Most banks have automated consumer credit scoring systems. These rely on the use of statistical data to determine likely risk of default. Non- quantitative factors that will be taken into amount include location, type of residence (house or apartment), rented or owned, children, and highest education qualification. An unmarried actor living in a rented apartment who dropped out of school may well be deemed a higher credit risk than a married lawyer with two children living in a townhouse she owns. This is likely to be the case even if the actor earns significantly more than the lawyer.
In the US there is a body of legislation intended to protect consumers from discrimination by prohibiting banks from making credit decisions on a number of grounds including race, gender and location. This is the exception rather than the rule. In most countries banks make such decisions with few legislative constraints.
Credit bureaus capture financial data from many sources in order to create credit histories for individuals. They then sell the results from this collated information on the basis of specific requests from banks or other creditors. This will identify people with a poor credit history. Ironically people who have never borrowed or had a credit card may find it harder to get their applications approved than applicants who have borrowed often and frequently been late with their payments. At least the latter have a credit history.
There is always the risk that the data held against individuals is inaccurate, resulting in them being effectively blacklisted for new credit. Consumer advocacy groups have campaigned to give individuals the right to check the accuracy of data held on them. Legislation in most developed countries has been enacted to establish procedures to enable this to be done.