As financial crime risks intensify and regulators demand proof over policy, 2026 is set to be a defining year for KYC. Signals from FATF, the FCA and the EBA point to deeper scrutiny of onboarding quality, beneficial ownership, governance and the effective use of technology across the customer lifecycle.

Adverse media screening has evolved far beyond simple name checks against search engines. In today’s regulatory and reputational risk environment, an effective adverse media screening system must be intelligent, multilingual, explainable, privacy-aware, and continuous.

As 2025 comes to a close, it is worth reflecting on a year defined by major regulatory shifts, rapid technological evolution, and significant milestones for smartKYC. Across global compliance, expectations continued to rise, driving firms towards more automation, multilingual intelligence extraction, and perpetual monitoring. Against this backdrop, smartKYC experienced one of its strongest and most transformative years to date.

The UK legal profession is entering a very different era of anti-money laundering (AML) regulation. With the Financial Conduct Authority (FCA) becoming the sole AML and counter-terrorist financing (CTF) regulator for professional legal services replacing the Solicitors Regulation Authority (SRA), law firms will soon be held to the same expectations that banks have lived with for years.

In the high-touch world of private banking and wealth management, time is money, and so is reputation. Every client interaction matters. Yet one of the most frustrating friction points in the client journey remains onboarding: a process notorious for its delays, documentation overload and lost opportunities.

In today’s increasingly complex regulatory environment, adverse media screening is no longer a tick-box exercise, it is a frontline defence against reputational, legal and financial risks before they escalate. However, how you screen matters just as much as what you screen for.

In the fast-evolving world of compliance and due diligence, understanding the difference between screening and monitoring isn’t just semantic, it’s strategic. For financial institutions, regulated industries, and global corporates, understanding these two pillars of Know Your Customer (KYC) is critical to building robust, proactive risk management programs.

In today’s compliance landscape, adverse media screening is no longer a regulatory nice-to-have, it’s now a necessity. Financial institutions, multinational corporations, and high-risk sectors depend on open-source intelligence (OSINT) to identify reputational and financial risks before they escalate. Yet, as digital footprints become increasingly traceable, a new challenge emerges: how can organisations screen without revealing who or what they’re screening?

In the constantly evolving world of financial crime compliance, Know Your Customer (KYC) processes remain both indispensable and increasingly complex. As institutions face growing regulatory demands, exploding volumes of data, and a constantly shifting threat landscape, artificial intelligence (AI) has emerged as a powerful tool to automate and enhance due diligence.

In the pursuit of Know Your Customer (KYC) and broader financial crime and third-party risk compliance, open-source intelligence (OSINT) has emerged as a vital weapon. By definition, OSINT refers to data collected from publicly available sources, such as news websites, blogs, company registries, court databases, and social media. Its value lies in its ability to surface risk-relevant information that traditional data providers may miss, especially in relation to reputational, political, legal, or ESG-related concerns.

In today’s high-risk, hyper-connected world, adverse media screening is no longer a “nice to have”, it’s a regulatory and reputational necessity. From financial institutions to multinational corporations, the need to detect early warning signs about clients, suppliers, and counterparties has never been more urgent. With regulators demanding continuous due diligence and the volume of multilingual data across global media growing exponentially, traditional methods simply can’t keep up.