Glossary: Fuzzy Matching | Fuzzy Search Matching

Fuzzy matching is a data comparison technique that is commonly used to identify non-exact matches between two data sets. Unlike exact matching strategies (which usually require identical values for a match), fuzzy matching uses algorithms to assess the similarity between strings, numbers or records. This approach makes it possible to identify likely matches in situations where the data is misspelt, incomplete or incorrectly formatted.

The Fuzzy matching method is used within the AML (anti-money laundering) compliance industry to compare data and identify similarities between values that may not be exact. This means that businesses and organisations can find approximate matches in names, addresses or other text fields — even when there are spelling mistakes or slight differences in the data. This is what makes the fuzzy matching technique so necessary within the AML compliance field.

Why is fuzzy matching so important for AML compliance?

Fuzzy matching is primarily used for companies seeking to be AML compliant. It enables you to easily identify potential matches between names, entities or transactions that are not an exact match, but are similar enough to warrant further investigation. This approach helps financial firms to detect and prevent money laundering activities from occurring by recognising variations in spelling, typos, abbreviations and other naming conventions.

Fuzzy matching plays a critical role in AML compliance for the following reasons:

• Criminals use name variations: Money launderers and sanctioned individuals often alter their names slightly to evade detection.
• Human errors in data entry: Spelling mistakes, typos, and inconsistent data formatting can lead to missed matches if only exact matching tactics are used.
• Global variations: Different languages, transliterations, and naming structures (e.g., first name vs. last name order) require flexible matching techniques.
• Regulatory requirements: AML regulations require financial institutions to implement robust screening processes that minimise false negatives and missed matches.

In the world of regulatory compliance (particularly with Know Your Customer (KYC) checks, AML checks and sanctions screening checks, exact matches are not always sufficient. People’s names can appear in many forms across different databases, and data entry errors are common. However, fuzzy matching reduces the risk of missing high-risk individuals by flagging up close name variations, which is vital for businesses wanting to meet their UK compliance obligations.

How does fuzzy matching work?

In most cases, fuzzy matching uses a range of advanced algorithms to calculate the similarity between data entries. The most common fuzzy matching algorithms include:

• The Levenshtein Distance: The Levenshtein Distance measures how many single-character edits are needed to change one string of characters into another string.
• The Jaro-Winkler Distance: The Jaro-Winkler Distance gives a higher similarity to strings that match from the beginning.
• Soundex/Phonetic Matching: The Soundex method (otherwise known as Phonetic Matching) compares words that sound alike but are spelled differently.
• Token-based Matching: Token-based Matching breaks down data into components (known as tokens) and compares them individually.

If you decide to undergo thorough fuzzy matching as part of your approach to fraud prevention, your chosen provider will likely use a combination of these methods.

What are the challenges of using fuzzy matching?

Fuzzy matching is usually perceived as a positive tool, but it can have several challenges, such as:

• False positives: False positives occur when legitimate entities are classified as suspicious, increasing the manual review workload.
• Performance issues: Comparing large datasets with fuzzy logic can be resource-intensive.
• Threshold tuning: Setting the right similarity threshold is crucial to balancing accuracy and efficiency.

However, your fuzzy matching algorithms can be adjusted to reduce the chances of false positives by:

• Setting confidence thresholds: For example, by only flagging matches above 85% similarity. 
• Using machine learning methods: Machine learning tactics can refine results based on past match reviews.
• Combining multiple techniques: Combining multiple fuzzy matching techniques (e.g., phonetic matching and the Levenshtein Distance) can give you greater accuracy.

How can SmartSearch help with your fuzzy matching?

SmartSearch empowers businesses to make smarter, faster decisions with precision and confidence. By leveraging advanced variable fuzzy matching technology, we can enhance match detection by capturing name variation similarities, leading to a more comprehensive and accurate screening process. 

This ensures compliance with practices recommended by the FCA (Financial Conduct Authority), enabling organisations to detect subtle discrepancies like fraudulent entries or input errors. As a result of this approach, modern businesses can reduce risks, improve regulatory compliance and streamline their business operations, saving valuable time and resources.

Please contact an AML and compliance expert if you'd like to see how we can help your business stay compliant with the law.