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American Journal of  Economics and 
Business Innovation (AJEBI)

Opportunities and Vulnerabilities of  AI in Automating Compliance and Regulatory 
Reporting in the Banking Sector in Bangladesh

Md Maruf  Hossain1*, Md. Ashraful Alam2, Sk Md Zafar Iqbal3 

Volume 4 Issue 3, Year 2025
ISSN: 2831-5588 (Online), 2832-4862 (Print)

DOI: https://doi.org/10.54536/ajebi.v4i3.5586
https://journals.e-palli.com/home/index.php/ajebi

Article Information ABSTRACT

Received: August 02, 2025

Accepted: September 04, 2025

Published: November 11, 2025

This research investigated the prospects and threats of  Artificial Intelligence (AI) in 
automating compliance and regulatory reporting for banks in Bangladesh. The study aimed 
to determine the current status of  AI implementation, explore its benefits and risks, and 
evaluate organizational readiness for its adoption. A structured questionnaire was used and 
120 respondents from banking professionals participated in this study, and 28% of  them 
indicated that they have adopted or partially adopted AI tools for their compliance purposes. 
Key findings included the observation that 78% of  respondents said they thought AI would 
reduce reporting time, and 72% agreed it would improve data accuracy, and that 81% were 
concerned about data security and 64% about transparency. Institutional readiness, perceived 
benefits and risk knowledge were evaluated using descriptive statistics and thematic analysis. 
There were several limitations of  the study, including recall and reporting bias due to the 
self-report nature of  the data, and the cross-sectional design allowing only limited cause and 
effect ascription. The implications suggests that banks and regulators need to work together 
to invest to develop necessary infrastructure, training and governance frameworks, in order 
to integrate AI in compliance system responsibly and efficiently.

Keywords
Artificial Intelligence, Compliance 
Automation,  Institutional 
Readiness, Opportunities and 
Threats of  AI, Regulatory 
Requirements

1 METSELL, Chittagong, Bangladesh
2  Division of  Trade Operations, Shimanto Bank PLC, Dhaka, Bangladesh
3   Department of  Economics, Galgotias University, Delhi, India
* Corresponding author’s e-mail: maruf@metsell.com

INTRODUCTION
In today’s changing world of  international finance, 
compliance and regulatory reporting has never been more 
intricate, data heavy, and held to such scrutiny by regulators. 
For banks, no matter in developed or developing countries 
(like Bangladesh) the timely and accurate compliance with 
the national and international regulatory requirements are 
vital for their strategic concern (Islam & Sadekin, 2020). 
Traditional approaches to compliance, often dependent 
on manual logging, reporting and monitoring, are no 
longer able to cope with the increasingly high volume 
and velocity of  regulatory demands (Dhawan, 2024). 
As a result, banks and other financial institutions have 
turned to new technologies, such as Artificial Intelligence 
(AI), that can be used to automate and supplement 
existing systems (Alhajeri & Alhashem, 2023). Artificial 
Intelligence, consisting of  machine learning, natural 
language processing and robotic process automation has 
become a disruptive force to enhance efficiency, accuracy 
and real-time enforcement in compliance (Robles & 
Mallinson, 2023). In sophisticated banking systems, AI is 
used to identify fraudulent transactions, analyze customer 
data for AML and KYC compliance, track suspicious 
behavior, and produce virtually all regulatory reports 
without human interference (Navya Vemuri & Kamala 
Venigandla, 2022). Such applications create real time data 
management. They minimize the human error factor and 
allow predictive analytics and dynamic risk assessment 
to be performed, in doing so, compliance becomes 
proactive rather than reactive (Lehto et al., 2021). In 
Bangladesh, the banking sector transformation has 
entered a digital era, pushed by policy changes, financial 

inclusion objectives, and intense regulatory pressure, such 
as the role played by the Bangladesh Bank (Malasriganga,. 
But AI in regulatory compliance is far from widespread 
and piecemeal at best. Some banks have already begun 
to apply automation tools, but more can be done when 
it comes to the wider adoption of  AI (Hu & Wu, 2023). 
Moreover, AI raises new risks such as data security, lack 
of  transparency of  algorithms and concern about people 
placing too much trust in error-prone systems without 
enough human supervision (Saberi, 2022). Lack of  clear 
AI in compliance regulatory environment also makes 
adoption and risk management more challenging.
The present study attempts to fill this gap by using the 
technology acceptance and risk governance theory to 
investigate how the institutional readiness, perceived 
usefulness, and perceived risks affect the adoption of  
artificial intelligence in compliance-related jobs. These 
theoretical lenses assist in framing organizational 
actions in the adoption of  nascent technologies in 
regulated settings. The research aims to: (1) to examine 
the current knowledge use and adoption of  AI in the 
realm of  compliance and regulatory reporting of  the 
Banks in Bangladesh; (2) to ascertain how AI relies 
on enhancing efficiency and accuracy; (3) to pinpoint 
different institutional challenges and risks associated with 
data security, less transparency in AI implementation 
and lack of  regulatory clarity; and (4) to ascertain the 
overall readiness of  the industry to implement AI-based 
compliance systems.
By framing the research in the context of  the particular 
risks and opportunities confronting the Bangladeshi 
banking industry, this study both extends academic 



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and policy conversations regarding the governance 
of  AI in emerging financial systems, and provides 
practical learnings for financial institutions, regulators, 
and technology suppliers aspiring to innovate while 
maintaining regulatory fidelity.

LITERATURE REVIEW
Artificial Intelligence (AI) is increasingly playing a role 
in financial compliance and regulatory reporting lately, 
and this is not surprising given the space of  digital 
transformation that the banking industry is heading 
towards globally (Temelkov, 2023). Compliance processes 
are being optimized and operational risk mitigated, as 
AI technologies, such as ML, NLP, RPA, and predictive 
analytics, are employed to improve the quality of  
regulatory reports themselves (Liang, 2024). This 
paper aims to review the recent academic and industry 
publications (mostly dated since 2018) to understand 
the opportunities and challenges of  AI in regulatory 
compliance that are specific in the context of  developing 
countries such as Bangladesh. Multiple reports have 
shown how AI can greatly enhance the efficiency in 
compliance work. According to Arner et al. (2019) show 
that, RegTech regulatory technology using AI can reduce 
compliance costs by 50%, by automating routine tasks 
such as aggregation of  data, transaction monitoring, 
and report production (Tillu et al., 2023). Likewise, the 
World Economic Forum (2020) report listed the use of  
AI to generate regulatory submissions within a banking 
setting as enabling a faster, more accurate generation 
process, lower levels of  manual intervention required 
and decreased human error risk (Patial, 2023). (Martinez 
et al., 2024) also highlighted that AI facilitates early risk 
detection through real-time transactional monitoring and 
identification of  outliers, which can prevent financial 
institutions from entering into non-compliance. AI has 
also held promise in Anti-Money Laundering (AML) and 
Know Your Customer (KYC) compliance (Karangara et 
al., 2024). A study of  (S M Shahariar Rafi et al., 2024) 
found that AML systems using AI can enhance detection 
of  transactions that are suspicious, and reduce false 
positives by as much as 30%. This is corroborated by 
a report by (Hrabariev et al., 2024) which pointed out 
that AI-based solutions enable financial institutions to 
simplify customer onboarding, improve due diligence, 
and ensure continued oversight of  high-risk customers. 
These are lifesaving features in high-risk situations, where 
a manual check may easily be overlooked or delayed. 
Despite the potential benefits, however, recent work 
has also highlighted potential vulnerabilities and ethical 
considerations (Feng, 2024). (Papadimitriou, 2023) and 
(Dunsin et al., 2023) posed the challenge of  algorithmic 
opacity, as many AI models (and particularly deep learning) 
are “black box” systems, so that compliance officers 
and regulators cannot know how a decision is being 
reached. Accardo Software If  there’s no transparency 
here, how could you make transparency accountability 
in an audit-heavy and accountable industry? Moreover, 

(Khan et al., 2024) study cautioned about the cyber risks 
of  AI systems like hacking into data, adversarial attacks 
and damage of  vulnerabilities caused by third-party 
AI vendors. Regulatory uncertainty is also a common 
thread in recent scholarship. Meanwhile, international 
regulators like the Financial Conduct Authority (FCA) 
and the European Banking Authority (EBA) have started 
to publish guidance on AI governance, but emerging 
markets remain without joined-up frameworks (Erdélyi 
& Goldsmith, 2020). A single case study on the South 
Asian banking industry by (Tinnirello, 2022) pointed 
out that the lack of  a defined AI guidelines along with 
the lack of  technical expertise and data infrastructure 
impedes a safe, proficient AI implementation. Very few 
empirical studies have been done in Bangladesh, but 
industry reports indicate that a majority of  banks are in 
the early phase of  experimentation and that full-fledged 
AI deployment is still low due to cost, risk, and regulatory 
ambiguity (Schmitt, 2022). The role of  human judgment 
in AI based compliance systems is also emphasized in 
literature. A research by (Enarsson et al., 2022) that hybrid 
algorithms/ machine into humans that is more effective 
in applications where compliance is an important issue, 
than algorithms that operate entirely on their own. This 
contributes to the case that AI should be additive, rather 
than substitutive, to human expertise particularly in 
sensitive areas like regulatory interpretation and ethical 
judgment (Licato, 2021). Complementary recent literature 
accepts that AI presents noticeable opportunities for 
automating the compliance and regulatory reporting, and 
in an efficient, accurate and scalable manner (Munivel 
Devan et al., 2024). However, problems of  issues 
such as transparency, security, ethics and institutional 
preparedness are enormous, especially in developing 
countries (Pangaribuan, 2019). Evidence appears to be 
calling for further empirical investigation into country-
specific contexts, like those found in Bangladesh, where 
degree of  digital maturity of  financial institutions vary 
and regulatory supervision is still emerging (Amin, 2024). 
This gap is something that this study seeks to address as it 
investigates how AI in compliance is being practiced, and 
what its benefits and risks are within the context of  the 
Bangladeshi banking sector.
This research was directed by a number of  research 
questions to understand the present and potential benefits 
and drawbacks of  using AI for automating regulatory 
reporting and compliance in the banking sector in 
Bangladesh. The research aimed to answer: what is the 
current level of  knowledge and adoption of  AI in the 
compliance functions of  the banks in Bangladesh; what 
are the unique opportunities AI offers for improving 
effectiveness, accuracy and speed of  reporting for 
compliance purposes; what are the perceived weaknesses 
including ethical, technical and regulatory obstacles in 
integrating AI; and finally, how ready are the banks in 
terms of  infrastructure, human resources, and strategic 
orientation to develop and implement AI enabled 
compliance systems safely and effectively. The goals of  



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this study are: 
1. To find out how many Bangladeshi banks are now 

using AI for compliance and regulatory reporting. 
2. To find out what AI can do to make regulatory 

processes easier. 
3. To look into what people think are AI’s weaknesses 

and what institutions are worried about when it comes 
to compliance. 
4. To see if  institutions and regulators are ready to use 

AI-powered compliance systems.

MATERIALS AND METHODS
The research was designed as a quantitative, cross-
sectional study, sample size was calculated as per the 
formula: 

Where Z was 1.96 (for 95% confidence level) 16; p was 
0.5 (the expected proportion); and E was 0.09 (the margin 
of  error). According to this calculation, the minimum 
sample size was 119 participants. 120 bank employees 
were approached through a formal questionnaire. 
Consecutive sampling method was used to recruit 
individuals responsible for compliance, audit, risk, IT 
and regulatory reporting in banks. It was based on the 
literature and expert studies, and consisted of  close-ended 
Likert-scale questions and some open-ended items. The 
instrument was found to be reliable with a Cronbach’s 
alpha of  0.82. Online and face to face data were collected 
over six weeks and subject to descriptive statistics, chi 
square tests for-association and thematic analysis for the 
qualitative responses.

RESULTS AND DISCUSSION
Findings Section This section presents the results 
of  study based on analyzed the responses obtained 
through standard questionnaire. It critically analyses the 
present conditions of  adoption of  AI in compliance 
and regulatory reporting, identifies the perceived 
opportunities and benefits, and recognizes the most 
vulnerable and potential risks, and also checks the overall 
preparedness of  banks in Bangladesh to adopt AI based 
compliance systems. The paper discusses these findings in 
light of  prior literature and the contextual realities of  the 
Bangladeshi banking sector, thus providing an in-depth 
explanation of  the potential and the limitations of  the 
use of  AI in the automation of  regulatory activities. The 
methodological limitations are also recognized by us, such 
as self-reported data and cross-sectional design, especially 
when we interpret significance tests, and generalization. 
This results and discussion section seeks to summarize 
and interpret empirical trends and to derive substantive 
implications for theory, practice, and policy in responsible 
AI deployment for financial regulation.

Demographic and Professional Profile
The two sections on the demographic and professional 

background of  the respondents gave useful context about 
the participants’ background and their interest related to 
the research topic itself.

Figure 1: Demographic Information of  the Respondents.

In the figure 1, of  the 120 respondents, a large share 
(42%) were employed by private commercial banks, 
and 30% were from state-owned commercial banks, 
18% foreign commercial banks, and 10% specialized 
banks (Mickey & Yanhaona, 2024). This distribution of  
respondents resulted in equal participation from various 
categories of  banks operating in Bangladesh. Men were 
compliance officers (47) and IT/Data professionals (38) 
with the smaller amounts of  other professionals like risk 
managers, audit and senior management responsible 
for regulatory reporting. For experience, 37% had >10 
years of  experience in banking, 29% had 6–10 years of  
experience, 21% had 3–5 years of  experience, and 13% 
had <3 years of  experience, so most had a certain level of  
industry knowledge. Interestingly, 64% of  the participants 
said that they were involved in compliance and regulatory 
reporting running direct, while 26% through an indirect 
process, and 10% had some sort of  vague general 



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knowledge. The demography statistics indicate that the 
perceptions extracted in the research were derived from 
the knowledgeable individuals who hold real time working 
experiences of  compliance exercises, hence robustness 
and the representation of  the scenario on the ground 
prevalent in the banks of  Bangladesh.

Current Practices in Compliance and Regulatory 
Reporting
The observation indicated that maximum banks operating 
in Bangladesh are still depended on manual or semi-
automated method.

The findings of  this study indicated that a traditional, 
manual or semi-automated approach still predominates 
compliance and regulatory reporting in those banks 
based in Bangladesh, where 68.3% of  participants 
indicated a reliance on spreadsheet and legacy systems, 
and only 14.2% were equipped with fully automated 
reporting system. Chi-square test of  independence 
indicated a significant association between the bank type 
i.e public versus private and the automation level int 
the compliance reporting (χ² = 11.47, p < 0.01) Private 
banks are perceived to be relatively advanced in adopting 
technology tools. These results are consistent with (Huda 
et al., 2020) commented that digital transformation of  
compliance in South Asian banking is still relatively 
asymmetric and contingent primarily on institutional 
capacity and leadership commitment. Additionally, 
interviewees shared the most significant challenges 
with current processes such as long time consumption 
(57.5%), potential for human error (52.5%), and the 
inability to aggregate information from various systems 

(49.2%). These inefficiencies are indicative of  structural 
bottlenecks to timely compliance costs but are consistent 
with the issues raised by (Basel Committee, 2021) relating 
to delinquency of  compliance dividends and risks in 
under-automated banking systems. Furthermore the 
proliferation of  out-dated methods also strengthens a 
required assumption made by our theoretical framing, the 
idea that even though the technology is present, without 
perceived institutional readiness (as per Risk Governance 
Theory) then the motivation to adopt AI will be low. 
Although insightful, these baseline findings must be taken 
with caution as data are self-reported and might reflect 
perceived but not objectively measured compliance 
performance, albeit this is reported as well. 

Awareness and Adoption of  AI in Compliance 
Function
Findings on AI awareness and adoption in compliance 
functions indicated a growing interest among banking 
professionals in Bangladesh albeit cautiously.

Figure 2: Automation Level in Bangladesh Banking Sector.

Figure 3: AI Adoption of  Banking Sector in Bangladesh.



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Figure 4: Opportunities of  AI in Regulatory Reporting.

Figure 3 shows that, a moderate but growing awareness of  
AI applications in compliance among Bangladeshi banking 
professionals, with 61.7% of  respondents stating they 
were aware of  AI tools relevant to regulatory reporting, 
yet only 28.3% confirmed their institution had adopted 
any AI-based compliance technology to date. A chi-
square analysis revealed a significant relationship between 
respondents’ job roles and their level of  AI awareness 
(χ² = 9.88, p < 0.05), suggesting that professionals in IT 
and risk management departments demonstrated higher 
familiarity compared to those in general compliance or 
audit roles. This aligns with the Technology Acceptance 
Model (TAM), which suggests that perceived ease of  use 
and relevance, often influenced by technical exposure, play 
a vital role in technology adoption (Davis, 1989). Despite 
the low adoption rates, 75% of  respondents expressed a 
favorable attitude toward future implementation, citing 
AI’s potential to streamline reporting processes and 
improve data accuracy. These sentiments resonate with 
global trends highlighted by (Karangara et al., 2024), 

where institutions in digitally progressive regions reported 
increasing intent to adopt AI-driven regulatory technology 
(RegTech) for real-time monitoring and compliance 
automation. However, the gap between awareness and 
actual adoption reflects the structural and organizational 
inertia also reported in other emerging economies (Islam 
& Sadekin, 2020), where budgetary constraints, lack of  
technical expertise, and risk-averse culture impede rapid 
technological transitions. While the present study provides 
valuable stakeholder insights, it is important to recognize 
that the awareness data are perception-based and may 
not fully reflect institutional-level strategic planning or 
capability assessments.

Opportunities of  AI in Regulatory Reporting
Bangladeshi banking experts thought that using AI in 
regulatory reporting may lead to a number of  important 
opportunities, according to the study. A large majority of  
respondents felt that AI might make compliance-related 
tasks much faster, more accurate, and more efficient.

Figure 4 shows that the research presented in this paper 
explored a number of  important opportunities seen by 
the banking professionals in the context of  using AI for 
regulatory reporting. Over 3/4 (78.3%) felt that the time 
needed for preparing and filing regulatory reports could 
be reduced with AI, while 72.5% believed that AI benefits 
would include better data accuracy and consistency. And 
65.8% said AI could help to detect risks in real time 
and detect abnormal behaviours earlier, increasing the 
compliance efficiency. These findings are consistent 
with the Technology-Organization-Environment (TOE) 
framework that suggests that perceived technological 
benefits are significant antecedents of  innovation adoption. 
Qualitative analysis of  free text comments show nuanced 
hopes for AI around predictive analysis, automation of  
rule based tasks, and the joining of  more and disparate 

data sources to deliver compliance engagements. These 
results are consistent with (Khan et al., 2024) , which also 
notes that AI is providing financial institutions across 
the world with the ability to report under complicated 
standards, such as Basel III and IFRS 9 more rapidly than 
before. And 58.3% of  the respondents also believed AI’s 
capacity to accommodate regulatory changes and foster 
dynamic compliance could be a game changer, particularly 
with the growing regulatory gaze from the Bangladesh 
Bank and international watchdogs. But the advantages 
also rely on sufficient digital infrastructure, training for 
staff  and compatibility with current legal frameworks, 
which the respondents conceded. Although the results 
reflect optimistic attitude, this study based on perceptual 
data could not quantify the actual benefit attained, and as 
such, future studies should carry out longitudinal research 



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to yield robust animal impact assessment. 

Vulnerabilities and Concerns of  AI in Compliance
The results of  the study on the risks and problems with 

using AI for compliance showed that there are numerous 
major problems that are preventing the Bangladeshi 
banking sector from using it more widely.
The figure above represents that, Nevertheless, the 

Figure 5: Vulnerabilities and Concerns of  AI in Compliance.

research revealed significant unease among banking staff  
members around the risks AI opens up in the world 
of  compliance. A large chunk of  them about 69.2% 
were concerned about data privacy and security risks, 
particularly in relation to how AI is being used to process 
sensitive financial information. 61.7% were also worried 
about the opacity and unexplainability of  decision-making 
in AI, which echoes the “black box” problem commonly 
reported in the literature (Amin, 2024). A chi-square 
test result showed a statistically significant relationship 
between years of  professional experience and worry about 
algorithmic bias (χ² = 10.26, p < 0.05), indicating that the 
more experienced professionals were less trustful about 
AI-based solutions not breaching compliance norms. Such 
concerns are aligned with the Risk Governance Framework 
that underscores trust, accountability, and interpretability 
to operationalize complex technologies in regulatory 
settings. In addition, 54.2% of  respondents indicated risks 
arising from the lack of  particular regulatory framework 
related to the integration of  AI, as a systemic risk, from the 
same perspective of  findings by (Kaur, 2024), that argued 
that the rapid deploying of  AI without accompanying 
monitoring frameworks could increase risks related to 
compliance failure and brand reputation. Respondents 
were also concerned of  the risks of  overreliance on 
technology, job loss and ethical challenges, especially 
without robust human-AI framework models. These 
findings indicate that while AI offers efficiency gains, it is 
also riddled with vulnerabilities that, when unchecked, can 
have worse implications than its advantages particularly 
in a developing economy like Bangladesh, where legal, 
institutional, digital discipliners are embryonic.

Institutional Readiness and Future Outlook
The study of  institutional preparation and future 
perspective showed that while more and more banks in 
Bangladesh are interested in using AI for compliance, 
most of  them are not yet fully ready to do so.
The data above explains that, the results of  the 
institutional readiness analysis indicated that Bangladeshi 
banking officials had a cautious optimism toward the 
adoption of  AI in compliance, with a noticeable lack of  
infrastructural and strategic preparedness. Just 36.7% said 
their organizations have a specific digital transformation 
roadmap that clearly outlines its AI plan for regulatory 
reporting, and 47.5% said their business had begun 
building capability through staff  training or system 
upgrades. Chi-square test found a significant relationship 
between size of  firm (as measured by total assets) and 
intention to use AI (χ² = 12.41, p < 0.01), meaning that 
larger banks are more likely to invest in infrastructure and 
talent related to AI. These findings demonstrate the TOE 
frameworks’ basic assumptions, including the effects 
of  organizational resources and strategic attitudes on 
technology adoption. Limited technical capacity (58.3%) 
and regulatory uncertainty (51.7%) are among the key 
obstacles to full implementation, echoing findings from 
the Asian Development Bank’s 2023 research into digital 
finance in South Asia. Nevertheless, 71.7% of  survey 
participants thought that AI will become indispensable 
for compliance functions in the next five years, as it is 
going to be important to try to keep up with increasingly 
complex regulations and reporting requirements. The 
future vision also stressed the need for technology 
investment ‘as well as sector-specific guidelines, public 
and private partnerships and regulation alignment’. 



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Figure 6: AI Adoption in Bangladesh Banking Sector.

Nevertheless, this measure of  readiness is still based on 
self-report and may be biased by institutional optimism 
or bias, thereby highlighting the importance of  tracking 
actual preparedness longitudinally and to actively engage 
policy makers over actual preparedness.

Findings
The study showed that a lot of  bankers in Bangladesh 
know about AI in compliance and regulatory reporting, 
but not many of  them are really employing it. They 
mostly use it for easy things like checking for fraud and 
KYC/AML screening. Most banks still employ manual 
or partly automated systems, and just a few of  them have 
started using AI tools that are very sophisticated. People 
who answered said that AI might make compliance 
processes faster, more accurate, and able to keep an eye 
on threats in real time. But there are also huge challenges, 
like not having enough skilled individuals, not having 
clear AI decision-making, not having enough regulatory 
guidance, and data security risks. The institutions were 
only somewhat ready because they didn’t have enough 
training or infrastructure. But a lot of  banks said they 
were interested in putting money into AI technologies 
in the foreseeable future. The results reveal that more 
and more individuals are starting to see how AI could 
revolutionize the way compliance works. But to use 
technology responsibly and effectively, we will need to 
plan ahead, make rules that are easy to understand, and 
build up our skills.

Recommendations
The results show that banks in Bangladesh should 
introduce AI to their compliance and regulatory 
reporting systems in stages and with care. They should 
start with trial initiatives in areas that will have a big 
impact, like AML/KYC, fraud detection, and automating 
reports. Institutions should spend money on upgrading 
their technology and on training their employees in 
specialized areas so that they can appropriately manage 

AI technologies. It is crucial to cooperate with regulatory 
groups, and Bangladesh Bank should be in charge of  
making clear guidelines, promoting AI governance 
standards, and supporting efforts throughout the industry 
to use AI in a safe and ethical way. Banks should also 
make sure that personnel are keeping a careful eye on AI 
operations, that data privacy and cyber security are solid, 
and that they promote internal research and innovation 
to create AI solutions that meet their specific compliance 
needs.

CONCLUSION
The study’s conclusion is that Artificial Intelligence might 
greatly improve the accuracy, speed, and responsiveness 
of  compliance and regulatory reporting in Bangladesh’s 
banking sector. But it is still in the early phases of  being 
used due of  a variety of  problems with institutions, 
technology, and rules. The results suggest that even 
though banking professionals think AI could change 
things, its widespread usage is being held back by concerns 
about data security, algorithmic transparency, a lack of  
regulatory direction, and a lack of  institutional readiness. 
Things look positive for the future, even though there 
are some problems. A lot of  banks want to invest in AI 
solutions. To make sure that AI is used in compliance 
processes in a responsible, safe, and long-lasting fashion, 
banks, regulators, and technology vendors all need to 
work together. This will let them get the most out of  AI’s 
benefits while keeping its risks to a minimum.

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