Data Analytics for Credit Risk Modeling

Strengthen Credit Risk Models

As consumer habits and practices continue to evolve, traditional credit score data is no longer enough to go on when attempting to build more accurate credit risk models. Instead, achieve far more successful lending decisions by incorporating de-identified data, outlining the footprints left behind by actual consumers as they transact through their everyday lives.

Envestnet | Yodlee’s de-identified user data gives you a representative view into how millions of consumers are managing and spending their money. Understand how things such as cash flow, ACH, direct deposits, NSFs and late fees can assist in building robust cohort models. Supplement the Envestnet | Yodlee Credit Risk API with Envestnet | Yodlee data to enable you to make smarter decisions around individual creditworthiness. With Envestnet | Yodlee Data Analytics, your credit risk models become more powerful and enable less risky lending decisions.

Learn how Envestnet | Yodlee’s data can help you build better credit risk models. Contact us today.

With Our Data, You Will…

Save Time

Easily import a representative, normalized, de-identified alternative dataset on consumer spending behavior

Find Signals

Detect nonlinear relationships between spending and credit default

Manage Risk

Improve default and delinquency prediction

Lend Efficiently

Grow your lending base by lending smarter

Build Better Credit Scoring Models

With near real-time updated data, Yodlee Data Analytics helps you to make better lending decisions by incorporating actual account information into your credit risk models. Better understand how consumer purchasing habits can improve credit risk modeling and start lending smarter today.

Additional Data Analytics Features

Data

Income, cash flow, bill payment history, investments and more

Geo-location

Filter data by city, state, zip code or region, where available

Category

Filter consumer expenditures by category

Timelines

Data updated daily, in near real-time; sample historical data