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, nonprime consumer segments and small businesses.
Goals and Objectives
Expand lending portfolios by ingesting a wider set of data to identify and underwrite loans for worthwhile nonprime 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/ML platforms, Big Data, and cloud
Use Case Summary
Rely on aggregate alternative data in addition to traditional data to identify lower-risk nonprime segments for lending. Data can also be used to prevent fraud and for predictive portfolio management.