Building Public Trust in Bahrain: Leveraging Artificial Intelligence to Combat Financial Fraud and Terrorist Financing Through Cryptocurrency Tracking

Abstract

This study assesses public trust in Bahrain regarding the potential of artificial intelligence (AI) to mitigate the use of cryptocurrencies in financial fraud and terrorist financing. The increasing risks associated with illicit financial activities have been exacerbated by the rapid expansion of e-commerce linked to cryptocurrencies, leading to vulnerabilities in financial technology systems. AI presents a viable solution for detecting, analyzing, and assessing the risks associated with cryptocurrency transactions, strengthening confidence in financial institutions’ regulatory measures. Evaluating public trust is crucial to understanding societal awareness of AI’s role in monitoring and regulating virtual financial transactions to prevent fraud. This research employs a quantitative approach to examine the key factors that enhance confidence in AI-driven auditing and oversight of cryptocurrency transfers. The findings indicate that, while AI offers significant advantages in combating financial crime, certain challenges remain. These include technological complexities, difficulties in accurately identifying users, and weaknesses in electronic financial and legal regulatory frameworks. Such challenges may undermine public trust in AI’s effectiveness in financial oversight. Addressing these concerns is essential to ensuring the successful integration of AI in financial regulation and reinforcing its role in enhancing security and transparency in cryptocurrency transactions.

1. Introduction

Innovations in new payment products and services and financial technology are on the rise in Bahrain, constituting a welcome development as they help consumers and businesses in many ways. Prepaid cards, electronic payments, mobile banking, mobile payment services, internet-based payment systems, and cryptocurrencies are just a few of the goods and services that come, however, with a plethora of drawbacks.1 Thus, new innovations in financial technology, such as cryptocurrencies, for example, are being used as a means of financing terrorism and money laundering. The risk of terrorist financing has risen with the emergence of rapid yet technologically complex payment systems that function across multiple countries with varying regulations on internet-based transactions.

Given the cross-border nature of these financial products and services, the use of artificial intelligence (AI) in tracking cryptocurrencies is required to reduce financial crimes. However, the use of AI in tracking cryptocurrencies faces many complex challenges related to legal aspects, financial regulation, and electronic infrastructure. These challenges undermine public confidence in all procedures and policies followed by financial entities. Therefore, this article attempts to measure public confidence in using AI to track digital currencies and its impact on reducing financial crimes and money laundering (Al-Harbi 2025).

A quantitative approach was used to analyze the public trust in Bahrain in the risks and challenges of using AI to track cryptocurrencies to reduce financial crimes. This research makes a significant contribution by exploring public perceptions of AI in tracking virtual currencies to combat money laundering and cybercrime, an area that has received limited attention in existing literature. While numerous studies examine technological transformations and their influence on digital currencies and financial crimes, they primarily focus on policymakers and private sector stakeholders. This study, however, shifts the spotlight to public trust in AI-driven financial tracking, offering both scientific and practical value. Just as a security system is only effective if people trust it to protect their homes, financial technologies must inspire confidence to fulfill their role in safeguarding economic activities. Understanding public trust in AI applications within financial systems provides insight into the effectiveness of policies implemented by both formal and informal financial institutions (Alrasheed et al. 2024). By evaluating this trust, this study sheds light on whether AI measures are perceived as reliable defenses against financial crimes or as potential risks, shaping the future of fintech adoption and regulatory frameworks.

2. Literature Review

Amidst developments in AI and cryptocurrencies, people believe regulatory authorities, financial institutions, and legal bodies should be directly responsible for the use of AI in cryptocurrency processing, as this helps to assess, identify, and mitigate the risks of combating the financing of terrorism and money laundering. In his study, Otubu (2024) found a positive inclination towards the application of AI in managing risks related to cryptocurrencies, with a particular emphasis on risk identification.

Considering the increasing number of virtual currency payment products and services that are fully online, financial institutions and law enforcement agencies should be able to use AI to verify the identity of their customers directly or indirectly using data or information held by documents or information contained in third-party databases.

Many people feel that a country’s financial regulations should require financial entities that offer virtual currency exchange services to use AI to audit and file reports related to suspicious transactions (FATF 2012). If the algorithms at currency exchange companies suspect that the funds are proceeds linked to terrorist financing or criminal activity, then the algorithms will report them to financial intelligence services.

It is widely acknowledged that using AI to monitor the activities of cryptocurrency exchange institutions can lower operational costs and reduce the time spent analyzing data to enhance efforts against terrorism financing and money laundering (Salami 2018). Digitizing the financial regulatory framework could help simplify regulatory complexity and reduce bureaucratic tasks (Balakrishnan 2024). This will contribute to the creation of global and comprehensive databases of companies, individuals, and groups identified as potential threats in these areas. Through data analysis and improved predictions, AI helps reduce this ambiguity in international security (Iqtait 2023).

2.1. AI, Crypto, and Global Security

Given the cross-border nature of cryptocurrency exchanges, terrorist financing and money laundering activities tend to be directed towards countries with weak regulatory systems. Therefore, all countries should have strong financial regulations and laws that legitimize the role of AI in tracking cryptocurrencies to combat money laundering and terrorist financing effectively and broadly (Agorbia-Atta et al. 2024).

However, there is a widespread view that many developing countries are considered fertile ground for financing terrorism and money laundering because they do not have strong financial and legal regulatory systems, which has led to an exacerbation of the level of terrorist activities worldwide (Valls-Prieto 2024). Developing unified global standards and strategies among countries to enhance the role of AI in tracking cryptocurrencies and confronting illegal activities will be difficult due to the degree of disparity between developed and developing countries in technological purification (FATF 2015).

Technological infrastructure plays a key role in enabling countries to use AI tools effectively. Countries with advanced infrastructure have robust data centers, high-speed communication networks, and advanced data analysis devices, enabling them to process huge amounts of information quickly and accurately (Udeh et al. 2024). In contrast, countries with limited capabilities may experience processing delays, inadequate system integration, and poor data storage, rendering the use of AI ineffective or prohibitively expensive (Agorbia-Atta et al. 2024)

At the same time, the use of AI technologies may increase the technological dependence of countries on foreign companies that develop these technologies. For example, many Arab states rely on AI tools and services provided by major technology companies, making their data and systems vulnerable to foreign influence (Jelinek 2023). If Arab states are unable to develop local solutions, they may lose control over their vital information, especially with the increasing complexity of decisions made by AI, raising concerns among the local population.

2.2. Complexity Dilemma and Privacy Risks

The complexity of algorithmic decision-making plays a crucial role in AI’s ability to track cryptocurrencies used in money laundering and terrorist financing. The more complex the algorithm, the better its ability to process big data and detect complex patterns (Wischmeyer 2020). However, increasing this complexity can lead to challenges that, in some cases, affect public confidence (Khaled et al. 2024).

The lack of transparency makes it difficult to explain how algorithms arrive at their decisions. In the context of cryptocurrency tracking, this can lead to unclear explanations about why a system categorized a particular transaction as suspicious (Otubu 2024). This lack of explanation can undermine trust between the public, regulators, and financial institutions, which require clear and understandable evidence to support any decisions they make. For example, if a financial account is frozen based on the recommendations of an AI system without sufficient explanation, the institution could face legal challenges and regulatory issues.

What is more, a lack of transparency can erode accountability. When errors or unforeseen results arise, it becomes challenging to determine responsibility if the decision-making process of the system is unclear (Pocher 2023). This presents a significant obstacle in enforcing laws or implementing corrective measures.

However, at the same time, the digital transformation witnessed by government and private institutions has contributed to enhancing transparency, promoting public trust, and achieving efficiency in administrative and financial operations. In this context, e-governance is one of the main drivers for achieving effective management, especially in the field of combating financial crimes such as money laundering (Bokhari and Myeong 2023).

Moreover, cryptocurrency financing has become a major challenge due to its decentralized nature and difficulty in tracking compared to traditional banking systems; tracking financing, however, raises concerns about the privacy of both individuals and institutions. Considering these challenges, e-governance powered by AI plays a pivotal role in enhancing the ability to prevent terrorist financing via cryptocurrencies/via technologies such as cryptocurrency (Campbell-Verduyn et al. 2021).

While tracking cryptocurrencies offers clear advantages, it also raises concerns about individual privacy. Analyzing transactions can result in the collection of sensitive data, such as spending patterns, geographic locations, and even personal connections. If these processes are not conducted with care and strict safeguards, these data could be misused, either illegally or for commercial purposes, leading to a breach of privacy and potentially eroding public trust in the policies and actions of involved stakeholders. On the other hand, there are individuals who turn to cryptocurrencies as a means of safeguarding their privacy from government scrutiny. Thus, the use of AI to monitor these currencies might be seen as an effort to limit personal freedom and undermine the fundamental purpose of cryptocurrencies (Gbadebo et al. 2024).

Meanwhile, cyberattacks represent a serious challenge to individual privacy and threaten the effectiveness of AI technologies designed for tracking cryptocurrencies. These attacks can disrupt operations and reduce the capabilities of such technologies. Cryptocurrencies, including digital currencies, are heavily reliant on blockchain networks and cloud computing systems, making them susceptible to cyberattacks that exploit system vulnerabilities to transfer funds illegally or hide their traces (George 2023).

Cyberattacks can target the underlying systems of AI technologies either by disrupting servers or stealing trained models. If these models are breached, they could be exploited for malicious purposes, such as amplifying hacking activities or misleading financial investigations. In addition, attacks can delay the tracking process and weaken the response of systems, giving attackers more time to hide their activities. Another challenge is that cyberattack techniques evolve in tandem with AI technologies (Jimmy 2021). Attackers use AI tools to carry out more sophisticated attacks, such as generating targeted phishing attacks or exploiting vulnerabilities, which, in turn, weaken public trust.

2.3. Public Trust in AI Regulation

While existing studies have explored the role of AI in financial oversight (Aksenova 2024; Khan et al. 2025), they have neglected the crucial aspects of public trust. There remains an important gap in comprehending how public support influences the use of AI-driven regulatory technologies, such as RegTech, in combating terrorist financing and money laundering. Particularly, previous research (Aksenova 2024; Khan et al. 2025) has not sufficiently analyzed public awareness regarding the benefits of AI in auditing cryptocurrency transactions or how this awareness enhances confidence in financial regulatory systems.

There has been little debate on whether the public perceives AI as a viable tool for tracking cryptocurrency transactions, especially in developing countries where weak financial policies create loopholes for illegal activities. The question remains whether AI can effectively function in regulatory environments with minimal oversight and whether these deficiencies impact public trust in AI-driven financial crime prevention.

Additionally, AI and technological disparities between countries have been acknowledged in the literature, yet their direct impact on tracking illicit transactions and public trust remains underexplored. The technological gap raises concerns about whether AI implementation can yield consistent outcomes across diverse economic structures. More importantly, there is a lack of inquiry into whether such technological disparities influence public confidence in AI as a regulatory tool.

While AI is widely recognized for its potential in detecting illicit activities (Aderibigbe et al. 2023; Khan et al. 2024), existing studies have not addressed how its integration into e-governance frameworks might strengthen its effectiveness and enhance public trust. The relationship between e-governance, public confidence, and AI in combating financial crimes remains an overlooked area of research, particularly regarding its impact on strengthening institutional credibility and fostering public trust in financial oversight mechanisms. Addressing these gaps is significant for developing a more comprehensive understanding of AI’s role in financial regulation and its broader societal implications, especially in Arab countries.

3. Unmasking Money Laundering: Challenges and Countermeasures in the Arab Region and Bahrain

Money laundering involves disguising or transforming illegally obtained funds to make them appear legitimate and serves as a primary method for terrorists to fund their activities. In the Arab world, many real-life stories have been linked to money laundering and terrorist financing, which have profoundly affected the stability of the Arab region (Alhejaili 2025).

3.1. Terrorist Financing and More Traditional Methods of Money Laundering

The Bank of Credit and Commerce International scandal is one of the most prominent stories that exposes the link between money laundering and terrorism. The bank was founded in the 1970s and targeted the Arab and Islamic markets, claiming to provide innovative banking solutions. However, in the 1980s, international authorities discovered the bank’s involvement in money laundering for suspicious entities, including terrorist organizations such as Al-Qaeda. The bank used a complex system of international branches to transfer illicit funds through countries in the region, such as Pakistan, to conflict zones.

In 2003, amid the turmoil in Iraq after the fall of the regime, vast money laundering networks emerged, exploiting the country’s financial collapse. Shady figures operating under front companies began smuggling money abroad using primitive methods such as transferring money through traditional remittances. Over time, the methods evolved to include front companies in Lebanon. The money fueled terrorist activities and supported armed groups operating in conflict zones in Arab countries (Alhejaili 2025).

Certain Arab states face significant challenges in combating money laundering and terrorist financing, due to poor transparency, lack of strict financial oversight, and regional conflicts (Alhejaili 2025). However, Arab countries such as Bahrain have begun to implement legal and banking reforms and to improve cooperation with international institutions to combat this phenomenon.

3.2. The Role of Cryptocurrency in Terrorist Financing and Money Laundering

The restrictions and regulations imposed on banks to combat money laundering and terrorist financing have made it difficult for terrorists to raise funds in many Arab states. As a result, terrorist components have looked for other sources of financial investment, namely, virtual platforms, as an alternative to financing their illegal activities. Two features that make the virtual financial environment attractive to terrorist groups are the digital world’s opacity (Haider and Akhtar 2024).

Despite the reduction in terrorist financing in the traditional financial sector of Arab states, the rise and growth of virtual financial transactions represent a difficult challenge for reducing terrorist financing and financial fraud. This stems from the fact that virtual financial transactions are characterized by a high degree of secrecy and privacy, creating a fertile environment for criminals to use cryptocurrencies to finance terrorism or employ them for financial fraud purposes without being noticed by regulatory authorities (Kapsis 2023).

Terrorist entities depend on hiding their identities, utilizing tools such as unidentified digital wallets or deregulated platforms. Phenomena such as extreme fraud and volatility determine the high risks that are interconnected with cryptocurrency transactions (Kapsis 2023; Kerr et al. 2023).

For example, in 2014, a controversial case came to light in the Arab region when financial authorities in Lebanon began tracking suspicious transactions using Bitcoin. At the time, cryptocurrencies were a new phenomenon in the Arab region, often associated with technology and innovation. However, it was soon revealed that a terrorist group was using Bitcoin to transfer money between countries in a covert manner, bypassing traditional banking systems and strict oversight. The case began when the Anti-Money Laundering Unit in Beirut noticed unusual financial transactions linked to cryptocurrency exchanges. Investigations revealed that these transfers came from various countries, including Iraq and Syria. After a deeper investigation, it was discovered that the funds were being used to finance the activities of a terrorist organization operating in the region.

In 2021, Moroccan authorities announced the discovery of an international money-smuggling network that used cryptocurrencies to transfer millions of dollars abroad. Investigations revealed that the network operated fraudulent trading platforms and utilized cryptocurrencies to conceal the origins of the funds.

These real-world examples show that cryptocurrencies present a dual challenge: they hold enormous potential to transform the economy, but, at the same time, they provide opportunities for illegal activity in Arab states. The future remains dependent on the ability of governments and institutions to develop effective regulatory systems that address these issues and protect the economy and society from growing risks (Al-Tawil 2023).

3.3. Related Work on Current Use of AI for Preventive and Detection Efforts

Despite efforts to track these activities, several Arab states, such as Bahrain, have faced significant challenges. Cryptocurrencies, such as Bitcoin, rely on blockchain technology, which allows all transactions to be recorded transparently, but the identities of the parties remain hidden. Terrorists exploit this feature to evade oversight and raise funds from their supporters around the world. Through people outside their main network, cryptocurrencies mislead legal authorities in several Arab states.

This being the case, Arab countries like Bahrain are starting to realize the seriousness of this matter. In 2019, the Central Bank of Bahrain issued the “Final Guidance Regarding Crypto-Assets and Crypto-Asset Platforms”, imposing strict restrictions on platforms that allow the trading of these currencies. Bahrain tightened controls on digital money transfers and banned a series of suspicious transactions. The country also began to embrace the fintech industry to improve financial systems and promote innovation.

According to the Central Bank of Bahrain (2019), the guidelines for crypto assets cover licensing requirements, governance, minimum capital, regulatory environment, risk management, anti-money laundering and counterterrorist financing, business principles and ethics, avoiding conflicts of interest, reporting, and cybersecurity. Moreover, in Bahrain, many government and private entities use AI to track digital money in order to reduce financial crimes and money laundering (Government of the Kingdom of Bahrain 2025).

As can be seen in

Table 1. Forms of AI and cryptocurrency tracking.

AI Technique	Use Case in Cryptocurrency Tracking	Benefit
Machine learning (ML)	Detecting suspicious transaction patterns	Identifies anomalies and flags potential fraud
Natural language processing (NLP)	Analyzing communication on darknet markets	Helps track illicit activities and actors
Neural networks	Predicting fraudulent transactions based on past data	Enhances fraud detection accuracy
Blockchain analytics AI	Tracing crypto transactions across multiple addresses	Identifies hidden connections and laundering
Anomaly detection AI	Flagging unusual transaction behaviors in real time	Improves early fraud detection

Source: (Wang et al. 2024).

, different forms of AI are crucial for monitoring cryptocurrency by investigating transaction patterns, determining anomalies, and identifying suspect activities in real time. AI’s advanced algorithms bolster financial security, helping prevent money laundering and other financial crimes. AI-powered systems also enhance regulatory compliance and increase transparency in digital financial transactions.

3.4. Key Research Factors

The use of AI in cryptocurrency tracking faces complex challenges, especially in terms of legal frameworks, financial regulations, and digital infrastructure. These obstacles erode public trust in the procedures and policies implemented by financial institutions. Consequently, the objective of this study is to identify the eight key factors (independent variables) that enhance public confidence in AI-driven cryptocurrency monitoring and its effectiveness in curbing financial crimes and money laundering in Bahrain (Figure 1).

Independent Variable 1: Proactive Tool

To reinforce the risk-based approach in combating the use of cryptocurrencies for terrorist financing and money laundering, the state must implement measures that strengthen its financial and legal systems, criminalizing illicit transactions to boost public trust. This entails adopting modern financial technologies like RegTech to stay aligned with evolving regulatory financial standards and efficiently manage financial data and transactions (FATF 2013). As a result, this research aims to explore the following hypothesis:

Hypothesis 1 (H1). 

The greater the utilization of AI as a proactive tool for tracking cryptocurrencies, the higher the public confidence in the measures taken by financial entities to combat financial crimes and money laundering.

Independent Variable 2: Tracking Mechanism

Machine learning and neural network techniques help build AI systems that can accurately track and analyze suspicious activity. These systems are used by regulatory bodies and financial institutions to enhance transparency and combat financial crimes (FATF 2012). Stemming from this, this study explored the following hypothesis:

Hypothesis 2 (H2). 

The increased application of AI in tracking cryptocurrencies enhances public confidence in the effectiveness of the efforts of financial institutions to mitigate financial crimes and money laundering.

Independent Variable 3: E-governance

E-governance enhances transparency by establishing reliable reporting systems and mandating financial institutions to disclose their transactions. AI depends on the thorough analysis of collected data to detect suspicious patterns. With clear and accessible data, AI systems can perform their tasks more efficiently and precisely (Bokhari and Myeong 2023). Therefore, we propose the following hypothesis:

Hypothesis 3 (H3). 

The expansion of e-governance within the domain of AI and cryptocurrencies positively influences public confidence in the strategies adopted by financial entities for reducing financial crimes and money laundering.

Independent Variable 4: Black Box AI

Despite the obvious benefits, the complexity of algorithms can make it difficult to interpret AI decisions, which is known as the “black box” problem. This problem makes it difficult for investigators or regulators to understand why a particular transaction was classified as suspicious. As a result, organizations may face challenges in providing clear evidence in court or regulatory filings (Wischmeyer 2020). Thus, this study analyzed the following hypothesis:

Hypothesis 4 (H4). 

The lower the complexity and ambiguity of AI and cryptocurrencies, the greater the public confidence in financial institutions’ measures to curb financial crimes and money laundering.

Independent Variable 5: Data Privacy

AI raises many ethical and social questions, such as privacy and manipulation of information. If these issues are not addressed carefully, they may lead to internal unrest that threatens the stability of states (Gbadebo et al. 2024). Therefore, we established the following hypothesis:

Hypothesis 5 (H5). 

The stronger the protection of data privacy in AI and cryptocurrency systems, the higher the public confidence in financial institutions’ efforts to prevent financial crimes and money laundering.

Independent Variable 6: Capability Gap

It is widely believed that, in Arab countries, there is a significant competence shortage for designing and operating AI and cryptocurrency systems. This shortage deepens the technological gap between countries or companies that can hire and train individuals with the relevant skills/competencies, thus weakening people’s confidence in the role of authorities and stakeholders in containing financial crimes (Udeh et al. 2024). Consequently, the present research assesses the following hypothesis:

Hypothesis 6 (H6). 

Advancements in technological capabilities related to AI and cryptocurrencies contribute to greater public confidence in financial entities’ initiatives to combat financial crimes and money laundering.

Independent Variable 7: Cyberattacks

AI can increase the risk of cyberattacks. Deep learning technologies can be used by malicious actors to develop more sophisticated and effective cyberattacks, such as smart phishing attacks that target victims in more precise ways or malware that adapts to defense systems. This puts additional pressure on countries to develop advanced cyber defense mechanisms so as to gain individual trust (George 2023). Considering this, we introduce the following hypothesis:

Hypothesis 7 (H7). 

A decrease in cyberattacks aimed at AI and cryptocurrencies boosts public trust in the effectiveness of financial institutions’ measures to prevent financial crimes and money laundering.

Independent Variable 8: E-sovereignty

AI technologies can enhance national cyber sovereignty if used strategically. AI can improve cyber defenses by identifying threats in real time and analyzing suspicious patterns. AI-based systems can quickly detect breaches, reducing their impact (Jelinek 2023). For this reason, we have outlined the following hypothesis:

Hypothesis 8 (H8). 

The higher the level of electronic sovereignty in AI and cryptocurrencies, the greater the public’s confidence in the measures implemented by financial entities to combat financial crimes and money laundering.

4. Methodology

Bahrain has positioned itself as a fintech hub, designing modern regulatory systems for digital finance under the supervision of the Central Bank of Bahrain. This has promoted a heightened level of consciousness among individuals, specifically among young people who interact more with fintech.

The increasing adoption of cryptocurrencies, especially among youth, is an international trend, but its influence is more pronounced in the Arab region, which features active fintech ecosystems. In Bahrain, youth dealing with cryptocurrency are impacted in several ways by the efforts of digital finance to ensure the safety of digital transactions. Exploring public perceptions is critical in this context, as it assists lawmakers in gauging the level of financial literacy and assessing possible vulnerabilities.

Moreover, engaging the public in such a debate enables a more comprehensive approach to limiting financial crimes, as young people play a key role in recognizing suspicious transactions. Given the fast digitalization of financial systems, particularly in developing countries, public participants in online surveys are not only relevant but are important for guaranteeing that both financial security measures and technological advancements evolve in tandem.

This study used an online public survey as part of a quantitative methodology. Online surveys have the benefit of letting respondents answer openly and honestly without worrying about judgment. Surveys containing multiple-choice questions were generated and sent out using Google Forms. Respondents were given the option to select multiple answers in various situations.

Six demographic questions about age, education, identity, gender, and nationality were included in the poll. Twenty-two questions were used to assess the primary factors. Participants were asked questions concerning how AI helps track cryptocurrencies used to finance terrorism and money laundering.

The research utilized snowball sampling to recruit respondents aged 18 years or older. To initiate the process, a sampling approach was used to recognize the first group of individuals who expressed readiness to take part in this study. These participants were chosen based on their potential to communicate with a broad layer of the target population. The recruitment of this initial cohort was conducted through social media platforms like WhatsApp. Following their involvement, the initial participants were urged to extend this research’s reach by referring the survey to others within their professional and personal circles.

The public had 60 days, from February to March 2024, to access the questionnaire. There were 403 responses to the survey, which included 268 Bahrainis and 135 non-Bahrainis. The sample size of 403 participants was determined using Cochran’s formula for an estimated population of over one million. Assuming a 95% confidence level, a 5% margin of error, and a 50% response distribution, the required sample size was calculated to ensure the statistical representativeness of the target population (Cochran 1977).

Participants gave details on their age, sex, and nationality, as shown in

Table 2. Demographic variables.

	Frequency	Percentage		Frequency	Percentage
Sex			Nationality
Male	153	38%	Bahraini	268	66.5%
Female	250	62%	Non-Bahraini	135	33.5%
Age Range
18–30	125	31%	50–59	58	14.4%
31–39	113	28%	60+	25	6.3%
40–49	82	20.3%

. It was discovered that 38% of the responders were men and 62% were women. Most responders (31%) were in the 18–30 age group, with those in the 31–39 age range coming in second (28%).

Table 3. Key factors influencing AI-related governance, risks, and security concerns.

Key Factors	Multiple Items	Number of Statements	References
Proactive tool	Preventive mechanisms and measures	1	(FATF 2013)
	Freezing the exchange and transfer of cryptocurrencies	1
Tracking mechanism	AI risks	1	(FATF 2012)
	AI effectiveness	1
	verifying the identity of clients and brokers	1
	AI challenges	1
E-governance	AI effectiveness	2	(Bokhari and Myeong 2023)
Black box AI	AI complexities	1	(Wischmeyer 2020)
	AI risks	1
Data privacy	Individual privacy	1	(Gbadebo et al. 2024)
Capability gap	AI effectiveness	1	(Udeh et al. 2024)
	AI challenges	1
	Bridging the technology gap	1
Cyberattacks	AI effectiveness	2	(George 2023)
	Types of cyberattacks	1
E-sovereignty	Protecting state sovereignty	1	(Jelinek 2023)
	AI challenges	1

outlines key factors influencing AI-related governance, risks, and security concerns, categorized into seven main themes: proactive tools, tracking mechanisms, e-governance, black box AI, data privacy, capability gaps, cyberattacks, and e-sovereignty. Each factor is linked to specific aspects, such as AI effectiveness, challenges, and risks, with references supporting their significance. Notably, AI effectiveness appears multiple times, emphasizing its role in governance and cybersecurity. Additionally, AI challenges and risks are prevalent, indicating concerns about transparency and security.

5. Findings

The integration of AI in combating money laundering and terrorist financing has revolutionized financial crime detection. AI-driven systems enhance monitoring by analyzing vast datasets, identifying anomalous patterns, and flagging suspicious transactions with greater speed and accuracy than traditional methods. These advancements strengthen regulatory compliance and mitigate risks, fostering trust in financial institutions. However, the reliance on AI raises public concerns about data privacy and potential biases in algorithmic decisions. Transparent implementation and ethical oversight are crucial to addressing these concerns, ensuring public confidence in AI as a tool to promote security while respecting individual rights (Garcia-Segura 2024).

5.1. Harnessing AI to Fight Financial Crime

Participants in the research were asked about the use of AI in addressing various types of financial crimes, with a focus on money laundering, terrorist financing, and financial fraud. Among those surveyed, 47.9% believe AI could be effectively used to combat money laundering, suggesting a higher awareness and perceived effectiveness of AI in managing money laundering risks compared to other areas, such as terrorist financing. The lowest percentage (42.2%) corresponds to responses about terrorist financing, which may reflect a belief that AI is less applicable or effective in this field, or it could indicate a lack of awareness about AI’s potential in this area. Meanwhile, 59.1% of responses related to financial fraud demonstrate strong confidence in AI’s capabilities.

Figure 2 shows different perceptions of how AI can help address the risks associated with cryptocurrencies in money laundering and financial fraud. The largest proportion of respondents (27%) believe that AI is most effective in identifying risks. This suggests a strong perception of AI’s capability to recognize potential threats in financial dealings involving cryptocurrencies. The responses for understanding (26%) and assessing risks (25%) are quite close, indicating a relatively balanced view of AI’s capabilities in these areas. The nearly equal distribution of the two (aforementioned) responses suggests that while AI is perceived as useful, there is no clear consensus on whether its strengths lie more in understanding the implications or in assessing the potential impacts of those risks.

Findings suggest a generally favorable view of AI’s potential in combating financial misconduct, with a notable emphasis on its applicability in detecting and managing risks associated with money laundering and financial fraud. Comparatively, its role in countering terrorist financing is perceived as less prominent, possibly reflecting differing levels of awareness or confidence in AI’s effectiveness across financial crime domains. Additionally, AI is broadly recognized for its capacity to identify, understand, and assess risks linked to using cryptocurrencies in illicit financial activities. However, views on the specific strengths of AI in these areas remain relatively balanced, indicating an ongoing exploration of its full capabilities and potential applications.

5.2. Tracking Suspicious Cryptocurrencies

AI plays a pivotal role in combating cryptocurrency laundering and terrorist financing by enhancing customer identity verification processes. Advanced AI systems analyze vast datasets to identify patterns, flagging suspicious transactions and behaviors in real time. By employing machine learning algorithms and biometric authentication, AI ensures accurate identification of customers and intermediaries, reducing anonymity in financial systems. This transparency fosters trust among the public, reinforcing confidence in regulatory institutions.

In this study, participants were presented with the question, “How can AI assist in tracking suspicious funds and virtual currencies, as well as verifying the identities of clients and intermediaries?” Participants were allowed to select multiple answers as responses.

Table 4. AI-powered processes for tracking suspicious cryptocurrencies by identity verification of customers and intermediaries.

	Percentage
Auditing documents or personal data	41.4%
Identifying the beneficial owner of the account that holds transaction data and financial returns	35%
Tracking the customer’s IP address and searching the web for confirmed activity information consistent with the customer’s transaction file	41.7%
Auditing the accuracy of data and information of financial platform intermediaries (cryptocurrency transfer service providers) by the competent authorities	33.7%
Monitoring the work and performance mechanisms of financial platform intermediaries and the extent to which they are subject to policies, instructions, and legislation	28.3%
Establishing suitable penalties for unlicensed financial entities or platforms involved in cryptocurrency transactions	15.1%
Submitting information and complaints to financial intelligence units	19.1%

shows that the highest percentages of responses (41.7% and 41.4%) reflect a strong confidence in the effectiveness of auditing documents and tracking IP addresses, highlighting that these methods are considered essential in addressing suspicious activities.

Many people think virtual currency exchange entities should be subject to AI regulatory systems to facilitate compliance with anti-terrorist financing and anti-money laundering provisions. Legal authorities should be able to identify entities or people providing virtual currency exchange services without registration or licensing and apply appropriate sanctions against them by employing algorithms. A minority of participants (33.7%) indicated that AI helps audit the accuracy of data and information of financial platform intermediaries (cryptocurrency transfer service providers) by the competent authorities. Also, 35% of participants expressed the belief that AI assists in identifying the beneficial owner of the account that holds transaction data and financial returns.

This discussion prompts us to ask whether individuals trust the role of AI in determining penalties for violators. Some believe that law enforcement agencies should require financial entities providing virtual currency exchange and money transfer services to implement national-level measures to combat the financing of terrorism and money laundering (FATF 2012), including the use of AI to exchange information with law enforcement agencies and other financial institutions to impose penalties on parties that do not comply with the law. Only a small number of respondents (15.1%) indicated that AI reinforces determining appropriate penalties for unlicensed financial entities or platforms transferring cryptocurrencies. This small percentage suggests that there is limited trust in the role of algorithms when it comes to determining penalties for those who violate regulations.

The results shown in Figure 3 indicate that the largest percentage of respondents (42.9%) were unsure whether AI should be used to freeze the exchange and transfer of cryptocurrencies. This suggests that there might be a lack of understanding or awareness about how AI can be applied in monitoring and freezing virtual currency transactions in these contexts. It is interesting to note that 40.7% of respondents see AI as a helpful tool in detecting and preventing financial fraud. This could reflect growing concerns over the rapid expansion of online financial crimes and the potential of AI to identify patterns of fraudulent activity.

Additionally, a slightly smaller percentage (34.2%) agree that AI should be used to freeze funds related to terrorism financing. While this remains a critical concern for both national and global security, the response rate is somewhat lower, possibly reflecting the view that some individuals perceive the issue as less urgent or believe other measures should take precedence. The smallest percentage (33.3%) indicates that fewer people think AI should be employed to freeze funds tied to criminal activities. This may suggest a belief that financial crimes are less closely linked to AI intervention or that other legal and financial mechanisms are considered more effective in addressing such issues.

Participants were surveyed regarding the benefits of utilizing AI in auditing cryptocurrency transactions, with the option to select multiple responses. As presented in

Table 5. The advantages of the use of AI in auditing cryptocurrency transfers.

	Percentage
Reducing the effort spent on financial regulation	47.6%
Applying the principle of transparency to the state’s electronic financial management system	26.3%
Applying the principle of transparency to the electronic management system of financial institutions	27.5%
Addressing the technological disparity between stakeholders	25.3%
Enabling stakeholders to manage risks better	34%

, 47.6% of respondents identified AI’s potential to minimize the effort required for auditing cryptocurrency transfers. This finding suggests that the primary advantage of AI in this context is its capacity to enhance the efficiency of regulatory procedures.

A significant proportion of respondents (26.3%) answered in favor of using AI to apply transparency principles to the state’s electronic financial management system, and a slightly higher number of respondents (27.5%) replied in favor of applying them to financial institutions. This shows that many respondents see AI as a tool for increasing transparency in financial processes, both within government frameworks and private financial institutions. Moreover, a quarter of participants (25.3%) said that using AI helps in addressing the technological disparity between stakeholders. The ability of stakeholders to manage risks better was cited by 34% of respondents, underscoring the growing awareness of AI’s role in enhancing risk mitigation strategies in cryptocurrency audits.

The research findings underscore a broad recognition of AI as a valuable tool in enhancing the regulation of virtual currency exchanges and financial transactions. AI is widely perceived as instrumental in document auditing, identity verification, and monitoring digital transactions, contributing to efforts against money laundering and terrorist financing. Participants emphasized the need for AI-driven compliance systems and information-sharing mechanisms between financial institutions and authorities. However, the role of AI in enforcing penalties and freezing illicit funds remains met with caution, revealing limited trust in algorithmic decision-making. Overall, AI is viewed as improving efficiency, transparency, and risk management in cryptocurrency oversight.

5.3. Using AI to Combat Financial Crime: Challenges and Issues

Participants were surveyed on the challenges associated with employing AI to track suspicious financial transactions and virtual currencies, with the option to select multiple responses. The findings indicate that the most significant challenge, identified by 42.4% of respondents, is the absence of robust legal frameworks. This is followed by difficulties in identifying cryptocurrency users (30.5%) and the weak regulatory oversight of financial institutions (29.8%). Conversely, concerns such as the inefficiency of payment platforms within private companies were perceived as less critical (17.9%). These results highlight a diverse set of challenges, primarily revolving around legal, regulatory, and technological complexities.

In addition, about a quarter of respondents view the absence of updated technologies as a barrier, reflecting the importance of technological advancements in addressing these challenges. Nearly 30% of respondents see weaknesses in financial institutions’ regulatory systems as a challenge, highlighting the need for stronger governance mechanisms within these entities.

More than a quarter of respondents (27%) recognize technological intricacies within cryptocurrencies as a significant hurdle, indicating the need for specialized expertise in this area. The need for improved digital financial regulation at the state level was marked/flagged as a concern by just over 20% of respondents. A similar proportion of respondents (20.1%) view the inadequacy of electronic resources and legal mechanisms as a challenge, pointing to the need for modernization in these areas.

This research reveals a complex landscape of challenges in employing artificial intelligence to monitor suspicious financial activities and virtual currencies. The primary obstacles are rooted in insufficient legal frameworks, regulatory limitations, and the technological opacity of AI systems. Difficulties in identifying digital asset users and weaknesses in financial oversight mechanisms further complicate implementation. Technological constraints, including the need for updated tools and specialized knowledge in cryptocurrency systems, underscore the necessity for enhanced digital infrastructure and governance. While AI holds significant promise in combating financial crimes, concerns over its transparency and decision-making processes raise critical issues regarding its broader applicability and public acceptance.

However, AI is a key tool in the fight against financial crimes, including tracking cryptocurrencies linked to money laundering and terrorist financing, and can enhance the quest by financial entities and authorities for public support in their crime-fighting efforts. However, the “black box” of AI, which refers to the lack of transparency in how algorithms make decisions, raises multiple issues and risks that affect the effectiveness of using this technology, increasing the probability of public concerns (Chaudhary 2024).

5.4. Black Box AI

The most surprising aspect of the data collected in this research is that a combined 53.6% of respondents think that the complexity of decision-making in algorithms either significantly or moderately affects the effectiveness of AI. This suggests that most individuals recognize a potential challenge in using complex algorithms to track cryptocurrencies for illicit activities effectively. A substantial 40% of respondents selected “I don’t know”, reflecting uncertainty or lack of clear insight into the relationship between algorithmic complexity and the effectiveness of AI. The notability of this portion suggests that the topic may require further research or clarification.

The data infer that there is a general belief that the complexity of decision-making processes in algorithms has an impact, either moderate or significant, on the effectiveness of AI in tracking cryptocurrencies used in money laundering and terrorist financing. However, a substantial percentage of respondents (40%) remain unsure, suggesting that additional education or empirical data on the matter may be necessary to form a more definitive consensus.

Figure 4 provides the distribution of opinions on whether the “black box” increases the risks associated with using AI to track cryptocurrencies linked to money laundering and terrorist financing. A strong majority (59%) view the “black box” as a risk factor, either significantly or moderately. A considerable portion of respondents (33%) are uncertain, indicating potential knowledge gaps or complexity in the subject matter. Only a small percentage (7%) believe that the lack of transparency does not significantly increase risks. However, to achieve maximum audience trust, there must be a balance between the complexity of algorithms and their ease of use and interpretation. Complex algorithms must be supported by comprehension-friendly technologies such as explainable AI to provide transparency for audiences.

The findings of this study suggest a general recognition that the complexity of decision-making in algorithms may influence the effectiveness of AI in tracking cryptocurrencies linked to illicit activities, such as money laundering and terrorist financing. Despite this, a notable portion of respondents expressed uncertainty regarding the relationship between algorithmic complexity and AI effectiveness. Additionally, the “black box” nature of AI was identified as a potential risk factor, with most participants indicating concern about its impact on trust and transparency.

To address these issues, the focus should be on developing explainable algorithms and providing tools to analyze the decisions made by intelligent systems and governance. Transparency is not only an ethical requirement but a practical necessity to foster trust and ensure the effectiveness of AI in tracking cryptocurrencies. Furthermore, institutions should work with regulators to ensure that AI systems are based on reliable data and comply with local and international laws.

5.5. E-Governance

E-governance provides a robust digital infrastructure that supports the collection and exchange of data between institutions in a secure and fast manner. AI systems can benefit from this infrastructure by accessing large and up-to-date databases, facilitating the process of tracking virtual funds associated with money laundering. Having a robust digital system reduces the loopholes that can be exploited to transfer illicit funds. All these operations will contribute to building community confidence in Bahrain.

A significant portion (48.8%) of respondents believe that AI-powered e-governance can be very effective in preventing terrorist financing through cryptocurrencies. This suggests a strong positive sentiment towards AI’s potential role in combating financial crime in this area. About 24.6% of respondents think that AI can help to some extent (“helps moderately”), indicating a moderate level of confidence in AI’s role.

A small percentage (3.6%) of people feel that AI-powered e-governance does not contribute to the prevention of terrorist financing through cryptocurrencies. This reflects a certain degree of skepticism about AI’s effectiveness in this domain. Another 3.6% believe that AI-powered e-governance is not suitable for tackling this specific issue. These respondents may have concerns about the technology’s ability to address the nuances of terrorist financing or its application in the context of cryptocurrencies. In addition, as we can see in Figure 5, 15.5% of participants are unsure or lack enough knowledge to make a definitive judgment. This indicates a level of uncertainty or a need for more information and understanding about the capabilities of AI-powered e-governance to prevent terrorist financing.

Strengthening AI-driven e-governance plays a key role in minimizing the risks related to suspicious activities within the virtual financial system. By increasing the accuracy of monitoring and oversight, financial transactions become more secure and dependable. These measures also boost confidence in digital systems, encouraging more individuals and businesses to engage with virtual financial services.

Countries have sought to secure themselves through cybersecurity, using technologies, technical processes, and controls aimed at protecting systems, networks, and programs from digital attacks. These attacks typically involve attempts to access, alter, or destroy sensitive information. If successful, these attacks can result in extortion of money from the victim, infringement of intellectual property rights, or disruption of service delivery (Aldada and Ali 2022).

Despite its benefits, e-governance faces challenges related to cybersecurity and privacy protection. If they are not adequately secured, these systems can be exploited by criminal groups. This is where enhancing cybersecurity, such as the use of advanced encryption techniques and continuous updates of systems to address threats, as part of e-governance can play a significant role (Kumar et al. 2025).

5.6. Data Privacy

The use of AI to track cryptocurrencies is a controversial topic in Bahrain, with supporters who see it as an effective means of combating financial crimes and opponents who see it as an invasion of privacy. To understand this contradiction, the role of AI in this context and its impact on privacy must be explored.

Figure 6 indicates that the use of AI in tracking cryptocurrencies raises concerns about privacy for a significant portion of the population. The majority of respondents (39%) chose to answer that the use of AI to track cryptocurrencies in money laundering and terrorist financing conflicts “to some extent” with individual privacy, implying that they have privacy concerns but are not fully convinced that AI-based tracking represents a serious threat. This is a nuanced view that might suggest people see a potential trade-off between security and privacy. A minority (19%) answered positively, indicating they fully believe that AI tracking conflicts with privacy. However, many respondents acknowledge that there may be a partial conflict rather than a total one. The uncertainty expressed by nearly 18% of respondents suggests that further discussion and education on the topic may be necessary to clarify public opinion in Bahrain.

Figure 6 shows that data privacy is a major concern for the public in Bahrain. With AI being used to analyze massive amounts of data, sensitive national data becomes vulnerable to hacking or manipulation, threatening data privacy and the digital sovereignty of states.

As AI technology rapidly advances, it brings significant implications for the cyber sovereignty of nations. Cyber sovereignty refers to the ability of a country to control and protect its cyberspace and digital infrastructure from external and internal threats. With the transformation of AI, challenges and opportunities arise that directly affect this sovereignty.

5.7. Technological Sovereignty

This study examines public awareness regarding the application of AI in detecting financial crimes and evaluates its implications for state sovereignty. In response to the question “How does the use of AI technologies to track cryptocurrencies enhance the state’s ability to protect its electronic sovereignty?”, a range of responses were elicited. Overall, the data show that most respondents view AI as having at least a moderate impact, with the largest group (44%) believing it has a high impact on protecting electronic sovereignty. A minority (6%) view AI as having either a weak or no impact, while about 15% are uncertain. This clearly indicates that the general perception tends to view AI as a crucial tool in protecting electronic sovereignty.

The findings indicate that AI is generally perceived as a significant tool in safeguarding electronic sovereignty, with most respondents acknowledging its positive impact. While a small proportion of participants expressed doubts about its effectiveness, the majority recognized AI’s potential to enhance state control over digital domains, particularly in monitoring and regulating cryptocurrencies. These results highlight the growing importance of AI technologies in reinforcing the protection of electronic sovereignty against emerging digital threats.

Moreover, Bahrain can use AI to develop indigenous and independent digital infrastructures, reducing its dependence on foreign technology. Investing in domestic research and development can lead to the creation of customized AI systems that meet their security needs, which may enhance people’s confidence in policies and procedures. The impact of AI on national cyber sovereignty depends largely on the policies that the state follows. By investing in building advanced domestic AI capabilities, Bahrain will be less likely to jeopardize its cyber sovereignty and will enhance public faith in policies and procedures.

5.8. Capability Gap

A fundamental aspect of this study is the examination of public awareness regarding the relationship between technological disparities and the implementation of AI in detecting financial crimes. In response to the question of how much the gap in the state’s technological capabilities affects the efficiency of using AI to track cryptocurrencies used in money laundering and terrorist financing, most of those surveyed (60.7%) indicated that the technological gap has a high impact on the efficiency of AI, while 23.8% of respondents think the technological gap has a medium impact on AI’s effectiveness in this area.

The above data show that participants believe that the technological infrastructure remains uneven between countries and even between sectors within the same country. Countries with developing economies often lack the resources needed to build advanced AI platforms. This includes the absence of robust data centers, advanced software, or high-speed internet networks, which diminishes the effectiveness of AI technologies.

When participants were asked which factors increase the technological gap in the use of AI technologies to track cryptocurrencies used in money laundering and terrorist financing, more than half (57.1%) responded that weak technical infrastructure is a major factor. This implies that insufficient technological systems and platforms present a major obstacle, hindering the effective implementation of AI technologies. More than half of respondents (54.8%) pointed to a shortage of local expertise as a key factor, indicating that the absence of skilled professionals and knowledge in AI is a critical obstacle to using advanced technologies for financial tracking.

The data in Figure 7 clearly show that nearly half of the respondents (44%) view dependence on foreign technology as a key factor contributing to the technological gap. Moreover, a third of respondents )33.33%) identified a lack of funding as a significant factor contributing to the technological gap in AI adoption for tracking cryptocurrencies in money laundering and terrorist financing. This suggests that, although funding is a concern, it is not as significant as other factors.

The above data allow us to infer that the technology gap between countries or institutions hinders the full benefit of these technologies coming forth. Closing this gap requires implementing comprehensive solutions, including enhancing infrastructure and reinforcing international partnerships. The lack of cooperation between countries in exchanging data and technologies contributes to widening the technological gap, as countries with strong infrastructure benefit from advanced technologies while others remain isolated (Medhioub and Boujelbene 2024).

Moreover, countries and institutions must invest in upgrading their internet networks, enhancing computing power, and providing data centers capable of accommodating advanced analysis and containing cyberattacks. A robust infrastructure allows cryptocurrency tracking systems to be deployed more effectively and accurately, helping to counter cyberattacks and, in turn, boosting public trust.

5.9. Cyberattacks

The rise in cyberattacks targeting financial platforms may contribute to public skepticism regarding the role of AI in detecting financial crimes. Accordingly, this study explores public perceptions of the influence of cyberattacks on AI and cryptocurrency transactions. In response to the question, “How much do cyberattacks affect the effectiveness of using AI techniques to track cryptocurrencies used in money laundering and terrorist financing in Bahrain?”, most respondents (56%) claimed that cyberattacks have a high impact on the effectiveness of AI techniques. This suggests that many perceive cyberattacks as a significant challenge to the efficacy of AI in these areas. A smaller percentage of respondents (23.8%) consider cyberattacks to have a moderate impact, suggesting that, while they acknowledge some effect on AI, they view it as less significant than those who see a high impact. A small number of respondents (1.2%) believe that cyberattacks do not affect the effectiveness of AI. The general trend uncovered by the analysis reveals that most respondents perceive cyberattacks as having some level of impact on AI’s effectiveness.

It should be noted that cyberattacks on AI cryptocurrency tracking systems differ in both nature and impact. The most common types include ransomware attacks, denial-of-service attacks, data theft, hacking incidents, as well as attacks targeting algorithm manipulation. Following this line of inquiry, participants were asked to choose the most common types of cyberattacks that hinder the use of AI to track cryptocurrencies used in money laundering and terrorist financing in Bahrain.

Figure 8 reveals that ransomware attacks are the least common type of cyberattack in this dataset, with only 13.1% of respondents identifying them as a significant hindrance. These attacks often involve encrypting data or systems, which can disrupt AI systems tracking cryptocurrencies. Algorithmic manipulation attacks are a significant concern, with 29.8% of responses identifying them as a major issue. Algorithmic manipulation involves altering or corrupting AI models, which could lead to false positives or missed detections in tracking virtual financial transactions used for money laundering or terrorist financing.

In addition, distributed denial-of-service (DDoS) attacks are more common, with 32.1% of respondents citing them as a major concern. These attacks overwhelm systems by flooding them with traffic, causing a disruption in the ability to track transactions accurately and efficiently. Hacking is the most reported type of attack, making up 45.2% of responses. Hacking and data theft can severely undermine AI systems by stealing sensitive data or compromising tracking algorithms, thus obstructing efforts to detect illegal money flows.

The findings suggest that cyberattacks are perceived as a significant challenge to the efficiency of AI in detecting illicit activities, particularly in the context of money laundering and terrorist financing. Various forms of cyberattacks, including hacking, algorithmic manipulation, and DDoS attacks, are highlighted as major concerns due to their potential to disrupt AI systems and compromise the accuracy of cryptocurrency tracking. These results underscore the growing need for robust cybersecurity measures to support AI-driven financial crime detection.

The cyberattacks on AI systems used to track cryptocurrencies are among the biggest challenges facing Bahrain and other countries today. These types of attacks can cause significant damage, not only to individuals and businesses but also to national and economic security. Therefore, countries must adopt a set of integrated strategies to reduce these risks and protect these vital systems. However, a notable 42.9% of respondents admitted uncertainty about which cyberattacks hinder the use of AI, highlighting a gap in knowledge or awareness regarding how cyberattacks impact AI-driven tracking systems in the context of financial crimes.

6. Discussion and Conclusions

This study contributes to the limited academic literature on public perceptions of AI in tracking cryptocurrencies to combat financial fraud and terrorist financing in Bahrain. While previous studies have tended to emphasize the perspectives of policymakers or technology developers, this research centers on public trust as a critical dimension in the implementation and legitimacy of AI-driven financial monitoring systems.

Although this study relied primarily on attitudinal data, the empirical insights gathered reveal nuanced and actionable findings. Participants expressed a generally positive outlook on the role of AI in detecting financial fraud and enhancing regulatory efficiency. However, this trust was notably lower regarding AI’s application in more sensitive areas, such as countering terrorist financing or determining legal consequences, highlighting a gap between technological potential and public trust.

The data also revealed recurring concerns over AI’s complexity, potential privacy infringements, and the scarcity of qualified personnel—concerns that signal systemic challenges in the adoption of AI technologies within Bahrain’s financial ecosystem. Skepticism around the freezing of virtual assets and the ambiguity regarding AI’s role in cross-border transactions reflect both technical and regulatory uncertainty. These perceptions underscore the importance of advancing not only the technology itself but also the supporting legal, institutional, and educational frameworks.

Moreover, the findings point to critical vulnerabilities: participants identified cyberattacks, such as hacking, DDoS incidents, and algorithmic manipulation, as substantial threats to AI effectiveness. This indicates a pressing need to integrate cybersecurity enhancements with AI development. This study suggests that bolstering AI’s resilience will require a coordinated approach involving infrastructure upgrades, specialized training, and sustained international cooperation.

Future research should seek to move beyond perception-based assessments by incorporating more robust empirical models that quantify the relationship between AI integration and measurable outcomes in crime prevention. Additionally, longitudinal studies could explore how public trust evolves alongside technological and regulatory advancements. Policy initiatives must prioritize transparency in AI systems, reinforce legal protections, and expand capacity-building efforts to foster a secure and trusted AI-enabled financial environment in Bahrain.