Fuzzy matching is a technique used to identify similar but not identical data entries by accounting for slight variations, errors, or misspellings. Unlike exact matching, which requires two data points to match perfectly, fuzzy matching uses algorithms to measure the degree of similarity between them, enabling the detection of close matches that might otherwise be missed. 

In the financial crime compliance space, fuzzy matching is essential for identifying individuals or entities involved in suspicious activities. For example, criminals may use slight variations in names or addresses to evade detection by Anti-Money Laundering (AML) systems. Fuzzy matching allows compliance teams to flag these variations and link them to known risks, such as sanctions lists or politically exposed persons (PEPs). 

Fuzzy logic, which handles uncertainty, underpins fuzzy matching by allowing systems to evaluate the degree of similarity, enhancing both techniques in identifying hidden patterns in financial crime compliance efforts.