To have good customer data, a bank must first understand the sources of its data. Because of banks previously merging or establishing new business units, customer data ends up being fragmented and can be inconsistent because of the way the information was collected. For example, the same customer may be ‘Jane Doe’ on the Internet banking channel and ‘Jane Mary Doe’ in the core banking system. In one product system, their date of birth might be recorded as ‘6 November 1984’ and 1 January 1980’ in another, where the person inputting the data guessed the date rather than asking the customer and the exact date may not have thought to be important.
In some countries, individuals have a unique national identity, tax number or social security number that can gather all the fragmented customer data around one individual. However, this is only possible if these numbers are used when signing up for products, making payments, or setting up new services such as Internet banking.
In countries without a reliable or consistent individual number, it is more challenging to identify the customer. Banks use automated data-matching software where the algorithm uses a matrix of common data to best match data into a customer, such as:
The matching algorithm often uses ‘fuzzy logic’ to match data and to report possible, not full, matches for manual checking.
The matching algorithm is set to a high con-incidence to ensure that the match is accurate and tested to make sure it is accurate.
Once all customer data has been gathered, a unique ‘customer number’ is allocated to link all products and services to that customer. This is a customer number, so a joint current account for a husband and wife will have two customers, one for each party to the account.
All product and service systems and channels (including third-party systems) hold the ‘customer number’ as their unique identifier, ensuring that a two-way link is used to reconcile data.
The data held on the customer, using their ‘customer number’ is more than the products and services that they hold. Banks need to build up a history of the data to allow more advanced analysis of trends.
The data held must comply with local data protection legislation regarding the legitimacy of holding the data, how it is gathered, updated, and available to the data subjects.