Why, Robot? - Explainability of AI models - Blog - InCube

The goal of a retail banking recommender is to make customized proposals of products such as credit cards and mortgages. We found the k-Nearest neighbours approach based on user features to be the right fit for this use case. The most common user features in a client's neighbourhood serve as explanations for recommendations. For instance, we recommend a youth savings account to a client who belongs to a neighbourhood where this product is popular and one of the common features is'Age group 18 – 24 years'. In the private banking use case, a recommender serves to provide personalized proposals of financial instruments, such as stocks and bonds.