Customer behaviours in retail banking have changed in recent years. Since 2014, customer willingness to switch bank accounts has doubled. Banks have various response options to sustain customer loyalty. However, those potentially migrating customers need to be identified beforehand. This can be done via AI-driven analysis of master data and transaction data. In 2016, Spanish bank Banco Santander published a public tender for this customer „churn case“, which was analysed by more than 5,000 data analysis teams worldwide.
COREai participated in the tender and tested the two AI models gradient boosting and deep learning, comparing their results on various dimensions. The article elaborates on COREai‘s analysis strategy.
In terms of the results, gradient boosting surpasses deep learning regarding its predictive accuracy and performance. The comparison was quantified by using a misclassification rate, which represents the proportion of incorrectly predicted data points and customers. During testing the deep learning method misprognosed 23% of the data, while gradient boosting only came to wrong results in 3.3% of the cases. Additionally, gradient boosting outperformed deep learning with shorter learning and prediction times. Also in terms of interpretability of the results, gradient boosing excels over deep learning, due to its easier interpretable functional structure using individual decision trees instead of one complex layered neural network, as is the case with deep learning. This potentially enables more effective drawing of conclusions regarding the reasons of a customer’s account termination.
Taking those findings into account, the authors argue that gradient boosting is a more promising solution for use cases, as the one defined by Banco Santander. Nonetheless, no general statements about the dominance of either of the models can be made, as it is dependent of the use case’s data patterns. For example, deep learning remains unchallenged in terms of image or speech recognition.
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