What The increased amount of data paves the way for machine learning algorithms to get a better and more accurate methodology for individual insurance consulting but simultaneously increases the risk of unfair discrimination and privacy concerns. The project aims to balance the benefits of using more data for life insurance guidance with the risks associated with data protection and equality. Why The increasingly data-driven and fine-grained statistical methodology for individual insurance consulting is discordant with ethical aspects and regulatory requirements of equal treatment and data protection. On the one hand, the demand and possibility of personal preferences and behavioural influence on insurance products, insurance risk and investment increase along with the amount of data available. On the other hand, using data and optimal individual insurance coverage expose discrimination and jeopardise respect for one's private life. How The postdoc project intends to use machine learning algorithms with a balanced integration of theoretical formalisation of preferences and behaviour to obtain better consulting models while considering equal treatment and data protection.