Minimize Transliteration Errors
Missing a watchlist match is costly and dangerous. Legacy solutions to name matching consist of static lists of known spelling variations of names. Unfortunately, this approach is bulky, expensive to maintain, and ultimately unreliable. Rosette instead intelligently compares names based on linguistic, orthographic, and phonological algorithms, preventing false negative errors and protecting against expensive KYC violations.
In addition to finding the correct match regardless of transliteration errors, Rosette mitigates the other common errors that lead to missed matches such as misspelling, phonetic misunderstanding, and nicknames, solving thirteen name phenomena across fifteen languages.
Intelligent Name Matching
Rosette names search presents all likely matches for each search query based on a predetermined, customizable match score threshold. Possible matches are ranked from most to least likely, allowing your staff to review the results and investigate any suspicious records further.
A hybrid solution, Rosette’s trusted blend of machine learning and traditional name matching techniques has improved name search for the world’s thorniest name matching challenges for over 15 years.
The best-of-breed solution must deliver not only faster, more accurate results with fewer errors, but also possess the inherent scalability to meet the ever-increasing complexity and volume of names on external and internal watchlists.
- Never Miss a Match: With Rosette, you can accurately match names and assess risk better than you would with clunky legacy solutions.
- Optimize Workflow and Increase Productivity: With the greater accuracy Rosette offers, you can eliminate false positives, allowing your compliance team spend less time on reviewing results manually.
- Search across Multiple Languages and Scripts: With fluency in 15 languages, Rosette enables you to search for names in their source languages, including those with non-latin alphabets.
Frequently Asked Questions
Is Rosette customizable to my needs?
We understand that different customers have different requirements. Rather than deciding what degree of similarity equates a match ourselves, we've made the match threshold tunable so you can look at only the most likely matches, or all possible matches.
Does Rosette support transaction screening in Russian, Arabic, and Chinese?
Yes. Global identity and financial investigations now depend on cross-lingual matching and linguistic expertise across non-Latin script language, including "difficult" languages such as Russian, Arabic, Japanese, Chinese, Korean.
Why can’t we rely on our current approach of generating variations to match misspelled names?
A three-component name translated into English can have hundreds of variations. In a climate of escalating international regulation and penalties and when even a single failure to match a single name could jeopardize your customer, it is too risky to rely on outdated methods that do not accommodate all world languages and linguistic mastery.
Can Rosette adhere to watch list requirements originating from foreign countries?
Yes, we have customers all over the globe. Every country where you do business sets it's own KYC standards. Your product needs to reliably screen names against each and every list.
Will my current solution scale affordably as name matching demands grow?
Probably not! As watchlists grow, the number of name matches grows exponentially for name-generation solutions, but not for knowledge-based solutions like Rosette. Your customers need a solution that absorbs change, with minimal additional hardware or cost.
Is there a way to increase accuracy and cross-lingual matching functionality in the product we have now?
There are name matching products designed to seamlessly layer on top of existing functionality without adding a lot of additional hardware. Rosette easily plugs into many of the most popular search tools in the industry.
Can Rosette match names even when components are entered into the wrong fields?
Yes, and your solution needs to as well. When your customers are held accountable for accurately matching names, it is risky to rely on, for example, a bank employee in Chicago knowing that a Mexican customer’s surname is often two words, where one may be mistakenly entered as a middle name.