A general model for fair and explainable recommendation in the loan domain

Recommender systems have been widely used in the Financial Services domain and can play a crucial role in personal loan comparison platforms. However, the use of AI in this domain has brought to light many opportunities as well as new ethical and legal risks. The customers can trust the suggestions of these systems only if the recommendation process is Interpretable, Understandable, and Fair for the end-user. Since products offered within the banking sector are usually of an intangible nature, customer trust perception is crucial to maintain a long-standing relationship and ensure customer loyalty. To this end, in this paper, we propose a model for generating natural language and counterfactual explanations for a loan recommender system with the aim of providing fairer and more transparent suggestions.