Accurately identifying individuals is at the heart of every robust KYC (Know Your Customer) and KYB (Know Your Business) programme. For firms operating in financial services, understanding who a customer or partner really is—despite variations in name spelling—is critical to mitigating risk.
According to Saifr, whether it’s Katherine Smith opening a brokerage account, renting an apartment, or delivering services via your platform, determining if she’s also a known criminal or listed on a watchlist requires more than a basic identity check.
Historically, firms have relied on manual reviews of structured data such as government watchlists and sanctions databases. However, these only represent around 20% of internet data and can be slow to update. With thousands—or even millions—of customers to screen, manual processes quickly become untenable. Many organisations resorted to risk-based triaging, reviewing only select customers periodically, leaving significant gaps in coverage and compliance exposure.
The integration of artificial intelligence into KYC/KYB systems is now revolutionising risk detection. AI enables ongoing monitoring of not just structured sources, but also unstructured data, which accounts for 80% of online content. Companies like Saifr are leveraging large language models (LLMs), natural language processing (NLP), and machine learning (ML) to scan 230,000 sources from 190 countries in 160 languages in real time. This continuous surveillance supports better risk identification at scale, including distinguishing between different risk types such as fraud versus violent crime. With accurate AI training, these models can resolve adverse media to a specific identity—even if the name is spelled differently.
The challenge lies in matching the correct person to data amidst massive linguistic variability. Take the name “Katherine”—derived from Greek origins and used across many cultures—it has dozens of spelling variations. These include common misspellings (Kathryn, Catharine), insertions (Katheriine), deletions (Katherin), transpositions (Katherien), and even keyboard errors (Jatherine). Each name component—first, middle, last—can generate hundreds of permutations, making it difficult to determine whether two records refer to the same person.
Addressing this requires powerful name-matching algorithms. But building one is not straightforward. Effective models need to balance two key concepts: recall (identifying as many true matches as possible) and precision (ensuring those matches are correct). If a system prioritises recall to avoid missing threats, it may generate more false positives. Conversely, a precision-heavy approach may overlook true risks.
The answer lies in hybrid algorithmic approaches. These combine phonetic matching, character-level similarity, and context-aware scoring to determine the likelihood that two different names belong to the same person. Sophisticated AI systems must process millions of such comparisons in seconds, weighing similarity metrics ranging from string overlap to vector space modelling—techniques that estimate proximity based on abstract semantic relationships.
Ultimately, the stakes are high. Firms that get this wrong risk compliance breaches, reputational damage, or enabling criminal activity. By adopting advanced AI-powered solutions, businesses can navigate the complexity of name variations and strengthen their defence against financial crime.
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