As financial markets have evolved and become more digital, the ways of marketing, communication and customer service have also adapted to the times. Customers expect a higher standard from all industries, financial services included. On top of being a nuisance to unsatisfied customers, poor customer service costs industries globally ＄338.5 billion in potential revenue losses per year. The industry with the highest losses is financial services, with about ＄44 billion lost per year (Genesys, 2009). Systems utilizing customer data for the purpose of improving business relationships with customers through better customer communication and service are called Customer Relationship Management (CRM) models (Chen & Popovic, 2003). Part of CRM operations is lead management. Lead management in particular is the set of methodologies, systems and practices with the aim of helping to better service existing clients (retention) or discovering and bringing in potentially new clients (acquisition). This project is performed with the goal of improving the lead management system in the private banking department of ING. More specifically, the goal is to improve the identification of the best leads weekly from the pool of all available leads to be sent out to customer contact teams. The methodology consists of the integration of machine learning algorithms into the lead management infrastructure for the purpose of scoring leads in order to improve the selection process, leading to improved customer communication as well as revenue potential. Additionally, more information was put into the decision making by considering the performance and preference of the customer contact teams. Due to the usage of data modelling, a review of relevant compliance measures with regards to GDPR was performed, with an additional measure of decision explainability being proposed for this project as well as all projects using machine learning algorithms.Three different algorithms were tested, with the best one selected based on performance being a random forest model. The model was tested against the existing lead selection method for 6 weeks, and showed considerable and consistent improvement in performance from the third week onwards (up to 16%). The random forest was more flexible over the weeks and based on analysis of decision interpretability made on particular model decisions, benefitted largely from the inclusion of team performance. In fact, the team which was handling the lead proved to be the most important factor in the decision making of the model. Preference did not seem to have any particular impact on the performance of the leads and thus was omitted from the final model. Overall, based on the results of the testing, the use of machine learning algorithms was shown to significantly improve the performance of the lead management system, based on better lead selection and the consideration of team performance. For future research, it is suggested to implement machine learning techniques in the lead generation step (rather than after), in order to reduce the information restriction the algorithm faced in the case of this project.