Your consumer data is an invaluable source of insight and decision-making intelligence.
When combined with Dun & Bradstreet’s expertise in building automated analytic tools this data can deliver more effective customer acquisition, extract greater value from existing relationships, and optimise the way you extend credit.
Dun & Bradstreet develops models that can automate, improve and reduce the costs of decision making and customer management.
Accept and decline
Utilise the information collected during the credit application process, such as personal details, credit files and customer verification data, to inform decisions on whether to accept or decline a customer, and under what credit conditions.
Leverage payment history data to support ongoing customer management decisions, such as identifying customers for collections activity, marketing campaigns and ‘on the spot’ authorisation.
Identify which applicants, at either end of the application process or during account review periods, are most likely to use a high amount of credit
Churn and profitability
Identify which of your applicants are most likely to remain on the books, those which will move on quickly and undermine the cost of acquisition, and those which are likely to be the most profitable.
Geographic Risk Index
Individuals residing in areas with higher incidence of adverse information are much more likely to default in future.
Dun & Bradstreet’s Geographic Risk Index (GRI) is a credit risk indicator, based on geographic locations, that indicates how likely a consumer is to have an adverse credit history relative to the Australian population.
Accessible as a standalone solution or integrated with credit bureau enquiries, the GRI is particularly predictive of future adverse behaviour for younger demographic and new-to-bureau consumers.