Bayesian Choice Model for Developing a Recommender System in Fintech

Music City Center 

Fintech companies use advanced algorithms on non-traditional data to assess creditworthiness but access to credit is often restricted by supply-side barriers such as limited financial infrastructure, high costs, strict documentation, and demand-side barriers like poor financial literacy, financial instability, and cultural concerns. We aim to promote financial inclusion in this process by developing a multi-step Bayesian choice model that considers loan defaults conditional on a loan product, a marginal model for the different loan products, and an intricate prior regularization to handle high dimensionality. The motivating application includes high-dimensional data from tens of thousands of customers, bringing major computational challenges. To ensure fairness, we conduct a counterfactual analysis by simulating random product assignments and studying the connection between product selection and default outcomes. This approach confirmed the model's ability to perform reliably even for unseen data patterns. Addressing bias and default risks with high-valued performance metrics, the model provides a practical solution for sustainable lending practices using mobile footprint data.

Fintech

Bayesian choice model

Financial inclusion

Counterfactual

Mobile footprints 

Business and Economic Statistics Section