The Role of Web Browsing in Credit Risk Prediction

Online mail order and online retail purchases have increased rapidly in the last five years worldwide, with Covid-19 forcing almost all non-grocery shopping to move online. These practices have increased the availability of new data sources, such as web behavioural variables providing scope for innovation in credit risk analysis and decision practices. This paper examines new web browsing variables and incorporates them into survival analysis as predictors of probability of default (PD). Using a large sample of purchase and repayment credit accounts from a major digital retailer and financial services provider, we show that these new variables enhance the predictive accuracy of probability of default (PD) models at account level. This also holds in the absence of credit bureau data, therefore, the new information can help people who may not have a credit history (thin file) who cannot be assessed using traditional variables. Moreover, we leverage on the dynamic nature of these new web variables and explore their predictive value in short- and long-term horizons. By adding macroeconomic variables, the possibility for stress-testing is provided. Our results provide insights into web-browsing behaviour, highlight how the inclusion of non-standard variables can improve credit risk scoring models and lending decisions and may provide a solution to the thin files problem