Defining Alternative Data

Borrower behavior is evolving faster than many conventional risk models are built to accommodate. Coupled with economic uncertainties driving a deeper demand for credit, lenders need broader, more dynamic views of financial behavior than that found in traditional credit data to help improve credit decisioning and access. 

Today, there’s no shortage of data available that can expand your view into consumers’ creditworthiness. But as diverse as new data types are, they also lack a common language to discuss them—creating friction when vendors and lenders attempt to align on their basic definitions and uses. 

To help finance organizations gain a clearer understanding of new data types, the “Defining Alternative Data” report provides definitions, examples and trends of the data types most heavily impacting modern credit underwriting.

Expand and safeguard credit decisioning with an understanding of “alternative” datasets. As traditional credit scoring may no longer be solely relied upon to assess creditworthiness, lenders are increasingly turning to expanded credit datasets to make more confident decisions. Those hoping to leverage non-traditional data need to understand the different kinds available, including which are trusted and proven to help improve credit decisioning.

Read the infographic to learn defining characteristics, examples and the use trajectory of datasets like:

• Traditional credit data
• Alternative data
• Open banking data
• Emerging & specialized data