Fuzzy matching in financial compliance is a crucial tool that assists institutions in identifying non-exact matches in data, thereby enhancing the accuracy and effectiveness of sanctions screenings and reducing the risk of overlooking potential red flags.

It is important to consider “fuzzy matching” as names might be missed if only exact matches are screened. Rather than flagging records as a ‘match’ or ‘non-match’, fuzzy matching identifies the likelihood that two records are a true match based on whether they agree or disagree on various identifiers. 

Fuzzy matching describes any process that identifies non-exact matches. They are often tolerant of multinational and linguistic differences in spelling, formats for dates of birth, and similar data. A sophisticated fuzzy matching system may have a variety of settings that enable greater or lesser fuzziness in the matching process.

Fuzzy matching software solutions identify possible matches where data, whether in official lists or in firms’ internal records, is misspelled, incomplete, or missing. False positives are considered one of the biggest issues with performing the fuzzy matching process. The use of an efficient system helps in raising fewer numbers of false positives.

An efficient system will identify:

• acronyms
• reversal of name 
• variation of name 
• phonetic spellings
• inadvertent misspellings
• use of specific abbreviations such as using ‘Ltd’ instead of using ‘Limited’
• insertion or removal of special characters, punctuation, spaces
• different name spelling such as spelling ‘Elisabeth’ as ‘Elizabeth’, 
• shortening of names such as ‘Elizabeth’ matches with Betty, Beth, Elisa, etc.

Fuzzy Matching in Financial Compliance

Here are some of the fuzzy matching techniques that may be implemented in the institution. 

Levenshtein Distance (or Edit Distance)

The Levenshtein Distance (LD) is one of the fuzzy matching techniques that measure the distance between two strings, with the given number representing how far the two strings are from being an exact match. The higher the number of the Levenshtein edit distance, the further the two terms are from being identical.

Hamming Distance

Named for American mathematician Richard Hamming, the Hamming distance (HD) is quite similar to Levenshtein, except that it is primarily utilized in signal processing, whereas the former is often used to calculate the distance in textual strings. This algorithm uses the ASCII (American Standard Code for Information Interchange) table to determine the binary code assigned to each letter in each string to calculate the distance score.

Damerau-Levenshtein

This LD variant finds the minimum number of operations needed to make two strings a direct match, using single-character distance operations such as insertion, deletion, and substitution. Damerau-Levenshtein goes one step further by integrating a fourth possible operation, the transposition of two characters, to find an approximate match.

Metaphone 3

Metaphone converts any string into an encoding based on the sounds present and outputs an all-alphabet code.

The major advantages of Metaphone include:

• Considering the entire string while generating code for a string
• Code length has no restriction. The large pool of words can be standardized without many collisions.

Name Variant

Different name-matching methods are best suited to solve different name-matching challenges. There are different ways to match names, but none is considered a universal solution. The name-matching software to be used should have the capability to perform a hybrid of multiple methods to address the maximum number of variations in names.

• Common key method: These methods reduce names to a key or code based on their English pronunciation, such that similar-sounding names share the same key. For example, Cyndi, Canada, Candy, Canty, Chant, and Condie share the code C530.
• List method: This method attempts to list all possible spelling variations of each name component and then looks for matching names from these lists of name variations. For example, the name John may have a different name list to be used, including John, Jon, Joan, etc.

Final Thoughts

Fuzzy matching is a crucial tool in data analytics, offering an advanced approach to identifying non-exact matches that account for linguistic variances, multinational spellings, and common errors. These systems, sophisticated in design, handle data discrepancies such as inadvertent misspellings, abbreviations, and phonetic variations, ensuring a comprehensive match analysis. However, with its capabilities come challenges like the potential for false positives.

To optimize this method, institutions often employ techniques like the Levenshtein Distance, Hamming Distance, and Metaphone 3, each catering to specific matching needs. Notably, when addressing name variations, a blend of techniques, from the common key method to the list method, proves invaluable. As data continues to evolve, the sophistication and precision of fuzzy matching remain paramount.