In honour of Holger Wandt: Customer screening with high precision matching

Today, we honour the memory of Holger Wandt, our dear colleague and one of the founders of our natural language processing principles, who passed away exactly one year ago.

Holger loved to share his knowledge on a variety of topics. Back in 2016 he already spoke passionately and convincingly about the importance of High Precision Matching for financial organisations; a topic that is still very relevant 5 years later.

In honour of his legacy, we are once again sharing his blog 'Customer screening with high precision matching'. We hope this will not only inspire you, but also ensure that his knowledge lives on.

Customer screening with high precision matching

As all financial organisations must demonstrate compliance with an ever growing number of rules and legislations, many vendors of customer screening systems will state that their products incorporate automated Customer Due Diligence (CDD) processes. After all, screening customers on a regular basis to see whether there are suspicious or sanctioned individuals and organisations in the data set, is an integral part of risk management.

The real challenge, however, lies in the quality of the screening process. This process must be aimed at cost efficiency, better customer experience and operational advantages. And that is only possible if you "understand" the data you are matching.

High precision matching (HPM) is a method in which probabilistic and deterministic methods are being deployed to achieve the best possible result. In other words, high precision matching uses fuzzy logic methods combined with knowledge on names, naming conventions, legal forms, companies, abbreviations, acronyms, cultural habits and the like.

It is, in fact, the ideal method to answer to the requirements of a really sophisticated screening process.

Let's have a look at some of those requirements:

• Being able to match against a large variety of external and internal lists, even if these lists include names of persons or organisations that are not in Latin script (think of Arabic, Mandarin Chinese, Hebrew, etc). HPM is applying solid transliteration capabilities in order to come up with highly reliable results. In addition, the results of all matching against all the lists are being consolidated in to one single view.
• Generate realistic matching scores: The data in a lot of lists is often misspelled, incomplete or sequenced incorrectly (Xao Yin Pin <--> Pin Yin Xao). Furthermore, aliases and nicknames are being used, as well as all kinds of different date notation. Keeping this in mind, a  matching score of 100% (as provided by many vendors) is not realistic at all. HPM generates an accuracy score that is congruent with the quality of the records that are being compared, without missing the actual match!
• Reducing the number of false positives: If the matching tool produces matches, that are in fact no-matches (no accurate hits against the different suspect lists), a lot of manual rework is involved to actually check these false positives. With HPM, a reduction of false positives up to 90% has been proven in the field.
• Not missing the real risk (no false negatives). If a real risk is missed and not flagged for further processing, organisations will have to suffer the consequences of such false negatives.

As I said, these are just some of the requirements for a sophisticated screening process. High precision matching is the main provision in achieving cost efficiency, better customer experience and operational advantages. To learn more, please read our white paper ‘The benefits of high precision matching in automated screening’. Enjoy!