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.

Creating a Single View of the Customer

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:

  • First name;
  • Family name;
  • Date of birth;
  • Mother’s maiden or unmarried name;
  • Address, including any zip or post code;
  • Product or service information.


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.

Customer 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.

It includes:

Demographic data

  • Employment details;
  • Recent previous addresses;
  • Marital status;
  • Dependents;
  • Special dates including wedding anniversary, their children’s dates of birth (where collected and relevant);
  • Hobbies and pastimes (where collected and relevant);
  • Connected ‘customer numbers’ of jointly held products.

Products and services

  • Current products and services;
  • Closed products and services;
  • Purchase date;
  • Closure date;
  • Current balances;
  • Term of product if a loan or savings product;
  • Any other relevant machine-readable information.


  • Channels used;
  • Channel preferences;
  • Frequency of usage;
  • Date of last use.

Contact and marketing

  • Customer’s stated communication channel preference for contact from the bank;
  • Marketing preferences;
  • Communication channel response;
  • Summary of all contact, response and next action and due date.

Bank-generated data

  • Current segment;
  • Previous segments;
  • Current value;
  • Future value;
  • Lifetime value.


  • Status of any complaint in progress;
  • Outcome of any previous complaints.

Collections and Recovery

  • Current state of any collection activity;
  • Previous collections activity.

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.