1. With only 29% of financial institutions currently using generative AI for financial crime detection, what are the primary barriers to adoption, and how can banks overcome these challenges?
The adoption of generative AI in financial crime detection remains limited, largely due to several key challenges. First, there is a natural reluctance within teams, driven by concerns about AI’s complexity, uncertainty about its practical benefits, and fears of job displacement. Additionally, legacy technologies pose a significant barrier. Many organizations still rely on outdated governance, risk, and compliance (GRC) systems that are costly to maintain and incompatible with modern AI solutions, making integration appear both daunting and expensive.
Regulatory uncertainty adds to the complexity. The compliance landscape constantly evolves, and teams are struggling to keep up with new requirements. Adding AI into the mix can feel like adding another process that may involve a steep learning curve – leading teams to be reluctant to test out AI waters. On top of that, AI depends on high-quality, consistent data to work effectively. Yet, many financial institutions deal with siloed and fragmented datasets, which can undermine the success of any AI-driven solution.
Overcoming these challenges starts with a strong data foundation. Banks must prioritize cleaning up and integrating their data systems to ensure they’re ready for AI. Additionally, having the right technology to support AI powered solutions is a must. Legacy systems can hold back banks. Modern regtech tools, particularly cloud-based solutions, offer a way to bridge the gap between legacy systems and new technology by enabling scalability, flexibility, and interoperability without the need for costly system overhauls. These tools integrate seamlessly with legacy GRC systems, reducing operational costs while improving efficiency.
Equally important is investing in training. Teams need to feel confident in using AI tools and the onus will be on executives and leadership teams to explore options that support in upskilling team members to ultimately transition away from error prone and slow processes to more efficient workstreams. In taking the time and effort to upskill, it will provide employees with a first-hand experience of the benefits of AI and ease the fear of AI replacement. Finally, transparency is key. As regulations around AI inevitably emerge, tools with robust reporting and audit capabilities will position banks to be able to smoothly steer any regulatory guidelines and drive long-term success.
2. Given the complexity of AI “black box” models, how can banks leverage AI while maintaining transparency in areas like AML where regulatory oversight is critical?
Understanding how AI makes decisions is non-negotiable for regulators and compliance teams. Banks must prioritize explainability to meet these expectations and build trust internally and externally.
To address this, AI models should be designed to offer clear, understandable, and readily available clarifications for information provided, if needed. This is especially important in anti-money laundering (AML), where transparency is at the root of remaining compliant and avoiding regulatory scrutiny. AI tools should also be capable of generating detailed reports that use standardized and consistent language, making it easier for compliance teams and regulators to interpret and validate the results.
Advanced reporting capabilities are equally important. Banks must establish internal principles for AI reporting standards, ensuring that reports are transparent, actionable, auditable, and aligned with regulatory requirements. These reports should provide insights that compliance teams can use to refine their processes and enhance decision-making.
Another key element is regular monitoring and auditing. AI can act as a valuable ally, improving real-time detection of illicit activity and potential risks; however, it is only as good as the data it pulls from. Having dedicated members regularly monitor and verify information output is key. This oversight ensures that AI systems remain accountable and effective over time.
By ensuring models are explainable, implementing robust reporting tools, and maintaining regular monitoring, banks are on the right side of utilizing AI to enhance compliance while being in good shape to meet regulatory standards. This approach enables financial institutions to unlock the potential of AI while keeping trust and transparency at the core.
3. Can you outline a framework that banks can use to adopt AI tools in a way that minimizes risk, ensures compliance, and meets regulatory demands for transparency?
Banks that continue to rely on outdated technologies and manual processes for compliance will simply fail to keep pace in today’s rapidly evolving regulatory and competitive landscape. The inability to adapt not only exposes institutions to heightened compliance risks but also erodes their competitive edge. To thrive, financial institutions must embrace AI as a critical enabler of agility and efficiency in compliance operations. However, successful adoption requires the right tools, robust processes, and a structured framework to ensure seamless integration and minimize risk.
The first pillar of this framework is data management. High-quality data is the foundation for effective AI. Banks must prioritize robust data collection and management practices, ensuring accuracy, consistency, and reliability. AI can also be deployed to automate repetitive tasks like data extraction, validation, and entry, reducing errors and creating a strong data infrastructure to support advanced compliance solutions.
Next, banks need to focus on assessment and planning. A thorough evaluation of existing compliance processes is essential to identify areas where AI can deliver the most value. From there, institutions should create a clear roadmap for AI adoption, outlining goals, timelines, and resource requirements to ensure alignment with business priorities.
Training and change management play a vital role in ensuring a smooth transition. Compliance teams must be equipped with the knowledge and skills to effectively use AI tools and change management strategies should address any internal resistance while fostering trust in the new technologies.
When it comes to implementation and integration, leveraging cloud-based regrech SaaS tools is crucial. These tools are designed for seamless integration with legacy systems and can be scaled to support AI-powered functionalities, including client onboarding, monitoring, detection, and reporting.
Finally, banks need to instill the process of regular updates and reviews of AI models to ensure ongoing accuracy, adaptability, and alignment with changing regulations.
By implementing a structured framework and the right tools, banks can transform their compliance processes, minimize risks, and maintain a competitive edge in an increasingly dynamic environment.
4. How can AI solutions be deployed to not only meet strict KYC requirements but also improve overall productivity and enhance the customer experience for banks?
Today’s customers demand quick, seamless processes, especially when it comes to financial services. Banks are under immense pressure to meet these expectations, and digitization and automation have become critical to delivering the efficiency and speed clients now expect. AI-powered solutions offer a transformative opportunity for banks to not only meet strict know your customer (KYC) requirements but also significantly enhance productivity and improve the overall customer experience.
One of the most impactful applications of AI for compliance teams in banks is in automating onboarding processes. By automating data collection and authentication, AI eliminates the inefficiencies associated with manual workflows. It can also detect and reconcile duplicate data in real-time, ensuring a smoother and faster client onboarding experience. This streamlined process reduces friction for customers while maintaining strict compliance standards.
AI also plays an important role in enhancing accuracy and consistency. Through automation, tasks such as data extraction, validation, and entry are executed with precision, drastically reducing the likelihood of human error. This level of accuracy not only meets regulatory expectations but also builds trust with clients by minimizing delays and errors in their interactions with the bank.
In terms of operational efficiency, AI delivers measurable gains. AI-driven document management solutions can reduce processing times by as much as 50%, accelerating onboarding and reducing the need for manual intervention. This efficiency translates to significant cost savings, as banks require fewer resources to handle routine compliance tasks.
From the client’s perspective, AI-driven solutions transform the customer experience. Faster processing, fewer touchpoints, and more accurate handling of information lead to a smoother journey. Moreover, AI provides a holistic view of a client’s business activities, enabling banks to make faster, more informed decisions. This not only enhances service delivery but also strengthens relationships by offering a more personalized and proactive approach.
By leveraging automation and intelligent solutions, banks can deliver faster, more efficient services that meet today’s expectations while laying the foundation for future growth.
5. What role can AI play in future-proofing compliance operations in the banking industry, and what strategic steps should banks take to ensure long-term success with AI-driven compliance tools?
A recent survey by Fenergo revealed that nearly half (48%) of banks globally have lost clients due to slow or inefficient onboarding processes, with 45% attributing these issues to poor document and data management. This is not a sustainable model for banks operating in a fast-paced, highly competitive environment. If banks want to grow and retain their client base, they must address these inefficiencies and align with customers’ expectations for speed, digitization, and seamless service. AI is paramount in achieving this transformation while simultaneously future-proofing compliance operations.
AI can redefine regulatory compliance by making processes such as AML and KYC more systemized. These technologies can detect illicit activity in real-time, allowing compliance teams to respond proactively rather than reactively. This shift empowers banks to meet and exceed regulatory requirements while improving operational agility.
Proactivity is key to future-proofing compliance. AI enables continuous monitoring and reporting, ensuring compliance teams can adapt to market and regulatory changes faster and more precisely. Additionally, AI tools can predict potential risks or shifts in the regulatory landscape, giving banks a strategic advantage in staying ahead of compliance demands.
Looking at AI adoption, I believe that 2025 will be the year when generative AI will become commonplace for content creation and business processes across the board. Agentic AI systems will autonomously plan and take actions to meet user-defined goals, potentially automating an increasing amount of day-to-day work decisions. The impact AI will have on productivity will be unprecedented.
Investing in AI-powered regtech innovations ensures readiness for ongoing advancements and long-term success. Automating routine compliance tasks reduces risk exposure and frees up resources for higher-value activities. Advanced reporting tools driven by AI provide actionable insights through data analytics, enabling compliance professionals to create new reporting queries and improve overall decision-making.
- About Niall Twomey
- About Fenergo
Niall is the Chief Technology Officer at Fenergo and is responsible for technical strategy, design and architecture. He leads core teams within Fenergo to create solutions that deliver impactful ROI for global financial services clients.
Prior to working with Fenergo, Niall spent over 10 years working in financial services product development and system integration roles at Barclays Capital, Fidelity Investments and Accenture.
Fenergo is the leading provider of AI-powered Client Lifecycle Management (CLM) solutions that digitally transform how financial institutions, asset management and fintech firms, corporates, and energy and commodities onboard and manage clients throughout their client lifecycle. Its software digitally orchestrates every client journey from initial Know your Customer (KYC) and client onboarding, automating regulatory compliance and enabling continuous monitoring throughout the client lifecycle (transaction monitoring, perpetual KYC), all the way to client offboarding.
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