Knowing exactly who you’re dealing with is critical in financial services. Whether onboarding customers, managing partnerships, or verifying identities for transactions, the stakes are high.
According to Saifr, the question isn’t just whether Katherine Smith is a new brokerage client—it’s whether she’s the same person flagged on a government watchlist. With thousands of individuals bearing the same or similar names, distinguishing between them is far from simple.
Traditionally, firms relied on manual name checks against government-issued watchlists and sanctions databases. This structured data represented a small fraction of what’s available online—roughly 20%—and updates were often slow. Manually reviewing customer lists was time-consuming, expensive, and typically limited to high-risk individuals, leaving the rest exposed to potential threats for months or even years at a time.
Enter artificial intelligence. Today’s AI-powered platforms are transforming KYC (Know Your Customer) and KYB (Know Your Business) efforts. Saifr, for example, combines multiple AI models—including large language models (LLMs), natural language processing (NLP), and machine learning (ML)—to continuously scan 230,000 internet sources in 160 languages across 190 countries. By analysing both structured and unstructured data in real time, AI is helping firms monitor entire customer populations around the clock, flagging potential reputational or financial risks that humans alone would likely miss.
However, name matching remains one of the most technically complex parts of the process. Take the name Katherine. Derived from the Greek word “Katharos”, meaning “pure”, this name has hundreds of variants across cultures and languages. These variations include phonetic differences (“Kathryn”, “Catherine”), typos (“Katherin”, “Katherinee”), letter swaps (“Katheirne”, “Katherien”), and even errors caused by mistyping on a QWERTY keyboard (“Kathwrine”, “Kqtherine”). Multiply these permutations by first, middle, and last names, and a single identity can generate over 100,000 unique combinations.
Ensuring your system accurately matches these variations means deploying a hybrid algorithm that can balance recall and precision. Recall is all about completeness—how often the model correctly finds a true match. Precision, on the other hand, measures how often the match is actually correct. A high recall rate can prevent dangerous oversights but may come at the cost of false positives. The challenge lies in optimising both, without overwhelming the system or analysts.
To do this, systems must assess a combination of phonetic similarities, string similarity scores, and contextual proximity—placing names in a vector space to identify those that are “nearby” in meaning or usage. These scores are then weighted to match the specific use case, whether prioritising safety and vigilance or avoiding unnecessary alerts. The algorithm must also scale efficiently to millions of identities in real-time environments, without compromising accuracy.
As firms navigate the complexities of global compliance and financial crime prevention, precision name-matching is more than a technical challenge—it’s a business-critical requirement. AI is not just enhancing KYC/KYB—it’s becoming essential to getting it right.
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