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Matching Multiple Factors

If your main focus is screening business transactions, you can skip to the next blog item you wanted to read – nothing to see here for you. On the other hand, if you screen static data, like customer records or insurance policies, one way to cut down the number of […]

What’s the frequency, Kenneth?

In a recent post, I suggested that workflow design be dictated, to some extent, by volumes. If you have lots of states or work folders that are lightly used, you might be better off with rethinking how you segment your data and design your workflow. Well, the same is true […]

Assuming a wee bit of risk…

In the previous post, we laid out how firms use repeated patterns in their data and/or patterns in the matched watchlist listing to ignore matches. The upside of those strategies is that the risk, if any, is very contained if not totally eliminated. The downside is that you tend to […]

In data we trust

When we screen data, we match data patterns we know to be non-matches. If they occur frequently enough, we can choose to ignore that pattern going forward. In general, there are two generic types of false positive reduction (FPR) strategies: whitelisting, and rules-based processing. Whitelisting generally means that a particular […]