Quantifying risk by analyzing the exposure and probability of loss in a customer interaction is becoming a must for any advanced risk solution. It is also a difficult burden to bear and remains a premium feature in most point solutions.
However, quantifying customer risk one factor at a time is itself a risky proposition. The reason: risk factors compound each other in a nonlinear fashion. For example if someone’s transaction has a 5 percent risk of being fraudulent and 10 percent chance of unintentional default, siloed analytical engines will treat it in one of three ways:
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