Ensuring accurate name matching is a fundamental aspect of regulatory compliance, particularly in areas like KYC (Know Your Customer) and AML (Anti-Money Laundering). However, screening names across different languages and scripts presents a significant challenge. To address these complexities, IMTF partnered with Babel Street to enhance precision in name matching and reduce compliance risks.
IMTF recently offered a guide covering the challenges of name screening for Chinese and non-Latin scripts, and how to overcome them.
Financial institutions must verify names against sanctions lists and watchlists, a crucial step in onboarding and compliance. Unlike unique identifiers such as Social Security Numbers, names can vary significantly due to phonetic spellings, transliterations, nicknames, abbreviations, and even different name order conventions. This variability makes name screening a difficult and error-prone process, with inaccuracies leading to increased compliance costs and potential regulatory penalties, it said.
Chinese scripts and ideographs pose a unique challenge due to the complexity of characters, phonetic variations, and cultural differences. Unlike Latin-based names, which follow a standardised spelling system, Chinese names can be transliterated in multiple ways, creating further ambiguity. Moreover, the Han script is shared across Chinese, Japanese, and Korean languages, complicating differentiation when the language of origin is unknown.
Simple transliteration methods fail to provide the necessary accuracy for effective name screening. Variations in phonetic interpretation, such as the lack of a distinct “R” sound in Cantonese, further complicate the process. Without advanced technology, financial institutions risk both false positives, which increase operational costs, and false negatives, which expose them to regulatory penalties and financial crime.
Fuzzy name matching is an AI-powered technique designed to address these issues by identifying names that are similar but not identical. This method enhances accuracy by accounting for variations caused by misspellings, phonetic differences, and formatting inconsistencies. Techniques such as edit distance algorithms, phonetic matching, and statistical similarity methods all play a role in improving name-matching precision.
However, each approach has limitations. Edit distance algorithms measure the number of changes required to transform one name into another but fail to consider cultural nuances. Phonetic matching techniques, like Soundex and Metaphone, work well for Latin names but struggle with non-Latin scripts. Statistical methods offer high accuracy in cross-language applications but require extensive training data, making them less effective for real-time compliance screening.
To overcome these limitations, Babel Street has developed a two-pass hybrid method that combines multiple approaches. This system first applies phonetic transliteration to generate a broad set of possible matches, followed by statistical analysis to refine the results and improve accuracy.
IMTF’s Siron One, powered by Babel Street’s advanced Name Matching technology, supports 24 scripts, including Han characters, enabling financial institutions to manage complex name variations seamlessly. By integrating AI-driven name screening capabilities, Siron One enhances compliance efficiency, reduces risk exposure, and streamlines global operations.