Fuzzy matching in financial reconciliation

What is fuzzy matching and how does it work?

Fuzzy matching algorithms work by comparing strings of characters and determining their similarity. The acceptable deviation from a perfect match is configurable and those predetermined threshold quantify the tolerance for error. The error thresholds can handle minor differences in spelling, punctuation, word order, and other common variations found in unstructured or semi-structured data.

There are often cases where names of people are written differently, for example, “John Smith” versus “J. Smith” or addresses written using different formatting conventions. Fuzzy matching algorithms can account for these variations and still identify a match. Fuzzy matching detect variations in comparable data that are typically distinguishable to the human eye only.

How does fuzzy matching help improve match rates in transaction reconciliation?

Fuzzy matching is particularly valuable in transactional reconciliation when dealing with imperfect or inconsistent data across different systems. There are many cases where data coming from the systems to be compared is not identical, and there may be variations in spelling, formatting, or even missing data. This makes it difficult to use exact matching methods and highlights the need for fuzzy matching techniques.

How does AI determine string similarity?

AI models can be trained to detect subtle patterns. Comparing character by character under a strict logic can produce false positive exceptions. Rigid rules match data with irrefutable certainty and that is great, provided that the data is clean and uniform at its source. However, more often than not, that is not the case and traditional approaches lack the flexibility of reasoning humans possess.

As new approaches emerge, however, this frontier is beginning to blur. Algorithms that analyze text at a deeper, semantic level can justify matches that are not 100% identical. They use various techniques to assess the similarity of two strings.

One common method is the Levenshtein distance, which measures the number of single character edits (insertions, deletions, substitutions) needed to transform one string into another. Another popular technique is phonetic matching, which compares the pronunciation of words and can identify similar sounding names even if they are spelled differently.

Fuzzy matching techniques have a broad application in reconciliation scenarios

Fuzzy matching algorithms have become increasingly important in data analysis and data integration tasks, as they allow us to find connections between data that may not be obvious at first glance.

For example, fuzzy matching can help identify duplicate records in a database or match customer information from different sources. ReconArt’s system reconciliation scenario handles such processes, so we find application of fuzzy matching for string values proper in those cases.

Fuzzy matching also allows for more flexibility in data merging and integration. Often, data from different sources may use different naming conventions or abbreviations, making it difficult to merge them accurately. With fuzzy matching, these discrepancies can be accounted for, allowing for more accurate data integration and analysis.

Here are five key scenarios where ReconArt users can find fuzzy matching algorithms helpful:

Bank statement reconciliation

When matching bank transactions with accounting entries, company names or reference numbers often appear differently between systems. For example, a payment to “ABC Corporation Ltd” in one system might appear as “ABC Corp” or “A.B.C. Corporation” in bank statements. Fuzzy matching acknowledges, with a level confidence, that the records are identical, highlighting the similarities. It is up to the user to decide whether those strings shall be considered equivalent or not.

Supplier invoice matching

Purchase orders, delivery notes, and invoices often allow slight variations in item descriptions, quantities, or pricing due to data entry errors or system formatting differences. Fuzzy matching can accept that “USB Cable – 6ft Black” and “USB CABLE 6 FOOT BLACK” refer to the same item and would not produce a false exception.

Payments reconciliation

When matching incoming payments with outstanding invoices, customer references, invoice numbers, or payment amounts might have minor discrepancies. A customer might pay “INV-001234” as “Invoice 1234” or round $999.99 to $1000, and fuzzy matching can still establish a connection between these transactions.

Multi-currency transaction matching

Exchange rate fluctuations and rounding differences can make exact matching challenging. Fuzzy matching with tolerance ranges can match transactions that are essentially the same, slight amount variations aside.

Intercompany reconciliation

When reconciling transactions between subsidiaries, differences in chart of accounts, coding practices, or timing can create matching challenges. Fuzzy matching helps identify corresponding transactions even when they are recorded with different reference numbers or slight amount variations.

The science behind fuzzy matching – common algorithms used

Financial reconciliation need to demonstrate a solid and auditable logical foundation in order to preclude arbitrary match results. Fuzzy matching in ReconArt rely on mathematically sound algorithms for string values comparison. Here are some of them explained:

Levenshtein distance: Measures the minimum number of character edits needed to transform one string into another. Useful for typos and minor spelling variations.

Jaro-Winkler distance: Particularly effective for short strings like names or reference codes, giving higher scores to strings that match from the beginning.

N-gram similarity: Breaks text into sequences of n characters and compares overlap. Effective for partial matches and reordered words.

Soundex / Metaphone: Converts words to phonetic representations, useful for reconciling names or descriptions that sound similar but are spelled differently.

Token-based matching: Breaks text into words/tokens and compares individual components. Good for matching product descriptions or addresses. This approach is similar to the algorithm used in LLMs.

Fuzzy numeric matching: Uses percentage thresholds or absolute value ranges to match amounts that are close but not exact. This matching has been leveraged in ReconArt for years through a variance/percentage matching approach with decimal values.

Key benefits of fuzzy matching for financial transaction reconciliation

The efficiency of automation: Fuzzy matching dramatically reduces manual review time by automatically suggesting transactions for matching that would otherwise require human inspection and analysis.

Flexible error tolerance: Handles data entry mistakes, system integration issues, and formatting inconsistencies without breaking the reconciliation process.

Higher match rates: Achieves 85-95% automated matching rates compared to 40-60% with exact matching only for datasets that do not contain comparable data.

Scalability: Enables processing of high-volume transactions without proportional increases in manual effort.

Pattern learning: Advanced systems can learn from confirmed matches to improve accuracy over time.

Reduced false negatives: Minimizes the risk of missing legitimate matches due to minor discrepancies.

Adjustable threshold control: Allows organizations to adjust matching sensitivity based on risk tolerance and data quality.

Cost reduction: Significantly decreases labor costs associated with manual reconciliation and exception handling.

Improved accuracy: Reduces human error in the reconciliation process while maintaining appropriate controls through configurable confidence thresholds.

These benefits make fuzzy matching an essential component of modern automated reconciliation systems, particularly for organizations dealing with large transaction volumes and imperfect data quality from multiple data sources. Fuzzy matching algorithms are one of the many exclusive features of ReconArt Enterprise edition.