The consumer lending domain has increasingly leveraged Artificial Intelligence (AI) to make loan approval processes more efficient and to make use of larger amount of information to predict their applicants’ repayment ability. Over time, however, valid concerns have been raised about whether decisions made about individuals using these data-driven technologies can lead to bias against women. In an attempt to assess the fairness of an algorithm, 21 prominent definitions of fairness have been proposed by the computer science community over the years. However, what remains absent is consensus on which definitions are suitable for assessing gender equality in consumer lending. There is also a lack of knowledge on how to appropriately implement these metrics in practice. To tackle the problems mentioned above, this research has investigated how automated loan approval processes can be assessed for gender equality. Two essential elements for assessing predictive tools were identified and investigated through a separate research question: What fairness metrics are suitable for assessing gender equality in consumer lending? How can the metrics be applied to observe gender bias in lending history data? Based on the questions above, the research was conducted in two stages: Stage 1 focused on analyzing the prominent definitions of group fairness, but before doing so, it conceptualizes gender equality in consumer lending by conducting an extensive literature review encompassing domains of philosophy, economics, gender studies, and history. In investigating the first research question, it is found that group fairness metrics are a measure of distributive justice. These metrics are based on three different statistical criteria commonly known as independence, sufficiency, and separation and each underlie different moral assumptions which should be verified based on the application scenario at hand. In Stage 2 of this work, the second research question was investigated by conducting an exploratory case study in which a logistic regression model is built to classify a sample of loan applicants in an open source dataset. Both the dataset and the model are then tested for bias using IBM’s open source Python AIF36 toolkit. After applying the group fairness metrics, it was found that the choice of separation and sufficiency can have different repercussions for each demographic group in the dataset. When false distribution of utility is under inspection, sufficiency advantaged male applicants more than the female applicants while separation advantages males more than females. Such inconsistency highlights the importance of realizing how relevant distribution of harm/benefit depends on the choice of fairness criteria made by decision makers. Lastly, the research provides an extensive discussion on possible root causes of bias and some recommendations to managers and data stewards on how to tackle bias issues that stakeholders may face in the context of consumer lending.