In our article last week, we showed how digital behavioural data can enhance the fraud models of insurers and lenders by boosting the ability of those models to identify inaccurate data. We also learned that such data has predictive power as an associative data element.
The answer an applicant submitted in an online questionnaire may have been perfectly accurate, but we saw that the user exhibited certain behaviours (like typing fast or correcting the field content multiple times) and it has been shown that these behaviours are themselves quite predictive.
How robust is this new data and how stable is its predictive power?
This is a new type of data being collected, so it is in very early stages for many of these business models. Discoveries are being made every day and naturally, there are challenges in teasing out the signals and deciding if and how to use the data. Consider one company's experience using the behaviours seen when entering an applicant's mobile phone number.
The company found a positive correlation for fraud when applicants entered the number many times or used the backspace key a lot. They also found a positive correlation for fraud if a user spent a minuscule amount of time on the field or typed excessively slowly. And time hovering over the field had no correlation at all. What is a company to make of these signals? Should they act on them? Should they try to interpret them?
Originally published September 10, 2020.
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