opportunities. AI, 5(1), 55–71. https://doi.org/10.3390/ai5010004
19
5. INITIAL DRAFT OF BACHELOR’S THESIS
Table 1 illustrates the planned content for the Bachelor’s Thesis on the subject matter. Since
this is an initial draft, the contents are not conclusive and are therefore subject to future changes.
Table 1
Initial Draft of Bachelor’s Thesis
SECTION
DESCRIPTION
Title Page
Includes the title of the thesis, information of the student and coaches, the
Bachelor’s Thesis client, location, date and name of the university.
Abstract
A short summary of the thesis that illustrates the topic, importance and
some intriguing facts arising out of it.
Table of Contents
A structured and detailed overview of chapters of the thesis.
Introduction
Briefly introduces the paper illustrating the impact of AI in finance sector,
customer experience importance and the challenges that might arise.
Literature Review
Scientific sources that cover similar topics are to be analyzed:
- Artificial Intelligence in Business and Finance
- Customer experience surrounding AI in finance
- Challenges faced by firms (legal and operational)
- Trade-offs
Methodology
Explaining the process of conducting primary research, specifically
surveying the target market.
Results
Illustrating the findings from the research conducted.
Discussions
Interpreting and discussing the results with additional tools.
Conclusions &
Concluding the research with a sound summary and sharing practical
Recommendations
implications for the finance industry.
Bibliography
List of all sources referenced alphabetically.
Appendices
Addition of supportive tables, figures, notes, as well as survey distributed.
Declaration of sole authorship included as well.
Note: Table created by author
20
6. ANNOTATED BIBLIOGRAPHY
Table 2 below indicates the current list of references and sources take under consideration for the
bachelor’s thesis. Due to the nature of the project, this list is subject to future adjustments.
Table 2
Annotated Bibliography
#
Source
Title
Type
Keywords
Relevance
1
Ahmed, F., Hussain, A., Khan, S. N., Malik, A. H.,
Asim, M., Ahmad, S., & El-Affendi, M. (2024).
Digital risk and financial inclusion: Balance
between auxiliary innovation and protecting digital
banking customers. Risks, 12(8), Article 133.
https://doi.org/10.3390/risks12080133
Digital risk and
financial
inclusion:
Balance
between
auxiliary
innovation and
protecting
digital banking
customers
Article
Digital risk,
financial
inclusion,
innovation,
banking
customers
Useful for
understanding
how innovation
in AI impacts
financial
inclusion and
what risks need
to be mitigated.
2
Aleksandrova, A., Ninova, V., & Zhelev, Z.
(2023). A survey on AI implementation in finance,
(cyber) insurance and financial controlling. Risks,
11(5), Article 91.
https://doi.org/10.3390/risks11050091
A survey on AI Article
implementation
in finance,
(cyber)
insurance and
financial
controlling
AI in finance,
cyber
insurance,
financial
controlling
Provides a
comprehensive
overview of AI
applications and
its role in
insurance and
finance, focusing
on operational
improvements.
3
Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A.
M. (2024). AI-powered innovation in digital
transformation: Key pillars and industry impact.
Sustainability, 16(5), Article 1790.
https://doi.org/10.3390/su16051790
AI-powered
innovation in
digital
transformation:
Key pillars and
industry impact
Article
AI, digital
transformation,
sustainability,
industry
impact
Highlights the
significance of
AI in advancing
sustainability
goals and
transforming
industries, useful
for understanding
broad
applications of
AI.
4
Anant, V., Donchak, L., Kaplan, J., & Soller, H.
(2020, April 27). The consumer-data opportunity
The consumerdata
opportunity
Report
Consumer
privacy, data
analytics,
Explores how to
balance
consumer data
21
5
and the privacy imperative. McKinsey &
Company.
and the privacy
imperative
Bloomberg Law. (n.d.). The EU General Data
The EU
General Data
Protection
Regulation
(GDPR)
Protection Regulation (GDPR). Retrieved
November 16, 2024, from
https://pro.bloomberglaw.com/insights/privacy/the-
financial
services
use and privacy,
relevant for
discussing ethical
AI
implementations
in finance.
Webpage
GDPR, data
protection,
privacy,
compliance
Explains GDPR
regulations,
essential for
analyzing legal
frameworks
governing AI and
data privacy.
eus-general-data-protection-regulation-gdpr/#whatis-the-gdpr
6
Buckingham, D. (2020). Epilogue: Rethinking
digital literacy: Media education in the age of
digital capitalism. Digital Education Review, 37,
230–239.
https://doi.org/10.1344/der.2020.37.230-239
Rethinking
digital literacy:
Media
education in
the age of
digital
capitalism
Article
Digital
literacy, media
education,
capitalism
Discusses the
societal impact of
AI on
information
access, critical
for understanding
the ethical
dimensions of AI
in education.
7
Butt, J. (2024). The General Data Protection
Regulation of 2016 (GDPR) meets its sibling the
Artificial Intelligence Act of 2024: A power
couple, or a clash of titans? Acta Universitatis
Danubius. Juridica, 20(2), 7–52.
GDPR meets
its sibling the
Artificial
Intelligence
Act of 2024: A
power couple,
or a clash of
titans?
Article
GDPR, AI
Act, regulatory
frameworks
Analyzes the
interplay
between GDPR
and AI-specific
legislation, useful
for framing the
legal challenges
of implementing
AI in finance.
8
Centre for Evidence-Based Medicine. (n.d.). Study
designs. University of Oxford. Retrieved
November 16, 2024, from
https://www.cebm.ox.ac.uk/resources/ebmtools/study-designs
Study designs
Webpage
Study designs,
research
methodologies
A practical
resource for
selecting
appropriate
methodologies
for the thesis
research.
9
Davies, M., & Hughes, N. (2014). Doing a
successful research project: Using qualitative or
quantitative methods (2nd ed.). Palgrave
Macmillan.
Doing a
successful
research
project: Using
qualitative or
quantitative
methods
Book
Research
methods,
qualitative
research,
quantitative
research
A detailed guide
on conducting
research, critical
for designing and
executing the
thesis study.
22
10 Erickson, G. S. (2017). New methods of market
research and analysis. Cheltenham, England;
Northampton, MA: Edward Elgar Publishing.
New methods
of market
research and
analysis
Book
Market
research,
analysis, data
interpretation
Discusses
innovative
approaches to
market research,
providing
techniques
applicable to
understanding
AI’s role in
customer-focused
industries.
11 Färber, M., & Tampakis, L. (2024). Analyzing the
impact of companies on AI research based on
publications. Scientometrics, 129(1), 31–63.
https://doi.org/10.1007/s11192-023-04867-3
Analyzing the
impact of
companies on
AI research
based on
publications
Article
AI research,
publication
analysis,
corporate
influence
Explores how
companies
contribute to AI
research through
academic
publications,
useful for
understanding
corporate roles in
advancing AI
technologies.
12 GDPR-Info. (n.d.). General Data Protection
Regulation (GDPR). Retrieved November 16,
2024, from https://gdpr-info.eu/
General Data
Protection
Regulation
(GDPR)
Webpage
GDPR, data
protection,
privacy
compliance
Summarizes
GDPR's key
provisions,
providing
foundational
knowledge for
analyzing AIrelated data
protection
policies.
13 Huang, Z., Shen, Y., Li, J., Fey, M., & Brecher, C.
(2021). A survey on AI-driven digital twins in
Industry 4.0: Smart manufacturing and advanced
robotics. Sensors, 21(19), Article 6340.
https://doi.org/10.3390/s21196340
A survey on
AI-driven
digital twins in
Industry 4.0:
Smart
manufacturing
and advanced
robotics
Article
Digital twins,
Industry 4.0,
robotics, smart
manufacturing
Provides a
comprehensive
review of AIdriven digital
twins, essential
for exploring
advanced
manufacturing
applications.
14 Leenes, R., van Brakel, R., Gutwirth, S., & de
Hert, P. (Eds.). (2017). Data protection and
privacy: The age of intelligent machines. Oxford,
UK; Portland, OR: Hart Publishing.
Data protection
and privacy:
The age of
intelligent
machines
Book
Data
protection, AI
privacy,
intelligent
machines
Offers an indepth look at
privacy
challenges
arising from AI
advancements,
useful for
framing legal and
ethical debates.
23
15 Mülhlhoff, R. (2023). Predictive privacy:
Collective data protection in the context of
artificial intelligence and big data. Big Data &
Society, 10(1).
https://doi.org/10.1177/20539517231166886
Predictive
privacy:
Collective data
protection in
the context of
artificial
intelligence
and big data
Article
Predictive
privacy, AI,
big data,
collective data
protection
Examines how
predictive
algorithms
impact data
privacy,
contributing to
discussions on
ethical AI
applications.
16 Noreen, U., Shafique, A., Ahmed, Z., & Ashfaq,
M. (2023). Banking 4.0: Artificial intelligence (AI)
in banking industry and consumers perspective.
Sustainability, 15(4), Article 3682.
https://doi.org/10.3390/su15043682
Banking 4.0:
Artificial
intelligence
(AI) in banking
industry and
consumers
perspective
Article
AI in banking,
consumer
perspective,
financial
technology
Analyzes AI
impact on
banking services,
focusing on
consumer
satisfaction and
technology
integration.
17 Prakarsh, P., Mansi, M., & Vardhan, H. (2024).
Balancing innovation and privacy: Assessing the
legal implications of artificial intelligence in the
context of privacy rights and data protection.
International Journal for Multidisciplinary
Research, 6(5).
https://doi.org/10.36948/ijfmr.2024.v06i05.26746
Balancing
innovation and
privacy:
Assessing the
legal
implications of
artificial
intelligence in
the context of
privacy rights
and data
protection
Article
Privacy rights,
AI regulation,
data protection
Explores the
balance between
innovation and
privacy laws,
essential for
understanding
AI’s
regulatory
challenges.
18 Remolina, N., & Gurrea-Martinez, A. (Eds.).
(2023). Artificial intelligence in finance:
Challenges, opportunities and regulatory
developments. Cheltenham, UK; Northampton,
MA: Edward Elgar Publishing.
Artificial
intelligence in
finance:
Challenges,
opportunities
and regulatory
developments
Book
AI in finance,
regulatory
developments,
financial
innovation
Provides a
comprehensive
examination of
AI's applications
and regulatory
hurdles in the
financial sector.
19 Ridzuan, N. N., Masri, M., Anshari, M., Fitriyani,
N. L., & Syafrudin, M. (2024). AI in the financial
sector: The line between innovation, regulation and
ethical responsibility. Information, 15(8), Article
432. https://doi.org/10.3390/info15080432
AI in the
financial
sector: The line
between
innovation,
regulation and
ethical
responsibility
Article
AI ethics,
financial
sector,
regulation,
innovation
Discusses the
ethical and
regulatory
challenges posed
by AI in finance,
highlighting key
innovation
trends.
24
20 Sanodia, G. (2024). Enhancing CRM systems with
AI-driven data analytics for financial services.
Turkish Journal of Computer and Mathematics
Education, 15(2), 247265.
https://doi.org/10.61841/turcomat.vl5i2.14751
Enhancing
CRM systems
with AI-driven
data analytics
for financial
services
Article
CRM systems,
AI analytics,
financial
services
Explores how
AI-driven CRM
systems enhance
customer
relationships in
financial
services, with
practical
applications.
21 Shaikh, A. A., Kumar, A., Mishra, A., & Elahi, Y.
A. (2024). A study of customer satisfaction in
using banking services through artificial
intelligence (AI) in India. Public Administration
and Policy, 27(2), 167181.
https://doi.org/10.1108/PAP-05-2023-0060
A study of
customer
satisfaction in
using banking
services
through
artificial
intelligence
(AI) in India
Article
Customer
satisfaction, AI
in banking,
Indian banking
services
Explores how AI
influences
customer
satisfaction in
banking,
providing
valuable insights
for consumerfocused AI
applications.
22 Sriram, V., Fan, Z., & Liu, N. (2023). ECLIPSE:
Holistic AI system for preparing insurer policy
data. Risks, 11(1), Article 4.
https://doi.org/10.3390/risks11010004
ECLIPSE:
Holistic AI
system for
preparing
insurer policy
data
Article
AI systems,
insurance,
policy data
preparation
Presents a
comprehensive
AI-driven system
for automating
policy data,
relevant for
insurance
innovations.
23 Svetlova, E. (2022). AI ethics and systemic risks in
finance. Big Data & Society, 2(1), 713725.
https://doi.org/10.1177/20539517231166886
AI ethics and
systemic risks
in finance
Article
AI ethics,
systemic risks,
financial AI
systems
Examines ethical
challenges and
systemic risks in
AI financial
systems, critical
for ethical AI
implementation.
24 Tulcanaza-Prieto, A. B., Cortez-Ordoñez, A., &
Lee, C. W. (2023). Influence of customer
perception factors on AI-enabled customer
experience in the Ecuadorian banking
environment. Sustainability, 15(16), Article 12441.
https://doi.org/10.3390/su151612441
Influence of
customer
perception
factors on AIenabled
customer
experience in
the Ecuadorian
banking
environment
Article
AI-enabled
customer
experience,
Ecuadorian
banking,
perception
factors
Analyzes how
customer
perception
affects AI
integration in
banking services,
relevant for
consumer-centric
AI research.
25
25 Yahoo Finance. (2023, March 3). Exclusive:
Revolut audit queries spark skittish investors.
Retrieved November 16, 2024, from
https://uk.finance.yahoo.com/news/exclusiverevolut-audit-queries-skittish-203742350.html
Exclusive:
Revolut audit
queries spark
skittish
investors
Webpage
Revolut,
investor
concerns,
fintech audits
Examines the
impact of audit
concerns on
investor trust in
fintech
companies, with
implications for
regulatory AI.
26 Yilmaz, A. E., Rhyner, U., & Ankenbrand, T.
(2023). Quantum computing and artificial
intelligence in finance.
https://doi.org/10.5281/zenodo.12759147
Quantum
computing and
artificial
intelligence in
finance
Report
Quantum
computing, AI
in finance,
future
technology
Explores
quantum
computing's role
in advancing AI
financial
applications,
providing
futuristic
insights.
27 Zheng, X., Gildea, E., Chai, S., Zhang, T., &
Wang, S. (2023). Data science in finance:
Challenges and opportunities. AI, 5(1), 5571.
https://doi.org/10.3390/ai5010004
Data science in
finance:
Challenges and
opportunities
Article
Data science,
financial
applications,
AI challenges
Examines the
intersection of
data science and
AI in finance,
discussing
opportunities and
challenges in
implementation.
Note: Table created by author
26
7. ACTION PLAN
The tables below illustrate a tentative plan of action from the author’s perspective with regards to
the fulfillment of the research required. As the thesis is yet to be initiated, all information and plans
below are subject to change.
Table 3
Tasks Schedule
Note: The author created this table
27
Table 4
Schedule
Note: Table created by author
28
8. REFLECTION REPORT
This bachelor thesis propsal is written within a short timeframe of only 4 weeks. The university
offered us the opportunity to find our own client and decide on a topic of our major. However, being a
non-native to the country and having limited contacts, I opted for the choice of topic selection from
the university’s platform. The allocation of topic took place on 28th October which was followed by a
few exchanges of emails with the supervisor, Ms. Hundertmark. Initially, this thesis was slated to be
written in German languge, however, due to lack of German language skills, the researcher requested
the supervisor to change the language to English. The topic in of itself is of great interest and thus
acted as motivators towards the write-up of the thesis proposal.
The first challenge I faced was structuring and outlining the proposal. Generally, few of my
strengths lie in organization and structuring. However, due to the scope and sheer size of the thesis as
a whole, I was peronally looking for additional advice or templates. This was quickly resolved thanks
to the presentations available to us coupled with the guidelines and documents provided on the
research topic assignment platform of the university. However, the second challenge was a more
personal one. Time was very limited for me as I had under 4 weeks to finish the proposal since I was
travelling out of country beginning of December. Although 4 weeks are sufficient in a vacuum, the
fact that I had to attend school, work 3 days a week and spend time with my family meant that I was
stretched thin. With a few days of sacrifices of sleep, I managed to relatively easily overcome this
specific constraint. One noteworthy challenge that I faced as well involved finding the right literature.
Since this topic had limited study and material on it, I was struggling to find scientific literature.
However, since the topic involved analyzing the legal frameworks, non-profit organizational analysis
and information provided by government bodies helped provide substantive insight into the subject
matter.
To conclude, these past 4 weeks allowed me to overcome some of my challenges and fears
regarding the topic and the support from the supervisor also paved the way for an expedited
completion of the proposal. Being able to excel at time-management and structure the thesis with
great precision strengthened my resolve and enabled me to gain more confidence. This experience and
the educational outcomes encourages me to look forward to tackling the bachelor’s thesis project in
2025.
29
9. DECLARATION OF SOLE AUTHORSHIP
I, Ankit Kumar Sharma, hereby certify that the attached work, Customer experience vs data
security in AI deployments in the financial industry, is wholly and completely my own work and that
I have indicated all sources (printed, electronical, personal, etc.) that I consulted. Any sections quoted
from these sources are clearly indicated in quotation marks or are otherwise so declared. I further
attest that I have included acknowledgments of the name(s) of any person(s) consulted in the course
of preparing this assignment, if any.
Signed:
Ankit Kumar Sharma
………………………………
Date: 27.11.2024
30