Watchlist Screening Hidden Risk Calculator
How many names does your screening never see?
You might be missing names and reviewing duplicate alerts because of data errors in your customer records: duplicates, names hidden in joint holdings or in address fields. See what it's costing you ↓
How FinScan Achieves Better Results
A different approach to matching
Most screening tools match whatever data they're handed. FinScan fixes the data first, then matches with precision.
Entity resolution
"John & Mary Smith" is made up of two people. A beneficiary's name in an address line is a person your software never screens. FinScan parses joint holdings and free-text fields to surface every name. That is the blind spot we account for.
Precision matching
Matching tuned on clean, resolved entities with field-level screening produces 3x to 8x fewer alerts than incumbent tools. We use 5x in this model, the average across deployments. Fewer false positives, and nothing true slips through.
De-duplication
The same customer often exists 3, 4, 10 times across systems, and every copy generates its own alert. FinScan collapses duplicates into single entities before screening, so 20 to 40% of a typical alert queue simply never gets created.
Methodology
How the number is calculated
Every figure traces back to inputs and benchmarks from real deployments.
1
Based on your own data and costs
Records, alert rate, and analyst cost come from you. Everything is calculated from those three numbers.
2
Benchmarks from real deployments
Duplication, joint-holder, and address-line rates are industry benchmarks displayed in the calculator. They are conservative defaults. In a POC, we replace them with measurements from your book.
3
The dollar figure only counts analyst time
Eliminated alerts × 5 minutes each × your analyst's hourly cost. New alerts created are factored into your cost, so you see the net result only. The other costs of hidden names we leave to you to estimate.
4
Many more risks remain unpriced
There is no honest way to price your next regulatory penalty, so we show how many names sit outside your coverage today. Public AML failure settlements have run from ~$72K to ~$2.98M.
Why care about hidden names?
Regulatory findings
Examiners look at whether every name in your book was screened. A gap in coverage is a finding on its own, even when no sanctioned party turns out to be involved.
Lookback remediation
When a coverage gap is identified, the remedy is usually to re-screen historical records. That work often costs an organization more than the penalty attached to the finding.
Reputational damage
A public screening failure affects how regulators, partners, and customers view your compliance program. This cost is real, and no calculator can put a figure on it.
Onboarding drag
Analysts working through duplicate alerts have less time for genuine review. New customers wait longer for a decision while that queue clears.
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FinScan addresses our regulatory and financial crime risks on a global scale, providing exceptional accuracy for the most complex screening scenarios.
James Parker
Risk & Assurance Director
FinScan helped us reduce the amount of work and reviews in our head office and dramatically increase efficiencies. Our AML compliance remains transparent and resilient.
Mariana Hori
Senior Compliance Manager
We needed a compliance tool that meets very high standards across a variety of areas. We've built a great partnership with FinScan that's helped lay a foundation for our growth.
Robbi Nagel
Deputy Global Chief Compliance Officer
Recognized by industry analysts. Trusted by the world's largest companies.
FAQs
The duplication, joint-holder, and address-line rates are benchmarks drawn from FinScan implementations across each industry, and they are set on the conservative side. They are illustrative figures rather than a commitment about your results. The calculator displays each one next to your inputs so you can see what it is doing. In a savings review, we replace them with rates measured from your own book.
Because any figure we produced would be a guess about your next enforcement outcome. Public OFAC settlements give some sense of scale. Coverage and data-quality failures have settled at roughly $72K (Uphold, sanctioned-country data left unscreened in address fields), $402K (Western Union, agent locations never screened), and $2.98M (Microsoft, incomplete customer data and screening gaps). Each of those figures sits well below the statutory maximums, which run from $44M to over $1B, and penalties rise with the size of the book and the extent to which a gap was known. The calculator gives you the count of unscreened names so your team can assess the exposure against your own risk appetite.
It is the average FinScan achieves compared with other screening solutions, and measured deployments range from 3x to 8x. The reduction comes from matching against de-duplicated, resolved entities instead of raw records. In one published deployment, a payment processor operating in 15 countries reduced false positives by 62% without losing a single true hit.
They are names that exist in your data but never reach your screening engine. Two common sources are secondary holders in joint accounts, where "John & Mary Smith" is screened as a single name, and people or entities recorded in free-text address lines. Regulators treat cases like these as screening coverage failures whether or not a sanctioned party was involved.
The result is still directionally useful. Across deployments, FinScan regularly uncovers hidden names and removes duplicate alerts in books their owners considered clean, drawing on five decades of data quality work at Innovative Systems. Every assumption in the calculator can be adjusted to reflect what you already know about your data. If you would prefer measured figures to benchmarks, we can run a proof of concept on your own records.
Yes. That is a proof of concept, and many of our customers ran one before moving to FinScan. You see measured results on your own records, which gives your team a basis for the decision beyond the benchmarks on this page.
See this on your data
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Walk through your calculator results with a screening specialist
Replace the benchmarks with measurements from your book
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Insights
