AI is moving from experimentation to operational reality across banking, financial services, and insurance. After a year in which real-world AI deployments accelerated, 2026 is shaping up to be a defining moment for AML and KYC functions.
According to Saifr, momentum around agentic AI – systems capable of autonomous task execution – is building alongside more established generative AI models. Together, they are beginning to redefine what modern RegTech can achieve.
Financial institutions have been quicker to embrace AI than their reputations for conservatism might suggest. Research from S&P Global found that 54% of financial services firms had deployed AI initiatives by January 2025, up from 40% a year earlier. That steady rise signals not just curiosity, but commitment.
As AI tools mature, the compliance function – traditionally reliant on manual checks, rules-based engines and fragmented systems – is increasingly in scope for transformation.
AML and KYC processes are particularly ripe for change. Many existing workflows are highly structured and rules-driven, especially in document verification and onboarding. Yet these legacy approaches often struggle to interpret unstructured data or contextual signals.
AI models, by contrast, can analyse vast volumes of structured and unstructured information, detect patterns and flag anomalies at scale. The result is the potential for greater accuracy, improved efficiency and faster customer onboarding without sacrificing regulatory rigour.
One area where this shift is expected to be especially visible is Banking-as-a-Service (BaaS). As BaaS platforms continue to expand, traditional banks are providing regulated infrastructure to digital-first brands that may not themselves be subject to the same regulatory frameworks. This dynamic creates additional layers of AML and KYC complexity, particularly around fraud detection, transaction monitoring and data security. As regulatory scrutiny intensifies, partner banks are likely to look to AI-powered RegTech tools to manage risk, automate secure task handoffs and maintain oversight across distributed ecosystems.
At the same time, the broader regulatory AI ecosystem is beginning to take shape. Rather than deploying isolated tools, chief compliance officers are increasingly exploring platform-based approaches that integrate seamlessly with core banking and IT systems. End-to-end visibility across onboarding, verification and reporting processes has long been an ambition.
Agentic AI may now bring that ambition within reach by orchestrating complex workflows across multiple systems. However, institutions must still weigh “build versus buy” decisions carefully. Data sensitivity, architectural constraints and long-term governance considerations mean due diligence remains essential to avoid costly compliance failures.
With expansion comes responsibility. As AI becomes embedded in AML and KYC operations, ethical considerations are moving to the forefront. Concerns around bias, explainability, privacy and workforce impact are no longer theoretical.
Institutions are expected to prioritise fairness in model training, reducing the risk of discriminatory outcomes in identity verification or transaction monitoring. At the same time, AI’s ability to enhance fraud detection could modernise AML frameworks that have remained largely unchanged for decades.
Transparency will also be critical. A lack of clarity around how AI models reach decisions has historically slowed adoption. Greater emphasis on explainability, robust auditing and strong governance frameworks will be necessary to build trust among regulators, customers and internal stakeholders. Data privacy and consent frameworks must evolve in parallel, particularly as autonomous AI agents become more commonplace in compliance operations.
Adapting to these trends will require coordinated effort. Financial institutions must remain alert to regulatory developments, technological breakthroughs and shifting risk landscapes. Change management will play a central role, ensuring compliance teams are supported as AI’s footprint expands. Collaboration, too, will be key. Carefully controlled proofs-of-concept and sandbox testing can help institutions evaluate new partners and technologies while mitigating risk.
As AI becomes more deeply woven into the fabric of financial services, 2026 is likely to mark a year of both expansion and refinement for AML and KYC. Institutions that combine agility with accountability – investing in responsible innovation while maintaining strong governance – will be best positioned not merely to keep pace with change, but to help define the next phase of regulatory transformation.
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