. What is the user's credit rating and how high is the probability of default? Has the user defaulted on payments in the past?
PSC Plausibility | identify formal & logical errors
. Users make mistakes when entering personal data such as address, phone number or bank details. Reasons are carelessness, fear of data release and more and more often fraudulent intentions. With our checks, we identify these incorrect entries before the transaction and thus prevent incorrect bookings and chargebacks (reversals).
PSC Scoring | Evaluate Data & Recommend Action
. In the context of plausibility checks, we examine whether the data can be formally and logically correct. In the context of scoring, we analyze whether the data allow conclusions to be drawn about the probability of payment default. A variety of checks are also available for this purpose.
Is the user who he claims to be?
Even if all data are plausible and the "scoring" does not allow any conclusion on a payment default, fraud can occur; e.g. if the user uses the data of a third party. To minimize the risk in this case, we offer our "confirmation" checks.
Why do you need limits?
The higher the turnover with a new user, the higher the risk of non-payment. It plays only a minor role whether the turnover results from one large or various small transactions. To minimize the risk of non-payment, it makes sense to work with turnover limits. Such limits can be set at various levels.