Churning of the customer base is always a top issue in Banking. It is directly related to recurrent revenue, and the ever increasing acquisition costs for new customers. In a first approach, this issue is related to both the quality of service (which is mainly in the front-office, say the contact center) and the speed of service, which is mainly in the back-office. Many studies published to date on this required manual data collection. This creates in general two concerns: worker behavior may change under observation, and manual data collection is expensive and often error prone. In this paper it is shown by means of a case study for a Multi-National Bank (with 5000 employees in the back office) how automated Business Process Discovery, which is an advanced type of process mining, makes it possible to handle the above concerns. The automated data collection and the analysis, in terms of Hidden Markov Models, are key elements. Several results regarding the quality and speed of service have been obtained. Most interesting was the discovery of deeper root causes for customer attrition. Once the deficiencies in the processes are identified, appropriate process improvements can be designed and simulated based on the models emerging from process discovery. In this case study, significant quality and speed improvements as well as customer churn reductions have been obtained.