Predicting fraud risk from customer online properties

This interesting paper presents the results of an investigation into how well a digital footprint, properties associated with online interactions with a business such as platform and time of day, can be used to predict risk of non-payment for a pay-on-delivery shopping business.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3163781

The properties that are predictive are not, by themselves, all that surprising: those who shop in the middle of the night are higher risk, those who come from price comparison web sites  are lower risk, and so on.

What is surprising is that, overall, predictive performance rivals, and perhaps exceeds, risk prediction from FICO (i.e. credit scores) — but these properties are much easier to collect, and the model based on them can be applied to those who don’t have a credit score. What’s more, the digital footprint and FICO-based models are not very correlated, and so using both does even better.

The properties collected for the digital fingerprint are so easy to collect that almost any online business or government department could (and should) be using them to get a sense of their customers.

I’ve heard (but can’t find a reference) that Australian online insurance quotes vary in price based on what time of day they are requested — I suppose based on an intuition that procrastination is correlated with risky behaviour. I’d be grateful if anyone has details of any organisation that it using this kind of predictive model for their customers.

Businesses like Amazon require payment up front — but they also face a panel of risks, including falsified payments (stolen credit cards) and delivery hijacking. Approaches like this might well help in detecting these other kinds of fraud.

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