Credit scoring in retail banking traditionally involved manual evaluation of payment behavior, age, wage, gender, zip code, and other personal information. However, with the growth of financial institutions and the volume of data, machine-learning solutions have become more prevalent.
Our platform Fabric optimizes data quality to enhance credit scoring by improving data cleansing and balancing the dataset. The solution involves iterating and improving the quality of available training data while comparing the impact of different data preparation methods. Fabric leverages synthetic data and data quality profiling to measure impacts throughout the iterations, resulting in an explainable and generalizable solution.
Download our case study and learn how.