AI-Based Consumer Credit Underwriting : The Role of A National Credit Database

Consumer credit providers increasingly rely on algorithmic credit underwriting, deploying Artificial Intelligence (AI) tools such as Machine Learning (ML), to predict consumers' credit risk.Lenders' turn to algorithmic underwriting was initially facilitated by credit data sharing regimes, originally established in the United States and which have now become prevalent in many other jurisdictions as well. A key player in these credit data sharing regimes is the credit bureau - a commercial entity that collects, processes and sells credit data and various services based on this data. ‘Credit scoring’ stands at the core of these services. Under this "commercial model" of data sharing, private credit bureaus operate independent credit databases, develop scoring models and sell credit scores to lenders who weigh them in their underwriting processes. In contrast, under a "hybrid model" of credit data sharing regime, all credit data is centrally held on a national, supervised and strictly secured database, to which authorized credit bureaus enjoy exclusive access. The credit bureaus develop scoring models under strict regulatory supervision of the central bank, and sell these scores to lenders.This chapter explores how the implementation of a hybrid data sharing model may impact the benefits and risks of AI-based consumer underwriting. The Israeli regime, which has recently implemented a national database, serves as a case study