It seems like only yesterday that Millennials were the up-and-comers, their digitally-driven behaviours forcing change in the financial industry. Now it’s Generation Z, half of whom are already adults, and all of whom are digital natives. Meanwhile, the Alpha generation – the under 10s – are hot on their heels. 

We’re now in a market, post-Covid, where digital expectations have been pushed to new heights. Digital customer acquisition strategies are more important than ever, but financial institutions (FIs) are still seeing too many application dropouts. 

Even before the events of 2020 pandemic, Australian and New Zealand consumers were embracing banking on the go. A RFI global digital banking study shows that 73% of consumers are using a mobile app to do their banking, with a significant increase of 12% over the past two years. The uptake of mobile banking apps has risen dramatically across all demographics, bringing with it new vulnerabilities.

In the machine learning, at least for some classes of problems, a predictive model is built based on training the dataset where the outcome is known, and the same model is then used to predict the outcome from actual input data. The model may be further refined when larger numbers of data with known outcomes are available, or in a continual fashion, when a 'training' occurs on an incoming data for which the current model has failed to predict the correct outcome. I.e. the data point that has been 'trained' enlarges the training dataset thus occasioning the model to be rebuilt considering one more datum points with known outcomes, than was previously available.