Financial institutions have long relied on legacy data sources, such as credit bureau scores, to make underwriting decisions, but this has prohibited flexibility in expanding the portfolio for lower risk, non-prime consumer segments and small businesses.
Goals and Objectives
Financial institutions will expand lending portfolios by ingesting a wider set of data to identify and underwrite loans for worthwhile non-prime segments. In doing so, they will combine traditional credit bureau scores with acquired data ranging from employment history to social media activity.
Alternative data, services, AI/machine learning platforms, Big Data, Cloud
Use Case Summary
Financial institutions will rely on aggregate alternative data in addition to traditional data to identify lower risk non-prime segments for lending. Data can also be used to prevent fraud and for predictive portfolio management.