AI and Compliance in Payments: Addressing Regulatory Challenges

AI Brings Efficiency, Security and Transparency to Compliance and Faster, Smarter Onboarding to Merchants

Compliance with regulatory requirements is an essential element of the daily activities of every financial institution. Every regulation, every requirement, every tiny procedural detail is a potential exposure, and businesses spend billions trying to navigate it all and ensure compliance with the regulatory requirements. In highly regulated environments such as finance, AI has tremendous advantages.  Enter artificial intelligence. AI doesn’t get tired, doesn’t make careless mistakes, and, most importantly, it implements the rules in a consistent manner. It learns, it adapts, and it applies compliance policies with machine-like precision over and over again. From automated KYC/KYB to adapting to region-specific compliance standards, the power of AI is undeniable. 

As part of our Rapyd series “Future Ready: The Guide to AI and Payments,” this article explores how businesses can use AI to meet compliance challenges and how payment solution providers can use AI to support smarter, faster and friction-free onboarding experiences for merchants across industries. 

Payments Meet AI-Driven Decision-Making

AI isn’t a black box that companies can deploy and forget. To unlock its full potential, businesses need to make AI systems explainable—transparent, auditable, and free from bias. Regulators aren’t interested in outcomes anymore; they want to understand the how and why behind AI-driven decisions. As scrutiny increases, companies that can clearly demonstrate fairness and accountability in their AI models won’t just meet compliance standards; they’ll set them.

Ensuring Ethical AI Use: The Need for Employee Training

Companies must invest in ongoing employee training to use AI responsibly. Teams need to understand AI’s strengths and limitations and when human oversight is essential to ensure ethical decision-making. 

A key aspect of responsible AI implementation is clarity. Policies must be translated into precise, actionable prompts that guide the system’s behaviour. However, AI should never operate unchecked. Human reviewers must remain involved, making the final decisions and ensuring compliance with ethical standards.

Best practices include:

• Develop a comprehensive AI policy to prevent AI models from being trained on confidential, proprietary, or sensitive company data, such as financial statements, business strategy, and other critical information.
• Ensure AI systems comply with standards such as PCI and SOC2.
• Avoid the use of PII in AI model training.

Businesses can confidently deploy AI systems for identity verification and compliance by focusing on these areas while safeguarding data integrity.

Regional Variations in AI Regulation

Regulatory frameworks for AI and compliance in payments vary across jurisdictions, requiring businesses to adopt flexible and region-specific strategies:

• European Union: GDPR enforces strict data privacy standards, while the AI Act will categorise AI systems by risk level, imposing additional controls on high-risk applications.
• United States: A fragmented landscape, with states like California implementing stringent data privacy laws while others are less regulated.
• China: Regulations prioritise state control, presenting unique challenges for businesses operating in this jurisdiction.

Global companies like HSBC navigate these complexities by tailoring AI systems to meet specific regional requirements, ensuring compliance without sacrificing operational scalability.

Compliance with Europe’s AI Act

The European Union’s AI Act introduces a comprehensive framework designed to ensure the safe, ethical, and trustworthy deployment of artificial intelligence systems while promoting innovation. Applicable to EU-based and non-EU providers, the Act mandates compliance across a broad range of AI applications, including customer support, transaction monitoring, and Know Your Business (KYB) and Know Your Customer (KYC) processes. The Act uses a risk-based classification system, categorising AI systems as unacceptable, high, limited, or minimal risk, with high-risk systems subject to stringent obligations. Non-EU companies must appoint EU representatives, and compliance entails robust technical documentation, adherence to standards, and transparency measures.

Steps for compliance involve conducting risk assessments, adhering to emerging technical standards, establishing quality management systems, and engaging with regulatory authorities. Organisations must align their governance frameworks with ethical AI principles, foster AI literacy, and leverage EU resources for support. 

Streamlining Know Your Business (KYB) and Anti-Money Laundering (AML)

AI can streamline Know Your Business (KYB) processes and Anti-Money Laundering (AML) checks. By continuously analysing data, machine learning models detect suspicious activities in real time, ensuring compliance with anti-fraud measures. Companies that integrate AI into their compliance strategies can reduce risks, save costs, and ensure a competitive edge.”

AI enhances KYC/KYB efforts by automating verification processes, ensuring customers and partners accurately identify and operate ethically. This helps payment providers meet regulatory requirements. It also provides value to partners and merchants. 

Improving the Merchant Onboarding Experience with AI

Industry leaders in payments are developing AI-enabled systems that make identity verification and customer onboarding faster, easier and more secure. Rapyd has robust AI-enabled systems designed to simplify KYB and onboarding for merchants, enabling referral partners, independent sales organisations and software vendors to add B2B and B2C payment capabilities to their offerings while reducing onboarding friction. 

Partners gain a competitive edge by accelerating merchant onboarding while unlocking new revenue streams. By using AI, they can efficiently serve businesses in high-opportunity industries such as gaming, online trading, affiliate marketing, and content creation. 

Merchants gain an advantage through faster time to revenue. Being able to onboard faster means they can start transacting and earning faster. That builds loyalty for Rapyd and for our channel partners. 

Challenges and Considerations

AI is a powerful tool for compliance, but it also raises some concerns. The challenge isn’t just about what AI can do; it’s about what it should do. That means asking the right questions: Who oversees AI-driven decisions? How do wpe keep systems transparent? Where do we draw the line between innovation and regulation?

Some considerations for financial services companies and channel partners are:

• AI must follow the rules, too: AI systems themselves must comply with relevant financial regulations, just as the businesses using them do.
• Explainability: AI cannot be a black box. Companies must maintain transparency in how AI makes decisions.
• Balancing innovation and compliance: Pushing the boundaries of technology is important, but not at the cost of regulatory missteps.

And then there’s the problem of bias. AI isn’t neutral; it reflects the data it is trained on. Without human oversight, it can reinforce the very biases it was meant to eliminate. That’s why:

• Human involvement is essential: Ethical AI use requires people to actively monitor and correct bias.
• Clear policies prevent loss of control: Companies must define exactly how AI should be used and ensure employees don’t unknowingly introduce risks.
• Data access must be limited: Machines should only process and share information within strictly defined parameters.
• Unapproved tools are a liability: Sensitive data should never be processed in systems that haven’t been vetted, as the risk of leaks, even internal ones, is too high.

AI is not a set-it-and-forget-it technology. It requires careful stewardship. The companies that get this right can comply with and navigate regulations efficiently and precisely.