INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue X, October 2025

www.ijltemas.in Page 971

“Assessing the Relationship between Financial Technology

(FinTech) Implementation and Financial Performance: Evidence

from NSE-Listed Banks in India”

Mr. Shamantha Kumar B. U

., Dr. Devananda H. M.

, Dr. Prakash Rao K. S.

Research Scholar, Dept. of MBA Adichunchanagiri Institute of Technology, Chikkamagaluru – 577102

Research Supervisor, Dept. of MBA Adichunchanagiri Institute of Technology, Chikkamagaluru – 577102

Professor & Head, Dept. of MBA Adichunchanagiri Institute of Technology, Chikkamagaluru - 577102

DOI:

https://doi.org/10.51583/IJLTEMAS.2025.1410000118

Abstract: Using digital innovations such as cloud computing, blockchain, big data analytics, mobile banking, and artificial

intelligence to deliver financial services is known as Financial Technology (FinTech). FinTech prioritizes speed, accessibility, and

user-centric design in contrast to traditional systems. This study examines the impact of Financial, Technology (FinTech) adoption

on the financial performance of 32 public and private sector banks listed on the NSE in India from 2015–2016 to 2024–2025.

FinTech tools such as mobile banking, credit/debit cards, UPI, AI, and blockchain have transformed India’s banking system by

enhancing efficiency, accessibility, and customer experience, while also posing challenges in cybersecurity, regulation, and

restructuring. The research uses a quantitative, descriptive design with secondary data from RBI, the World Bank, and banks’

annual reports, applying descriptive statistics, correlation, and regression analysis. FinTech adoption is measured through

transaction-based indicators (mobile, credit, and debit usage), with bank size, GDP, and inflation as control variables. Findings

indicate that FinTech adoption significantly improves financial performance. Mobile banking enhances capital adequacy and

earnings through efficiency gains, while card usage strengthens liquidity and profitability via increased transactions and fee

income. GDP growth further boosts bank performance, whereas inflation has mixed effects on asset quality and liquidity. Private

banks, being more technologically agile, show faster adoption and better results compared to public banks, which face legacy

infrastructure and administrative hurdles.

Keywords: digital innovations, mobile banking, blockchain, UPI, GDP, liquidity, and profitability.

I. Introduction:

The functioning of any economy is strongly supported by the banking system, which mobilizes resources, links savers with

investors, and drives investment activities. However, the advent of Financial Technology (FinTech) over the past 20 years has

significantly changed the business. In general, fintech refers to the use of cutting-edge technology like blockchain, cloud

computing, digital wallets, mobile applications, artificial intelligence, and big data analytics to deliver financial services in a way

that is more effective, accessible, and user-friendly. Banks now face both possibilities and problems due to wave of technological

development, which has altered their business practices, service offerings, and competitive positioning in the financial industry.

In the past, banks provided services like deposits, loans, fund transfers, and investments through physical locations and in-person

contacts. This approach was frequently expensive, time-consuming, and inaccessible to underserved or rural communities. This

conventional approach was upended by the introduction of FinTech solutions, which provided quicker, less expensive, and more

practical substitutes. Peer-to-peer (P2P) payment systems, online lending platforms, robo-advisors, mobile banking, and the

Unified Payments Interface (UPI) are a few examples of developments that have fundamentally altered how consumers engage

and respond to the financial institutions. Customers now demand instantaneous transactions, individualized goods, 24/7 access,

and seamless digital experiences, forcing banks to reconsider their business plans and embrace new technologies.

From the perspective of banks, FinTech has a dual impact. On the positive side, the integration of FinTech brings several benefits,

including reduction in the cost in transactions, greater operational efficiency, improved customer relationships, and access to fresh

income channels. For example, the utilization of AI (Artificial Intelligence) in fraud detection and risk management has made

banking safer and more reliable. Similarly, blockchain-based solutions are improving transparency and reducing settlement times

in payments and trade finance. FinTech also allows banks to extend services to populations previously excluded from the formal

financial system, thereby contributing to financial inclusion and long-term growth.

The impact of FinTech adoption on banks’ performance will be different from bank to bank due to different factors such as size,

resources, technological readiness, customer base, and regulatory framework. Smaller banks might find the shift more difficult,

but larger Indian private sector and Indian public sector banks typically have the know-how and resources to invest in digital

infrastructure. While regulatory organizations such as the Central Bank of Indian, which also known as Reserve Bank of India

(RBI) impact the rate of adoption through regulations on cybersecurity, digital transactions, and financial inclusion, customer trust

and acceptance of digital banking also play a significant effect. In India, FinTech growth has been boosted by programs like

Digital India, Jan Dhan Yojana (JDY), and UPI. Additionally, the businesses and consumers are forced to use digital platforms,

making digital payments and mobile banking a significant component of transactions in COVID-19 Pandemic.

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This study looks at how banks' performance is affected by the usage of FinTech in relation to important financial factors as capital

adequacy (CA), asset quality (AQ), earnings, stability, and liquidity. Financial stability may be impacted by the high investment

costs and risks associated with digital solutions, such as cyber threats, even while they increase productivity and profitability. The

study emphasizes how FinTech is changing banks' overall competitiveness and health in a world that prioritizes digitalization.

II. Literature Review:

Xu (2025) conducted a systematic bibliometric review of FinTech–banking research, mapping publication trends, and main

research clusters such as payments, blockchain, and AI. The study concluded that FinTech investments improve operational

efficiency and non-interest income, though effects differ across bank sizes. It identifies methodological gaps that can be addressed

by Indian-based empirical studies on NSE-listed banks. Iatzaz Ul Hassan (2025) examined FinTech adoption across Asian

developing economies using panel and quantile regressions. The findings show that FinTech adoption enhances profitability,

particularly for low-performing banks and those operating under stronger regulatory frameworks. The results underline the

moderating role of governance in determining FinTech outcomes. Albuainain and Ashby (2025) presented a systematic review

of factors enabling or hindering FinTech adoption in the banking sector. They emphasized customer acceptance, organizational

readiness, and regulatory frameworks as critical success factors. Their review suggests that profitability improvements depend on

both technological investment and consumer adoption. Bueno et al. (2024) analyzed the impact of digitization on operational

efficiency across countries. They found that digitalization reduces operating costs and enhances service delivery, but benefits vary

depending on the institution's legacy systems. The study recommends using the cost-to-income ratio as a measure of operational

efficiency. Shivakumar (2024) conducted a panel data study on Indian commercial banks to examine FinTech adoption’s effect

on profitability. The research revealed that digital banking significantly improves ROA, particularly for smaller banks. It also

established a link between FinTech adoption and enhanced customer retention. Kharrat (2023) proposed a FinTech index based

on keyword analysis in bank disclosures to quantify digital adoption. This innovative approach found that FinTech intensity

positively correlates with bank profitability. The method is suitable for replicating similar analyses for NSE-listed banks using

textual data.

Global FinTech Adoption Index (2024) developed an NLP-based method to measure FinTech usage intensity in banks. The

study linked FinTech adoption with improvements in non-interest income and operational performance. It supports the use of

machine learning for measuring digital transformation. A Bahrain-based study (2024) analyzed Islamic and conventional banks

using a FinTech index capturing AI, blockchain, and payments innovations. Results showed a strong positive link between

FinTech output (e.g., mobile payment products) and profitability. The study encourages decomposing FinTech inputs and outputs

in future banking research. Pandey (2025) examined digital transformation, customer experience, and efficiency in Indian

banking. The research highlights that improved customer satisfaction through digital channels significantly boosts revenue growth

and profitability. This suggests incorporating qualitative customer data in future FinTech impact assessments. Shaikh (2025)

studied digitalization’s impact on banking structure, employment, and profitability in India. Findings revealed short-term cost

burdens due to high capex but longer-term efficiency gains. The author recommends using dynamic panel models to capture

lagged effects. PwC India (2024) released an extensive industry report exploring FinTech’s influence on traditional banks. It

emphasizes collaboration between banks and startups, noting efficiency gains and innovation acceleration. This report is key for

contextualizing India’s FinTech ecosystem. The India Brand Equity Foundation (2024) summarized growth and digitalization

trends in Indian banking. It outlines UPI adoption, branch digitization, and government incentives. The data can serve as

contextual background for explaining FinTech intensity across NSE-listed banks. Thakur (2023) analyzed ICT spending and

profitability in Indian banks using a nonlinear regression model. The study found diminishing returns to IT investment until a

threshold level is reached, after which profitability rises. It advocates for testing quadratic FinTech terms in bank-performance

models. Ovenc (2025) investigated how FinTech partnerships impact bank performance in developed markets. Results revealed

increased revenues and reduced operational costs from collaborations but temporary profit declines during implementation. The

study underlines the importance of measuring partnership variables. Economic Times (2025) reported growing risks associated

with FinTech-driven personal loans in India. Rising delinquencies indicate that technology adoption can amplify credit risk

exposure. These findings highlight the need to incorporate asset-quality controls such as NPL ratios in performance models.

Reuters (2024) documented RBI’s supervisory actions on digital banking, such as the Kotak Mahindra Bank case. It

demonstrates that regulatory actions and IT governance influence FinTech expansion. This source offers real-world evidence of

compliance and risk management shaping FinTech performance outcomes. Comparative studies (2021–2024) compared digital-

first and conventional banks worldwide. These studies consistently found digital-first banks demonstrate higher efficiency but

mixed profitability depending on funding models. This evidence suggests segmenting NSE banks by digital maturity for detailed

insights. Methodological guidance papers emphasize using difference-in-differences (DiD) and staggered adoption designs.

These allow causal estimation of FinTech impacts when banks implement new platforms at different times. The approach is

directly applicable to Indian banks with phased digital rollouts. Recent systematic reviews (2024–2025) synthesize evidence on

FinTech’s positive effect on efficiency and inclusion while noting risks from cyber threats and operational disruptions. These

reviews call for India-specific causal research. Your study directly contributes by examining listed banks under NSE.

INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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Research Gap

This research aims to addresses a critical gap in the literature in understanding the impact of FinTech adoption on banks in an

emerging economy context. The focus of this study is on the Indian banking industry, including public as well as private sector

banks, and analyses their performance across pre- and post-COVID periods.

Conceptual Framework:

Research Objectives:

1. To Identify the factors that affects financial technology and financial performance of banks.

2. To examine the impact of financial technology on financial performance of selected banks.

3. To suggest the relevance of financial technologies in the business process and risk management.

III. Research Methodology:

Research Methodology is the methodical approach to addressing a research problem is known as research methodology. It covers

the strategies, processes, and tactics needed to find, gather, examine, and interpret data to draw trustworthy conclusions. Applying

systematic methods and techniques to assess data and accurately accomplish the study's goal is the aim of research methodology.

Research Design: - Quantitative and Descriptive Method

Sample: - Selected Indian public and private banks Sample size: - 32 banks

Sampling Technique: - Non-Probability Sampling (Purposive Sampling)

Data Collection Method: Data is collected from selected bank’s annual report and RBI website for dependent and independent

variable, and World Bank Data used to collect control(macroeconomics) variable like GDP and Inflation Rate.

Data Analysis Tool: - Descriptive Statistics, Correlation and Regression Analysis.

Period of the study: - 2015-2016 to 2024-2025

Limitation of the Study:

This study is limited by its dependency on secondary data, which may be subject to reporting delays, revisions, or inconsistencies

across different sources. These results may not accurately portray the whole banking sector because the scope is limited to a few

Indian public and private sector banks. The study ignores other digital breakthroughs like UPI, wallets, and blockchain-based

services in favor of concentrating just on transaction-based fintech adoption, which includes mobile banking, debit card, and

credit card usage. Furthermore, macroeconomic indicators like GDP and inflation rate. are taken into consideration as control

Dependent Variable

1. Size

2. GDP

3. Inflation Rate

Control Variable

Bank’s Performance Indicators

1. Capital Adequacy

2. Asset Quality

3. Earning

4. Liquidity

5. Financial Stability

Independent Variable

FinTech Indicators

1. Mobile Banking

2. Credit Card

3. Debit Card

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variables, but other outside impacts like shifts in policy or the state of the world economy, are not taken into consideration. As the

study period spans 2015–2016 to 2024–2025, covering pre-, during, and post-COVID phases, the unusual effects of the pandemic

may also influence results in ways that are difficult to isolate.

Hypothesis:

Null Hypothesis (H₀):

There is no significant impact of financial technology (FinTech) adoption on the financial performance of selected banks listed in

the NSE, India.

Alternative Hypothesis (H₁):

There is a significant impact of financial technology (FinTech) adoption on the financial performance of selected banks listed in

the NSE, India.

Analysis and Interpretation:

This investigation deals chiefly with analysis of data and interpretation. of results to evaluate the impact of financial technology

adoption on the effectiveness of chosen Indian banks which are listed in NES, India during the period 2015–2016 to 2024–2025.

The analysis focusing on the dependent variables: CA, AQ, Earnings, Liquidity, and Financial Stability. The independent

variables are Mobile Banking Transactions, Debit Card Transactions, and Credit Card Transactions, while Bank’s Asset, Inflation

Rate, and GDP are control variables to eliminate the influence of size and macroeconomic fluctuations.

Table 6.1: -Table Showing Descriptive Statistics:

Minimum

Maximum

Mean

Std. Deviation

320

7.51

32.71

15.7943

3.92109

320

0.00

17.00

2.8141

2.88859

Earnings

320

-6.36

3.67

0.5083

1.13165

Liquidity

320

0.469876

5.560184

0.78402194

0.302492433

Financial Stability

320

-2.7560009

41.7669840

2.785914065

6.8013791480

Mobile Banking

320

0.00000000

81456799458

3561910458

8713956384

Credit Card

320

0.0000000

5724676253

161031928

676282512

Debit Card

320

184.8000000

13181622509

361438708.

1312320915

Size

320

11798.89

6676053.27

555337.6039

903345.71456

GDP

320

-5.8

9.7

5.607

4.4761

320

3.30

6.70

5.1450

1.18262

Interpretation of Descriptive Statistics

From 320 observations, the results show that banks had a good level of capital adequacy on average of 15.79, meaning they kept

enough capital to cover risks. Asset quality was moderate, with an NPA ratio of 2.81, while earnings were quite low at 0.50, and

some banks even showed losses. Liquidity was steady at 0.78, but financial stability (Z-score) differed a lot across banks,

showing that some were showed strong stability, in contrast to others that were weaker. In terms of fintech use, mobile banking

transactions grew the most and showed huge variation, while credit card and debit card transactions also differed widely between

banks. Bank size varied a lot too, with very large public sector banks and much smaller private banks. For the economy, GDP

growth averaged 5.6% but included both strong growth years and some negative years, while inflation stayed stable around 5%.

Overall, the numbers suggest that banks were stable in traditional measures, but fintech adoption grew quickly and unevenly.

Table 6.2: - Table Showing Correlations Analysis:

Earni

ngs

Liqui

dity

Financia

Stability

Mobile

Banking

Credit

Card

Debit

Card

Size

GDP

Pearson

Correlat

ion

.582

.663

.252

.125

.107

.090

.053

-.046

.145

.318

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Sig. (2-

tailed)

.000

.026

.055

.106

.347

.417

.010

.000

320

Pearson

Correlat

ion

.582

.696

.204

-.208

-.258

-.193

-.196

-.109

-.080

-.363

Sig. (2-

tailed)

.000

.001

.000

.052

.154

.000

320

Earnings

Pearson

Correlat

ion

.663

.696

.137

.279

.234

.213

.152

.099

.165

.150

Sig. (2-

tailed)

.000

.014

.000

.006

.076

.003

.007

320

Liquidity

Pearson

Correlat

ion

.252

.204

.137

.096

.021

.065

-.003

-.023

-.009

.008

Sig. (2-

tailed)

.000

.014

.085

.702

.248

.961

.681

.874

.891

320

Financial

Stability

Pearson

Correlat

ion

.125

.208

.279

.096

.189

.331

.072

.198

-.037

-.061

Sig. (2-

tailed)

.026

.000

.085

.001

.000

.197

.000

.514

.279

320

Mobile

Banking

Pearson

Correlat

ion

.107

.258

.234

.021

.189

.805

.882

.847

.095

.217

Sig. (2-

tailed)

.055

.000

.702

.001

.000

.091

.000

320

Credit Card

Pearson

Correlat

ion

.090

.193*

.213*

.065

.331**

.805**

.673**

.585

.079

.135*

Sig. (2-

tailed)

.106

.001

.000

.248

.000

.159

.016

320

Debit Card

Pearson

Correlat

ion

.053

.196*

.152*

-.003

.072

.882**

.673**

.732

.107

.144*

Sig. (2-

tailed)

.347

.000

.006

.961

.197

.000

.057

.010

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320

Size

Pearson

Correlat

ion

-.046

-.109

.099

-.023

.198**

.847**

.585**

.732**

.057

.127*

Sig. (2-

tailed)

.417

.052

.076

.681

.000

.312

.023

320

GDP

Pearson

Correlat

ion

.145

-.080

.165*

-.009

-.037

.095

.079

.107

.057

-.124*

Sig. (2-

tailed)

.010

.154

.003

.874

.514

.091

.159

.057

.312

.027

320

Pearson

Correlat

ion

.318

.363*

.150*

.008

-.061

.217**

.135*

.144*

.127

.124

Sig. (2-

tailed)

.000

.007

.891

.279

.000

.016

.010

.023

.027

320

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Interpretation of Correlation Analysis

The correlation analysis shows that capital adequacy (CA) has a strong positive relationship with earnings (r= 0.663), suggesting

that banks with higher capital buffers generate better returns, while its link with asset quality (r = –0.582) is strongly negative,

meaning higher NPAs reduce capital strength. Earnings are also negatively correlated with asset quality (r = –0.695), confirming

that poor loan performance reduces profitability. Relationships of liquidity with CA (r = 0.252) and earnings (r = 0.137) are weak,

while stability shows small positive associations with CA (r = 0.124) and earnings (r = 0.278). Among fintech indicators, mobile

banking correlates positively with earnings (r = 0.234) and stability (r = 0.189); credit cards with stability (r = 0.331) and earnings

(r = 0.212); and debit cards with earnings (r = 0.152) and stability (r = 0.0724). All three fintech indicators are strongly related to

one another (mobile–debit r = 0.882; mobile–credit r = 0.804; debit–credit r = 0.672), showing that digital payment channels tend

to expand together. For control variables, GDP growth has weak but positive links with CA (r = 0.144) and earnings (r = 0.165),

while inflation rate shows a positive correlation with CA (r = 0.318) and earnings (r = 0.150), but a negative one with asset

quality (r = –0.363). These results suggest that fintech adoption and macroeconomic factors both contribute to banking

performance, with mobile and card-based transactions particularly supporting profitability and stability, while capital adequacy

and asset quality remain crucial in explaining overall performance.

Table 6.3: - Summary All Regression Analysis Model

Variable

Model 1 CA

Model 2 AQ

Model 3 Earnings

Model 4 Liquidity

Model 5 Stability

Mobile Banking

0.000***

0.513

0.373

Credit Card

0.260

0.175

0.611

0.374

0.000***

Debit Card

0.080

0.228

0.017***

0.455

0.003***

Size

0.000***

0.001***

0.281

0.007***

GDP

0.001***

0.065

0.004***

0.819

0.286

0.000***

0.103

0.908

0.088

Interpretation of Summary Table

The above regression analysis table indicates that mobile banking has a highly significant impact on capital adequacy, asset

quality, and earnings, but no effect on liquidity or stability. Credit cards do not influence most models but are highly significant

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for financial stability, while debit cards significantly affect earnings and stability. Bank size consistently plays a strong role in

explaining capital adequacy, asset quality, earnings, and stability, but not liquidity. GDP growth significantly drives capital

adequacy and earnings, while interest rates strongly affect capital adequacy and asset quality, with only a weak influence on

stability. Interestingly, liquidity is not significantly explained by any of the variables, suggesting other factors may play a more

dominant role. Overall, the findings highlight that digital channels (mobile, credit, and debit cards), along with macroeconomic

factors (GDP and interest rate), are crucial for bank performance dimensions other than liquidity.

Table 6.4: - Decision of Hypothesis Testing:

Sl. No.

Hypothesis

Decision

There is no significant impact of financial technology (FinTech) adoption on the

financial performance of selected banks listed in the NSE, India.

Reject

There is a significant impact of financial technology (FinTech) adoption on the

financial performance of selected banks listed in the NSE, India.

IV. Findings, Suggestions and Conclusions

Findings

This research evaluated. the influence of fintech adoption on the performance of selected Indian banks listed. in the NSE, covering

the period 2015–2016 to 2024–2025. The analysis was based on five dependent variables representing bank performance—CA, AQ,

Earnings (financial performance), Liquidity, and Financial Stability. To capture fintech adoption, three independent variables

were considered: Mobile Banking transactions, Debit Card transactions, and Credit Card transactions. In addition, 3 control

variables were included to account for macroeconomic and institutional effects: Asset Size (total assets), GDP growth rate, and

Interest Rate (IR %). The results reveal that fintech adoption has had a varied and multidimensional influence on the performance,

of Indian banks. Mobile banking emerges as the most influential driver, as it significantly improves capital adequacy and earnings

by generating fee-based income, enhancing efficiency, and reducing operational costs. However, it also has a negative influence on

asset quality, pointing to challenges such as greater exposure to defaults, digital lending risks, and potential cyber vulnerabilities.

Debit card usage, although widely adopted, negatively affects profitability and financial stability due to its low margins and

limited revenue potential. In contrast, credit card usage, while not significantly influencing capital adequacy or earnings,

contributes positively and significantly to financial stability, underlining its importance as a fee-generating instrument that

diversifies bank revenues.

Additionally, the study shows that bank size has a significant impact. Larger banks exhibit stronger asset quality and financial

stability, largely due to better governance, diversified portfolios, and more advanced risk-management systems. At the same time,

they display weaker capital adequacy and earnings efficiency, reflecting diseconomies of scale, structural inefficiencies, and

higher administrative costs. From a macroeconomic perspective, GDP growth positively supports earnings and capital adequacy,

indicating that banks benefit during periods of economic expansion through higher credit demand and investment activity.

Alternatively, an increase in interest rates reduces asset quality and financial stability, since borrowers face repayment stress and

banks are exposed to greater credit risks. Liquidity, however, remains the only performance dimension unaffected by fintech

adoption or macroeconomic variables, demonstrating that liquidity management is shaped more by regulatory requirements

(CRR, SLR) and treasury operations than by transaction-based fintech innovations.

In summary, the findings confirm that fintech adoption enhances efficiency, competitiveness, and profitability in the Indian banking

sector, particularly through mobile banking and credit card, operations. Nonetheless, it gives rise to new vulnerabilities that

weaken asset quality and long-term stability. These results highlight the need. for banks to balance technological adoption with

effective risk management frameworks and supportive regulatory oversight.

Suggestions:

Strengthen digital risk management by adopting AI-driven credit scoring, predictive analytics, and real- time monitoring for

mobile banking.

Improve debit card profitability through value-added services, innovative fee models, and merchant partnerships.

Expand credit card adoption responsibly under strong risk frameworks to ensure stability without raising default levels.

Address inefficiencies in large banks by modernizing legacy systems, cutting administrative costs, and automating operations.

Adopt interest rate hedging strategies, including stress testing and buffer capital, to dampen the effects of fluctuating rates.

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Focus on liquidity resilience by enhancing treasury management, maintaining statutory reserves, and diversifying funding

sources.

Enhance financial inclusion with safeguards by linking digital adoption to financial literacy and awareness programs.

Strengthen regulatory frameworks with stricter cybersecurity norms, consumer protection policies, and capital adequacy rule.

V. Conclusion:

The study concludes that fintech adoption considerably influences the performance of Indian banks. By analyzing 32 banks across

public and private sectors over ten years, it was found that fintech tools such as mobile banking, credit cards, and debit cards

affect different performance indicators in distinct ways. Mobile banking and GDP growth support stronger capital adequacy and

higher earnings, while larger asset size and rising interest rates weaken banks’ capital positions.

In capital adequacy, the findings. indicate that mobile banking, and GDP growth strengthen banks’ capital positions by improving

efficiency and supporting stronger buffers. However, larger bank size and higher interest rates weaken capital adequacy, showing

that growth without efficiency can put pressure on resources. This highlights that while fintech adoption has the potential to

support stronger capital strength, other structural and macroeconomic factors also play a critical role.

For asset quality, Results suggest that mobile banking and elevated interest rates adversely affect loan performance, reflecting

risks of defaults and repayment challenges in a digital environment. On the other hand, larger banks perform better in maintaining

asset quality because of diversification and stronger monitoring systems. This suggests that fintech adoption brings benefits but

also increases risks that need to be managed carefully to protect credit quality.

When it comes to earnings, mobile banking and GDP growth significantly improve profitability, underlining the efficiency gains

and income opportunities created by digital transactions. However, debit card usage and larger asset size reduce profitability,

reflecting the low margins of debit card services and inefficiencies in large institutions. Credit card usage not significantly boost

earnings but plays a positive role in financial stability.

Liquidity and stability present mixed results. Liquidity is found to be largely unaffected by fintech adoption, showing its

dependence on regulatory norms and treasury operations rather than transaction-based innovations. For stability, credit card

usage, and larger bank size enhance resilience, while debit card usage, and high interest rates weaken it. Overall, fintech adoption

acts as a double-edged sword: it improves profitability, capital strength, and competitiveness but simultaneously introduces risks

to asset quality and stability. The conclusion is that fintech can deliver sustainable benefits only when combined with sound

governance, robust risk management, and supportive regulatory oversight.

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