AML DATA QUALITY & ENRICHMENT
Hidden names in data are hidden risk
As one of the only AML screening tools with built-in data quality, FinScan helps you identify data quality issues, uncover hidden entities, and screen imperfect records with greater accuracy, reducing false positives while helping prevent missed risk.
.png)
Customers rely on FinScan's 50+ years of data expertise
How we solve data quality challenges
Screening is only as accurate as the records going into it.
Reduce false positives
Address the data errors, duplicates, and noise that generate unnecessary matches.
Uncover hidden risk
Reveal entities hidden in joint accounts, addresses, and other problematic customer data.
Improve analyst productivity
Spend less time investigating redundant alerts and more time assessing meaningful risk.
Strengthen compliance confidence
Understand the condition of the data flowing into your compliance systems.
Data Quality Built for Real-World AML
Bad customer data impairs screening. FinScan finds the problems, then screens through them.
Clean Your Data Before It Creates Risk
FinScan provides a comprehensive enrichment of your compliance data:
Identify missing and incomplete customer information
Detect duplicate records
Find inconsistent or problematic formats
Identify individual/entity classification errors
And other errors that affect screening effectiveness
.png)
Screen Through Imperfect Customer Data
FinScan helps uncover the real entities hidden within problematic customer records, so data errors don't prevent potential risk from being identified.
Joint accounts
Names hidden in address lines
Noise words in name lines
Multiple organizations
Invalid and dummy data
Reduce Noise Without Losing Risk
Improve analyst productivity by identifying duplicate records across data sources, accounting for misspellings and variations, and applying remediation decisions across duplicate alerts, while continuing to detect the entities that matter.
From data problems to better screening.
Know what is wrong
Assess your customer data to identify errors, gaps, duplicates, and inconsistencies affecting your AML screening.
Uncover what's hidden
Enrich imperfect records and identify individuals and entities that may otherwise be missed during screening.
Get better screening outcomes
Reduce unnecessary alerts, improve analyst productivity, and strengthen risk detection.
Keep your data fit for purpose
Periodically reassess data as new sources, acquisitions, geographies, and operational changes are introduced.
Better Data. Better Detection. Better Decisions.
Compliance teams measure the difference in alerts, hours, and matches found.
A GLOBAL FINANCIAL SERVICES FIRM
46K misplaced names identified, 560K false matches eliminated
-
misplaced names, now screened, identified 407 actual sanctions matches
-
20% of false matches eliminated saved 1.8 FTE in resources
A LEADING UNDERWRITER
FinScan consistently caught more than 38 algorithmic variations
-
phonetic algorithms, date shifts, character mapping, abbreviations, concatenations, and transliteration and translation errors
A LARGE SHIPPING COMPANY
93% reduction of existing near matches
-
solved data quality issues caused by inconsistent notations
-
fine-tuned matching scenarios and precise watchlist pre-filtering
Real-time API-based screening, where automated parsing creates sub-records to catch true hits
A TECH-FORWARD SPONSOR BANK
Reduced False Positives by 74%
-
dropped noise words, incorporated spelling variations & cultural naming conventions
-
fuzzy match capture improved from 82% to 93%
Achieved 60 ms end-to-end latency per transaction
A GLOBAL FINANCIAL SERVICES FIRM
46K misplaced names identified, 560K false matches eliminated
-
misplaced names, now screened, identified 407 actual sanctions matches
-
20% of false matches eliminated saved 1.8 FTE in resources
A LEADING UNDERWRITER
FinScan consistently caught more than 38 algorithmic variations
-
phonetic algorithms, date shifts, character mapping, abbreviations, concatenations, and transliteration and translation errors
A LARGE SHIPPING COMPANY
93% reduction of existing near matches
-
solved data quality issues caused by inconsistent notations
-
fine-tuned matching scenarios and precise watchlist pre-filtering
Real-time API-based screening, where automated parsing creates sub-records to catch true hits.
A TECH-FORWARD SPONSOR BANK
Reduced False Positives by 74%
-
dropped noise words, incorporated spelling variations & cultural naming conventions
-
fuzzy match capture improved from 82% to 93%
Achieved 60 ms end-to-end latency per transaction
Built to Scale With Your Data Environment
Multiple data sources
Assess and process customer data across multiple sources and databases.
Global operations
Support data quality across business units, geographies, and evolving operational environments.
Ongoing data changes
Reassess data quality as you add sources, acquire organizations, or expand into new markets.
Existing compliance systems
Improve the quality of data flowing into your existing AML screening processes.
FAQs
AML data quality refers to the accuracy, completeness, consistency, and usability of customer data used by compliance systems for screening and risk detection.
Incomplete, inconsistent, duplicate, or incorrectly structured data can increase false positives and create a risk of missing true matches.
Common issues include missing dates of birth, missing names, duplicate records, joint accounts, names hidden in addresses, noise words, invalid or dummy data, and incorrect individual/entity classifications.
FinScan identifies problems in underlying compliance data, then helps process imperfect data so hidden entities can still be identified during screening.
Yes. FinScan is designed to identify sanctions risk despite conditions including joint accounts, names hidden in address lines, noise words, multiple organizations, and invalid or dummy data.
FinScan recommends periodic reassessment, particularly when adding data sources, acquiring a company, or consolidating operations across geographies.
.png)
.png)
.png)
.png)
