Dealing with matching of persons or contact data in general, we are all aware that individuals can make use of abbreviations or nicknames as kind of synonyms for their name. Classic examples are the usage of the name Bill for the actual name William, or like my own father is using the name Mans while officially his name is Hermanus. Most data matching engines make use of a kind of synonym table to take care of this. That can be done because within a culture or region the nicknames are quite often linked to the same names and people do not tend to use completely different official registered names.
It becomes more challenging if there is no longer a link between nickname and official name. That may happen, for example, if people move from one cultural region to another where also other writing sets are used. Take for example my chinese friend 高为民, whose Latin name would be Gao Weimin (family name first), but the moment he works in Europe or the US he is using the Latin variant William Gao. There is no common relation to the name William and Weimin both in Latin or Chinese and it they are no phonetic variants of each other. Continue reading ‘Matching persons with different official names’
Through the increase of modern technologies our payments are processed electronically more and more. Banks try to reduce costs and force their customers to carry out the payments themselves. Internet banking has become the standard. Customers no longer can deliver written transfer orders at their bank, but have to book the transfers using internet banking facilities.People can easily make a typing error in the account number that still will result in an existing account number. The risks are fully on the customer’s side. Although banks always are willing to help them to get the money returned, it’s better to avoid these errors.
In my opinion, banks should be obliged to perform a name-number-check for every payment or at least for every larger amount. Continue reading ‘Mandatory name-number-check at money transfer?’
In many cases an inductive method of data cleansing is the way to go. With the right tools and expertise you can inspect, transform and cleanse entities in a database and reach high levels of data quality without the need to use external reference data. In some cases, however, only working with the internal data and inductively identifying and fixing data patterns is not sufficient. Let’s take a practical example: a bank needs to report on a particular segment of its clients to German bank supervisor BaFin – the Federal Financial Supervisory Authority aka Bundesanstalt für Finanzdienstleistungsaufsicht. The bank apparently has done its homework and has created a central database containing all entities needed for the compliance check. Moreover, the bank has worked out a rather complex set of rules how data must be processed and corrected. One of the most important anchor points in this specific framework is the separation between B2C and B2B entities and for the latter the exact identification of the correct legal form. But what if you cannot trust this identification? Continue reading ‘Data Cleansing with intelligent identification’