Developing a Loan Recommendation System using Genetic Algorithm

The digital world is flooded with an overload of digital contents, creating challenges for users the tasks of finding, chosing, and purchasing. Recommender systems, acting as intelligent filtering mechanism that compile a catalogue of potential preferred items for users. This greatly minimizes the time users need to spend choosing from the extensive array of options on websites and streamlining the entire process. To circumvent the intricacies and time demands associated with conventional statistical and computational programming, intelligent methods have gathered significant interest in various financial research domains, with a particular focus on optimizing decision-making within the banking sector. Nevertheless, the problem of selecting an optimal lending decisions to maximize the banking revenue is still considered as a substantial and enduring challenge. Recommendation generation is a difficult job because there are some challenges engaged such as like cold start and sparsity that curb the production of correct and high quality recommendation. Genetic algorithm based recommendation systems mitigate such issues by generating paramount recommendations without utilizing the similarity metric. In this work, we have developed an efficient genetic algorithm based loan recommendation system that can suggest potential borrowers that are eligible to get the desired loan. On the other hand, this is a system that can maximize the fitness function and thereby the bank’s overall profitability.