Conventional ‘total credit’ based income assessment methodologies fail to discern critical information about clients’ income situations: the category of income, its ‘credit-worthiness’, and consistency. Furthermore, they aren’t dynamic enough to factor in customer event data. This makes it difficult to fine tune an individual’s credit risk profile at the approval stage and over the lifetime of a loan.
We developed “income verification” and “risk signals” modules to help drive credit pre-scoring processes and provide companies with early warning systems. They work based on detailed assessments of clients’ income situations and the identification of a set of transaction risk signals (non-bank loans, gambling) and events (unemployment).
A transaction classification and risk assessment engine