The financial services industry now witness cutting-edge technology and innovative features being introduced every day. Neobanks and fintechs now compete for the smoothest, fastest customer experience they offer – from advertising the least number of clicks to open a bank account to using artificial intelligence (AI) to inform on spending habits and credit behaviour. The inability to leverage AI can result in a competitive disadvantage, with larger institutions using AI to enhance customer experiences, reduce costs, and innovate products.
Artificial Intelligence has also made its way in anti-money laundering (AML) compliance. Financial institutions, technology companies, and third-party service providers are increasingly leveraging AI to combat illicit finance by automating the detection of suspicious activities and enhancing compliance processes. AI-driven tools analyse vast amounts of transactional and customer data in real-time, identifying patterns indicative of money laundering, fraud, or other financial crimes that may be missed by traditional rules-based systems for AML. AI implementation also helps streamline and optimise operational processes in financial crime risk management.
For smaller financial institutions looking to keep up, there are many considerations before jumping into the AI race. Especially in the high-stake industry of financial crime compliance, a sub-par implementation of AI into your system may do more harm than good, if not done right.
What are the risks and barriers when adopting AI for AML as a small financial institution?
A significant barrier is the high costs associated with implementing AI solutions, which require substantial financial investment in infrastructure, software, and talent. Smaller institutions often do not have the budget to afford these expenses.
Another barrier is the lack of expertise; developing and maintaining AI systems requires specialised knowledge in data science, machine learning, and AI technologies, which small institutions may not possess. This includes knowing the right questions to ask regarding explainability/ accuracy measures of AI models, as a sub-par implementation without these will introduce even more risks to their services.
Furthermore, AI models necessitate large amounts of high-quality data, and small financial institutions may struggle to gather, clean, and maintain such data due to smaller customer bases and less comprehensive data collection systems. Not every firm is inherently well placed to collate customer and transaction data – not to mention data from external sources – as this is necessary to generate a holistic picture of customer behaviour in financial crime compliance.
Regulatory and compliance issues also pose a challenge, as navigating the complex regulatory landscape related to AI can be daunting for small institutions with limited legal and compliance resources. Lastly, technological integration issues can arise, as integrating AI systems with existing legacy systems may be difficult and costly, requiring significant upgrades or replacements. Data protection requirements can often prevent leverage of cloud based SaaS for AI as data cannot be sent to third parties.
These barriers introduce several risks for small financial institutions. Operational risks are a major concern, as ineffective or poorly implemented AI systems can lead to operational disruptions, errors in decision-making, and reduced service quality. Compliance risks are also significant, as failing to comply with regulations due to inadequate understanding or resources can result in legal penalties and reputational damage. Data security risks also arise as insufficient data security measures can lead to breaches, exposing sensitive financial data and damaging trust.
To mitigate these risks, small financial institutions can employ several strategies:
Partnerships and collaboration with fintech companies, academic institutions, or larger banks and guidance or support from regulators in other regions e.g. the Financial Conduct Authority in the United Kingdom can help share resources and expertise. This for financial crime compliance, could mean access to an industry-standard, open-source regulated data set that encapsulates known typologies but is minus the personally identifiable information (PII).
Read about the use of synthetic data sets for financial crime compliance
Utilizing cloud-based AI services can reduce the need for significant upfront investment in hardware and infrastructure, providing scalable solutions managed by providers with expertise in AI. Investing in talent development by training existing employees in AI and data science or outsourcing AI development to specialised firms can also be effective taking into caveat to maintain data integrity and security.
Lastly, adopting a phased approach to AI implementation, starting with small, manageable projects to build experience and demonstrate value before scaling up, can help manage the transition more smoothly. Parts of the new system are activated one at a time to allow users to get used to new processes and identify any problems before the next area is implemented. If any issues arise then they are likely to be limited in scope and any lessons learned from each phase will make each subsequent stage more efficient and reliable than the last.
For organisations looking to keep up with the AI revolution, the turnkey offering of the Napier AI Continuum platform delivers a return on investment from day one, without the need for pre-existing artificial intelligence expertise in your organisation. The pre-configured solutions for Client Screening and Transaction Monitoring with auditable, explainable AI are tailored to financial institutions with lower volumes, and in need of expertise embedded into the rules libraries.
