Reduce human labor with artificial intelligence
Anti-money laundering (AML) due diligence requires financial crime investigators to follow up on every watchlist alert. The only way to lighten their workload is to present fewer irrelevant results. AI provides greater search precision, reducing the number of erroneous name matches compliance officers must review.
Resolving personal names, place names, and dates is vital to anti-fraud efforts, AML laws, and sanctions screening mandates. Financial services companies must resolve identities, but name matching can be challenging.
Yoko Jean Hawkinberry
Does your system find:
Y. Jeanne Hockenbury
Yoko J. Hawkinbury
Hawkenberry, Y. J.
If your system cannot spot data that includes variables such as misspellings, aliases, nicknames, initials, and non-Latin scripts, you may be unable to resolve identities.
Names of streets, cities, states, provinces, and buildings are as prone to spelling variations and errors as personal names.
|Phonetics and spelling differences
|100 Montvale Ave vs. 100 Montvail Av
|Missing address field components
|100 Montvale Ave vs. 100 Montvale
|Use of uppercase and lowercase letters
|100 Montvale Ave vs. 100 MONTVALE AVE
|Reordered address components within a field
|100 Montvale Ave. vs. 100 Avenue Montvale
|Address field abbreviations
|100 Montvale St. vs 100 Montvale Street
Your system may have difficulty recognizing and resolving these place name variations.
Finally, differences in the way information is recorded can challenge companies trying to resolve significant dates, such as dates of birth.
|Use of commas
|October 7 1997 vs. October 7, 1997
|7 October 1997 vs. October 7 1997
|Use of abbreviations
|October 7 1997 vs. Oct. 7 1997
Intelligent search technologies can help your system recognize that these are all the same dates.
Trusted by mission-critical systems
Rosette is a text analytics and discovery platform that provides enhanced name matching to mission-critical systems. These include the watchlist systems used by U.S.Customs and Border Protection, the U.K. Home Office, and major commercial AML due diligence systems. Rosette saves money by reducing the amount of human labor needed to resolve false positives (erroneous matches). It also reduces risk by catching false negatives (missed matches).
Rosette can be deployed quickly, in the cloud or on prem. By enhancing functionality within existing systems, Rosette helps companies avoid the significant work and expense entailed in ripping and replacing those systems. Name matching capabilities are customizable to your organization’s risk policy.
AI you can understand
Rosette also offers explainable AI. Of particular importance to financial institutions, explainable AI consists of methods and processes which enable stakeholders to better understand the decisions AI makes, and on which data it bases its decisions. This explainability lets regulators and others audit the match scores given by Rosette for consistency.
AML due diligence monitoring
As in watchlist screening, traditional due diligence systems are plagued by both misses and false positives. These can be especially problematic for financial institutions striving to comply with know your customer (KYC) laws and other industry AML due diligence regulations.
Somerville-based Acme Bank executive Alan Rose Gardener
The gardener created a bank of roses on the path for the home of Alan Smith.
Good fuzzy match:
Executive Allen R. Gardner was convicted of embezzling $1.6 million from Bank of Summerville.
- Entity extraction finds people, organizations, and locations in each article, so that searches on people names only look at people names, and not words that coincidentally match parts of a name (e.g., “gardener,” “rose”)
- Entity linking uses the surrounding context of a name and compares it to your knowledge base of known bad actors to link names in articles with the correct person (and not erroneously link different people with the same name)
- Fuzzy name matching overcomes more than 13 types of variations in 20+ languages
Benefits of intelligent fuzzy name matching
Unlike legacy solutions that generate static lists of name variations, hoping to match the name being queried, Rosette employs a hybrid of intelligent algorithms.
- Compares names based on linguistic, orthographic, and phonological algorithms that understand how names are formed and how they vary
- Handles disorganized and messy data
- Matches name components within a single field or in the wrong fields
- Handles inverted names and names that are incorrectly glued together
- Speedily processes names with many components
Fuzzy name matching overcomes many problems
Problem: Language barriers
Solution: Cross-lingual name search finds all mentions of a person or company regardless of the language it is written in
Problem: Word/name ambiguity
Solution: Uses the context of name in an article and compares it to known data about a bad actor in a knowledge base to correctly match name to real-life actor
Problem: Name variety
Solution: Higher precision reduces load on investigators
- Cross-lingual search in 20+ languages including Arabic, Chinese, Korean, and Russian
- Simultaneous consideration of 13+ types of name variations in each search
- Confidence-scored results for relevancy-ranked search results
- Configurable to your data and needs
- Easily integrates with existing systems
- Intelligent algorithms that understand cultural and linguistic influences on how names are formed
- Traceable match score reports
- Scalable and lightweight enough to run on a laptop
Real world benefits of improved name matching in financial services
Working with Rosette, fintech company KOHO made it easier for consumers of its prepaid, reloadable debit cards to quickly receive funds and pay bills. Benefits include:
- A 50% reduction in the number of direct deposits delayed because they required manual review
- A 25% reduction of manual labor for the risk team, equal to one full-time equivalent
- Name match scores in milliseconds
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.