risks, etc. If an individual profile is applied to the individual that it was mined from, then that is direct individual profiling. If a group profile is applied to an individual whose data match the profile, then that is indirect individual profiling, because the profile was generated using data of other people. Similarly, if a group profile is applied to the group that it was mined from, then that is direct group profiling . However, in as far as the application of a group profile to a group implies the application of the group profile to individual members of the group, it makes sense to speak of indirect group profiling, especially if the group profile is non-distributive. Distributive and non-distributive profiling Group profiles can also be divided in terms of their distributive character . A group profile is distributive when its properties apply equally to all the members of its group: all bachelors are unmarried, or all persons with a specific gene have 80% chance to contract a specific disease. A profile is non-distributive when the profile does not necessarily apply to all the members of the group: the group of persons with a specific postal code have an average earning capacity of XX, or the category of persons with blue eyes has an average chance of 37% to contract a specific disease. Note that in this case the chance of an individual to have a particular earning capacity or to contract the specific disease will depend on other factors, e.g. sex, age, background of parents, previous health, education. It should be obvious that, apart from tautological profiles like that of bachelors, most group profiles generated by means of computer techniques are non-distributive. This has far-reaching implications for the accuracy of indirect individual profiling based on data matching with non-distributive group profiles. Quite apart from the fact that the application of accurate profiles may be unfair or cause undue stigmatisation, most group profiles will not be accurate. Applications In the financial sector, institutions use profiling technologies for fraud prevention and credit scoring. Banks want to minimize the risks in giving credit to their customers. On the basis of the extensive group, profiling customers are assigned a certain scoring value that indicates their creditworthiness. Financial institutions like banks and insurance companies also use group profiling to detect fraud or money-laundering. Databases with transactions are searched with algorithms to find behaviors that