8.1 Introduction

The digital age has ushered in an era where criminals use advanced technologies to flood the financial system with financial crime risks (FCRs), showing a level of sophistication that traditional crime prevention measures struggle to counteract effectively. Article 3(2) of the EU AI Act 2024 defines risk as ‘the combination of the probability of an occurrence of harm and the severity of that harm’. According to the UK’s Financial Conduct Authority (FCA’s) 2025 Firm’s Guide to Countering Financial Crime Risks FCRs (FCA, 2025), FCRs include but are not limited to risk of money laundering and terrorist financing; fraud; bribery and corruption; sanctions, asset freezes, and proliferation financing; and insider dealing and market manipulation. Banks such as HSBC see FCR as the risk that their products and services will be exploited for criminal activities such as ‘fraud, bribery and corruption, tax evasion, sanctions and export control violations and evasion, money laundering, terrorist financing and proliferation financing’ (HSBC, n.d.). In an increasingly interconnected economic landscape, financial institutions face a persistent and growing threat from resourceful and adaptable financial criminals, who engage in these criminal offences. These crimes pose unprecedented threat to the integrity and stability of financial systems worldwide.

8.2 Applications of AI in Financial Crime

8.3 Voices of Bank Employees on the Use of AI

While our critical analyses above have thrown more light on the existing literature, the empirical dimension of these elements in the context of benefits and utility has been limitedly approached. This sub-section, thus, explores UK banking employees’ perspectives on AI adoption in relation to advantages and best practices for responsible deployment and utilisation in AI-driven FCRM systems. In this regard, we anecdotally surveyed thirty-seven employee respondents (including frontline client-facing teams, compliance officers, risk analysts, and AI specialists) from top three UK high street banks (i.e., Barclays Bank, HSBC Bank, and Lloyds Banking Group), selected based on their description as a major UK bank by the Bank of England in its 2024 resolvability assessment of major UK banks published on the 6th of August 2024 (Bank of England, 2024). We have then analysed the findings from this anecdotal survey on the perspectives of bank employees on AI’s application in FCRM in the three UK banks, along with authoritative statements of senior bank managers we extracted from the websites of the banks and third parties. These have been complemented with findings from a 2024 AI qualitative survey by the Bank of England and FCA based on 118 survey responses (Bank of England and FCA, 2024). The following thematically represent the results therefrom.

The first theme considered was strategic advantages. One of the strategic advantages of AI in FCRM identified by all the thirty-seven respondents is that AI enhances fraud detection and transaction monitoring. Respondents posit that AI-driven fraud detection algorithms identify anomalous financial behaviours in real time, strengthening response mechanisms. They also opine that machine learning models reduce false positive rates, improving efficiency in fraud mitigation (El Hajj and Hammoud, Reference El Hajj and Hammoud2023; Jofre, Bosisio and Riccardi, Reference Sachoulidou2024).

Another strategic advantage is that AI deployment in FCRM ensures operational efficiency and cost optimisation. To this end, respondents have averred that responsible deployment of AI can automate compliance tasks, streamline labour-intensive processes and reduce operational costs (Doddipatla, Reference Doddipatla2024). Respondents also observe that AI-enhanced KYC frameworks expedite customer due diligence while ensuring regulatory adherence. This is in line with authoritative statements from NatWest, HSBC, and Barclays extracted from the internet. For instance, the Head of Payment Fraud of NatWest, Nick Elliot, observes that a new Mastercard’s AI tool helps the bank to ‘identify payments to fraudsters and stop them before money is lost’ (Mastercard, 2024).

Similarly, HSBC’s Group Head of Financial Crime, Jennifer Calvery, notes that ‘AI has helped us to improve the precision of our financial crime detection and reduce alert volumes, meaning less investigation time is spent chasing false leads … contributing to more effective outcomes in the fight against financial crime’ (Calvery, 2024). Barclays Group Chief Information Officer, Craig Bright, has also said that ‘while analytical AI has long helped us manage fraud and money laundering risks, generative AI creates opportunities to help us reduce these even more – something that is absolutely essential for us as a bank’ (Barclays, Reference Ranchordas2024).

In fact, as part of the above advantage, the adoption of AI in UK financial services has grown significantly, with banks leveraging machine learning and generative AI to address FCRs. The 2024 Bank of England and FCA AI survey shows that of 118 survey responses, 75 per cent of financial firms currently use AI, with an additional 10 per cent planning to adopt it in the next three years. The survey highlights that 33 per cent of respondents are using AI for fraud detection, and an additional 31 per cent plan to do so over the next three years. AML and combating the financing of terrorism (CFT) are also among the top use cases, with 64 per cent of firms either currently using or planning to use AI in these areas (Bank of England and FCA, 2024).

Ensuring regulatory compliance and institutional reputation is yet another advantage espoused by the respondents under the strategic advantages’ theme. In this regard, AI-driven risk assessment tools bolster compliance with regulatory bodies such as the FCA and the Competition and Markets Authority (CMA). In the same vein, AI strengthens compliance mechanisms in FCRM to reinforce institutional credibility and clients’ trust.

Apart from the strategic advantages, gendered disparities in AI perceptions were thematised. The responses on this theme exhibit notable gender-based variations in attitudes toward AI for FCRM in banking. While female respondents are more likely to affirm the ethical viability of AI in FCRM, male respondents display a broader range of opinions, including higher levels of uncertainty. These findings resonate with prior research indicating that gender influences ethical decision-making and risk aversion in financial contexts (Marcus and Hargrave, Reference Marcus and Hargrave2021). Studies such as Huang and Kisgen (Reference Huang and Kisgen2013) suggest that women in financial sectors tend to exhibit heightened ethical awareness and greater caution in risk assessment (Huang and Kisgen, Reference Huang and Kisgen2013).

The other theme considered is generational divide in AI acceptance. Here, the data reveals that younger banking professionals exhibit a stronger propensity to endorse AI as an effective tool for financial crime mitigation, whereas older respondents present a more ambivalent or critical stance. This divergence is aligned with existing scholarship on technology acceptance, which posits that younger demographics may demonstrate a higher level of adaptability to AI-driven solutions in financial services (Dwivedi et al., Reference Rizinski2021). Resistance among older professionals may stem from entrenched scepticism regarding AI’s capacity for unbiased decision-making and regulatory compliance.

The last theme considered is institutional trust and risk perception. The dataset on the thirty-seven respondents suggests that professional trust in AI governance frameworks significantly influence attitudes toward AI ethics. Respondents who have expressed neutrality or disagreement with AI deployment in FCRM harbour concerns regarding algorithmic bias, data privacy risks, and regulatory oversight (De Silva, Reference De Silva2025). Conversely, respondents in strong agreement view AI as an indispensable instrument in combating financial crime through automation and predictive analytics. These survey results resonate with findings from statements of Lloyds Banking Group and NatWest Group executives on their websites. For instance, talking about Lloyds Banking Group’s AI Centre of Excellence, Dr Rohit Dhawan, Director of AI and Advanced Analytics at the Group, has observed that as they push the boundaries of GenAI, it is ‘vital that we have the appropriate safeguards in place to ensure the technology is implemented ethically and safely’ (Lloyds Banking Group, 2025). In the same vein, NatWest Group Chief Information Officer, Scott Marcar, notes that AI ‘is already playing a vital role in our digital transformation’ and that bank commits to deploying it ‘in the safest and most responsible way’ under a strict ethics code (NatWest Group, 2025).

8.4 Challenges and Opportunities of AI in FCRM

The increasing integration of AI into FCRM has been associated with several challenges and opportunities, which change the ecosystem of regulatory compliance in relation to financial services. We have explored a few of these challenges and opportunities that contribute to robust FCRM systems.

One of the primary challenges of FCRM is that financial institutions need to upgrade their underlying data to ensure high-quality data and analytics capabilities, which comes with significant costs. The voices of the thirty-seven UK bank employees also point out that effective integration of AI depends on building and sustaining robust data quality infrastructures. This is in line with participants in numerous studies that stress that the accuracy and reliability of AI outputs are fundamentally connected to the quality of input data, and that insufficient or poor data quality can lead to misinterpretation of financial risks. For instance, the 2024 Mosaic study of six million fixed income, currencies, and commodities (FICC) transactions from global and regional investment banks in Australia, Canada, European Union (EU), Japan, South Africa, South America, UK, and US finds that 66 per cent of banks are challenged with ‘data quality, gaps in important data points and some transaction flows not being captured at all’ (Mosaic, 2024).

Similarly, in support of the above, the 2022 Bank of England’s Study indicates that 43 per cent of firms identify data quality and structure issues as significant risks associated with machine learning (Bank of England, 2022). This finding is further supported by studies such as by the Central Banking Staff (Reference Regnier2022) and Ramaliba and Jacobs (Reference Ramaliba and Jacobs2024). Therefore, data quality emerges as a cornerstone in the responsible and effective implementation of AI-based FCRM. It is imperative to fully establish rigorous data governance practices, continuous data cleansing, and verification mechanisms to capitalise on AI capabilities.

The requirement for high-quality datasets implies that banks need to invest more in infrastructure, technology, and AI-skilled personnel, alongside dealing with the nuances of data privacy and management to ensure effective compliance with relevant regulatory frameworks such as the Data Protection Act (DPA) 2018 (Rouhollahi, Reference Rouhollahi2021; Sari and Indrabudiman, Reference Sari and Indrabudiman2024). Our survey of the thirty-seven respondents from the three major banks shows that respondents are concerned about the lack of AI regulation in the UK together with AI’s reliance on large-scale data aggregation and the impact of these on clients’ privacy and data protection (Kanamori, et al., Reference Sachoulidou, Vavoula, Lucarelli and Sperling2022). While AI regulations are being formulated, financial institutions in the UK should maintain strict adherence to DPA, General Data Protection Regulation (GDPR) and other international data governance frameworks (European Commission, Reference Roodt and Benson2025).

Another challenge is that the risk of algorithmic bias remains high. For instance, if the datasets used to train AI systems have biased elements in favour of high-income earners, such systems are likely to inadvertently perpetuate these biases. This could lead to erroneous FCR assessments and compliance failures. These could result in poor decisions on FCRs, exacerbate systemic inequalities, and erode public trust (Kurshan and Shen, Reference Samuel2020). We also find from our thirty-seven survey respondents that AI systems may reinforce discriminatory biases, disproportionately affecting marginalised demographics (Barocas, Hardt and Narayanan, Reference Rane, Choudhary and Rane2023). Ensuring algorithmic fairness and transparency therefore remains a pressing ethical imperative (Durodola, Reference Durodola2024).

An additional challenge is that, although AI systems can identify complex patterns and anomalies, they are also inclined to produce false positives, which can overwhelm compliance teams, undermining confidence in the overall results (Kan, Reference Sachoulidou2024). This can be attributed to the fast-evolving nature of financial crimes, which requires AI systems to continuously adapt and learn. This adaptive learning requires banks to constantly update their algorithms to keep pace with new fraud tactics that criminals deploy (Odeyemi et al., Reference Odeyemi2024). The risk of banks to unduly rely on AI to address FCRs is also high. This could lead to complacency among human analysts who may neglect their critical judgement in favour of AI-generated insights (Kanupriya, Reference Kanupriya2024). Banks’ reliance on AI systems in FCRM also raises concerns such as opacity of decision-making processes, which challenges fairness, accountability, and transparency of these systems (Owolabi, Reference Slaby2024).

Furthermore, there is inadequate AI-related skills in banks, which makes effective FCRM challenging. The continued evolvement of AI systems along with limited qualified professionals that are adept in AI-related skills such as machine learning, NLP, data science and financial regulations does hinder the implementation and advancement of AI applications in FCRM (Liu, Reference Sathye and Islam2023). This issue is particularly pronounced in small-scale financial institutions that may not have enough resources to attract top talent to develop and maintain sophisticated AI systems related to FCRM. For instance, the sophistication of deepfakes using deep learning does challenge data integrity, requiring highly skilled bank professionals to unravel. Deepfakes refer to the outcome of cloning real-life situations, which are often difficult to be detected even by experts.

As a case in point, criminals used deepfakes to impersonate ‘chief financial officer of a multinational firm through a video conference’ (Vecchietti, Liyanaarachchi and Viglia, 2025, p. 1) to deceive a finance professional into paying them US$25 million. Unique challenges of deepfakes such as the manipulation of video calls to impersonate clients, production of fake financial documents, and creation of synthetic identity fraud have particularly been critical to the banking sector. Deepfakes compromise data by threatening the accuracy, reliability, authenticity, and consistency of data, which makes it challenging for banks to trust the information they use for FCRM decision-making, even with the help of AI (Vecchietti, Liyanaarachchi and Viglia, 2025).

In the same light, we have gathered from our thirty-seven survey respondents that there is high rate of sophisticated AI-driven financial crime in the UK. Respondents note that malicious actors exploit AI’s capabilities for advanced cyber fraud, including deepfake identity fraud, and algorithmic phishing attacks (King et al., Reference Sakshi and Wadhwa2021). Respondents propose that AI-driven adversarial attacks necessitate continuous system refinement to counter emerging threats. The Bank of England and FCA AI Survey 2024 supports this finding, further highlighting that AI-based disinformation tools such as deepfakes could be used to increase economic uncertainty. Notwithstanding this, AI has the potential to be used to enhance ability of financial institutions to counter threat or malicious actors, such as in the detection ‘of cyberthreats by improving the automated identification of malware or illicit finance activity’ (Bank of England, 2025).

Additionally, as AI technology continues to evolve and there is no comprehensive AI regulation covering all present and future risks, the evolving regulatory landscape for AI in financial institutions complicates compliance efforts (Buckley, Arner and Zetzsche, Reference Rebovich, Kim and Layne2024). For instance, we find that the absence of comprehensive standardised AI governance protocols in the UK poses challenges for bank employees and their financial institutions seeking regulatory clarity. Overall, in our survey of the thirty-seven bank employees, they agreed strongly that the deployment of AI in banks is challenging.

Despite these challenges, AI provides several opportunities in FCRM (Dwivedi et al., Reference Rizinski2021), which can be aligned to sustainable and responsible use of AI. One of the notable opportunities is that AI systems can predict potential vulnerabilities and anticipate fraudulent activities before they occur (Rouhollahi, Reference Rouhollahi2021; Milon, Reference Milon2024). This enables banks to allocate resources more judiciously and mitigate risks before they materialise. Additionally, AI tools such as machine learning algorithms and deep learning can enable real-time tracking of financial transactions and instantaneously flagging of suspicious activities (Kan, Reference Sachoulidou2024; Кuzmenko et al., Reference Кuzmenko, Yarovenko and Perkhun2023). This measure supports banks to better understand risks and approaches that can be used to address such risks. For instance, biometric authentication and advanced encryption using machine learning algorithms bolster security and decrease the likelihood of fraud.

The deployment of AI to automate and streamline compliance operations can enhance the ability of banks to monitor and comply with stringent AML regulations such as in relation to reporting suspicious activities and transactions (Milon, Reference Milon2024). This is particularly significant because there is increasing scrutiny from regulators on compliance and penalties for violations, which can have huge financial ramifications for the institutions. For instance, in October 2024, TD Bank was fined about US$3.09 billion for AML failures in its USA’s operations (KYC360, Reference Sarker2025).

The opportunity to use AI systems in enhancing collaborative approaches to tackling financial and economic crime is also notable. Improving data-sharing mechanisms through AI offers banks the opportunity to foster better cooperation among themselves and regulatory bodies (e.g., LEAs). This enables a more coordinated approach to uncovering cross-border illicit activities (Jamil et al., 2021; Fauzi et al., Reference Fauzi, Szulczyk and Basyith2018). Such collaboration could provide financial institutions with a more comprehensive view of potential risks and crime syndicates, which enhances their FCRM and deterrent capabilities.

AI tools can empower banks to respond to several risk factors and economic uncertainties more deftly and adaptively. For instance, advanced (AI-embedded) robotic process automation (RPA) can automate routine banking processes such as compliance checks and transaction monitoring, which can significantly reduce operational costs and potential human error (Nama, Reddy and Pattanayak, Reference Nama, Reddy and Pattanayak2024; Temelkov, Reference Temelkov2023).

AI can be integrated into business intelligence to identify vulnerabilities in banking operations and to prioritise remediation efforts, thereby strengthening risk protection mechanisms in banks such as cybersecurity measures amid increasing threats associated with economic and financial uncertainties. AI integration in FCRM leads to agile organisational structures that could better respond to risks and even sudden economic shifts. Stakeholders will better benefit from a more adaptive approach to FCRM provided by AI systems or tools (Oyeniyi et al., Reference Oyeniyi, Ugochukwu and Mhlongo2024).

8.5 AI and FEAT in FCRM

The integration of AI with FCRM must be approached with a commitment to sustainable and responsible principles such as those encapsulated in the FEAT framework. The thirty-seven survey respondents opine that financial institutions should integrate FEAT principles into AI governance frameworks to ensure equitable, accountable, and transparent AI applications (Bogina et al., Reference Rashid and Kausik2021). Fairness (F) is a principle that must be upheld in the deployment of AI systems in FCRM. As AI systems that are trained on historical data could inadvertently perpetuate existing inequalities (Owolabi, Reference Slaby2024; Adeyelu, Reference Ping2024; Kurshan, Reference Kurshan2021), banks must implement rigorous testing and validation processes to ensure that AI models do not discriminate against any group or individual.

This includes applying fairness-aware algorithms to regularly audit AI systems to identify and rectify unintended biases in areas such as approving credit or loan pricing, ensuring that there are equitable outcomes in the risk management ecosystem. It also includes engaging with diverse stakeholder teams to incorporate fair AI algorithms in the development and deployment of AI (Ostmann and Dorobantu, Reference Singh and Best2021; Rizinski et al., Reference Rizinski2022; Owolabi, Reference Slaby2024). For instance, fairness-aware algorithm used in IBM’s AI Fairness 360 (AIF360) can check for unwanted biases in datasets and machine learning models, and present state-of-the-art algorithms to mitigate such biases (IBM, 2018; Fritz-Morgenthal, Hein and Papenbrock, Reference Fritz-Morgenthal, Hein and Papenbrock2022).

Education (E) also plays a pivotal role in fostering FCRM. Banks must continuously equip their staff with the knowledge and skills to understand the dynamic nuances of AI technologies such as interpretation AI outputs as well as ethical considerations, implications, and limitations of AI in banking and finance (Hasan et al., Reference Hasan2023; Oyewole, Reference Sophia2024). Bank professionals and other stakeholders should remain abreast of emerging trends and best practices in AI and risk management. AI and FCRM education in banks can promote a culture of informed decision-making, thereby enhancing their capacity to leverage AI responsibly and ethically as well as the overall landscape of FCRM (Hasan et al., Reference Hasan2023; Oyewole, Reference Sophia2024). Industry-wide knowledge sharing enhances the collective resilience of financial institutions against AI-enabled financial crime.

Accountability (A) mechanisms are equally essential to ensure that AI systems are used responsibly in FCRM. Banks must establish clear lines of accountability for AI-driven decisions, ensuring that specific individuals or teams are assigned the responsibility of overseeing the implementation of AI (Owolabi, Reference Slaby2024; Adeyelu, Reference Ping2024). To harness accountability of AI in FCRM, it is crucial to foster a regulatory framework that emphasises the human oversight. While AI can enhance efficiency, it is also fallible and should, thus, be used as a tool to support judgement of bank professionals in FCRM rather than replace it (Adeyelu, Reference Ping2024; Akinrinola, Reference Akinrinola2024).

Thus, there should be a human-in-the-loop process established, as exemplified in the Quilt Framework with a robust Senior Managers and Certification Regime (SM&CR). This measure enhances accountability and mitigates risks associated with automated decision-making (Owolabi, Reference Slaby2024; Adeyelu, Reference Ping2024; Durodola, Reference Durodola2024). In addition, AI models must undergo continuous refinement to address the evolving accountability issues and tactics of financial criminals. Similarly, there should be more investments in explainable AI (XAI) as this will enhance regulatory confidence and institutional accountability.

Such an accountability framework must have protocols for addressing any adverse outcomes from AI decisions (Owolabi, Reference Slaby2024; Adeyelu, Reference Ping2024). Therefore, it is imperative for banks to implement frameworks that ensure their AI systems are interpretable and that their decision-making processes can be effectively audited regularly (Maree et al., Reference Sciurba2020). Regular AI audits and bias assessments should be mainstreamed as they enhance the reliability and fairness of decision-making algorithms. Further, banks should advance practices that facilitate the explainability of AI models. This should be done in such a way that stakeholders can comprehend the rationale behind automated decisions (Maree et al., Reference Sciurba2020).

Transparency (T) is also vital in FCRM. Banks should enhance predictive analytics to refine, for instance, credit risk assessment, making it more transparent and reliable for stakeholders involved in decision-making processes (Brown, Reference Brown2024). With the use of AI-driven analytics, banks can better assess creditworthiness of clients and tailor financial products to underserved populations, thus, promoting financial inclusion (Sari and Indrabudiman, Reference Sari and Indrabudiman2024; Kanupriya, Reference Kanupriya2024).

In the same vein, stakeholders such as clients and regulatory bodies should be able to access clear information regarding how AI systems operate, their performance, impact, and the criteria used in decision-making processes (Maree et al., Reference Sciurba2020; Akinrinola, Reference Akinrinola2024). Banks should, thus, engage in transparent reporting practices to foster a culture of accountability, trust, continuous improvement, and empowerment of users to make informed decisions based on insights generated from AI (Maree et al., Reference Sciurba2020; Akinrinola, Reference Akinrinola2024).

Furthermore, the integration of AI in FCRM necessitates a proactive regulatory approach to ensure that as many risk factors as possible are covered. This requires close collaboration between policymakers, banks, and technology providers to develop and update regulations that foster innovation while safeguarding against potential risks of AI deployment in FCRM (Truby et al., 2020; Ekundayo, Reference Roberts2024). This includes bolstering measures such as standards for AI governance, risk assessment, and data management that promote sustainable and responsible financial practices (Ekundayo, Reference Roberts2024; Zhou, Reference Turksen, Benson and Adamyk2023).

Additionally, the ethical deployment of AI in FCRM necessitates a commitment to sustainability. Banks should consider the environmental, social, and governance (ESG) implications of their AI deployment in FCRM, ensuring that the use of AI contributes positively to sustainable development goals (SDGs) (Oyewole, Reference Sophia2024; Lim, Reference Lim2023). By aligning AI applications with ESG principles, banks can enhance their reputation and build trust with stakeholders, which will lead to a more resilient and sustainable financial ecosystem (Oyewole, Reference Sophia2024; Lim, Reference Lim2023).

In the nutshell, UK bank employees have increasingly recognised that AI integration is a pivotal change agent in the ecosystem of FCRM, which does provide significant enhancements to operational processes and compliance functions. Employees covered in the selected studies observe that AI can streamline the detection of sophisticated financial crimes such as money laundering and fraud while reducing manual error and reliance on outdated rule-based systems (Balcıoğlu, 2024; Kapate et al., 2025). UK bank employees are actively shaping responsible AI by establishing dedicated governance bodies, embedding ethics into development lifecycles, and upskilling workforces. Across the UK banking sector, staff-driven working groups, Centres of Excellence, and ethics boards are being established as key mechanisms for balancing innovation with the regulatory, operational, and reputational risks of AI. The senior managers of these banks are upbeat about ensuring robust security and safety mechanisms to address FCRs.

These findings underscore the necessity for targeted AI adoption strategies that accommodate varying levels of trust and acceptance among banking employees. Financial institutions should implement AI responsibility and ethics training programmes to bridge the gap in AI literacy and foster confidence in AI’s regulatory compliance. Furthermore, inclusive policy development is imperative to ensure gender-equitable discourse on AI ethics and integrate diverse professional perspectives in AI governance (Gebru, Reference Gebru, Dubber, Pasquale and Das2020). These views do reflect an underlying optimism about the role of AI in not only managing FCRs but also ensuring financial integrity and stability.

8.6 Conclusion

AI uses advanced analytics to identify, assess, and mitigate potential threats. Banks are using AI-powered systems to conduct analysis of patterns and behaviours in transaction data, which could be overlooked by humans. With unprecedented capabilities, the AI systems are used in the detection of subtle anomalies, in real time, which could exhibit fraudulent activities. This reduces the incidence of financial loss and enhances consumer trust (Balcıoğlu, 2024; Nguyen et al., Reference Sheptycki2023). Although there is increasing recognition of the potential for AI to mitigate FCRs by improving ‘the accuracy, speed, and cost-effectiveness of AML processes’ (Goldbarsht, Reference Ryan-Mosley and Heikkilä2023, p. 61), significant challenges of using AI in FCRM such as data quality, bias, privacy, interpretation, collaboration, big data, liability, and costs persist. These undermine innovations in AML compliance.

Essentially, the sustainable and responsible use of AI in FCRM hinges on a commitment to the ethical and legal principles of FEAT. By harnessing these principles, banks can enhance the transformative potential of AI while mitigating the associated risks and ethical concerns. This approach enhances operational efficiency and decision-making as well as fosters trust among stakeholders and promotes a more equitable and sustainable financial landscape. The integration of AI in FCRM must be guided by a robust ethical framework that prioritises fairness, education, transparency, accountability, and human oversight. By effectively addressing the challenges and harnessing the opportunities of FCRM and AI, banks and related financial institutions can create a more resilient financial ecosystem. As this chapter is the first academic piece that explores AI and FCRM along with the FEAT, future research could explore these elements in a comparative study to enrich the discourse in this area for enhanced financial crime countering and integrity. The future of FCRM hinges on striking a fine balance between leveraging technological advancements and maintaining ethical standards and regulatory compliance.