How can alternative data support your business?

  • Assess the creditworthiness of customers with little or no prior credit history.
  • Identify new segments of unbanked prospective customers.
  • Combine standard and alternative data to achieve the best performance. 
  • Improve the accuracy of profiling and segmentation of your portfolio.

Mobile and Internet Wallet Behavior

Multiple datapoints from smartphones


Utility payments

Home bills and contracts


Banking transactions

A full history of movements


Mobile Data


Social Media Data

There are two approaches to the development of alternative models depending on the use case and regulation:

Standard Risk Scorecard

These logistic regression models can be considered as an industry standard. They provide high robustness and interpretability.

Machine Learning Models

These models (e.g. random forest, neural network, etc.) are more complex and thus they tend to have a higher predictive power and a lower degree of interpretability.

Key Benefits

Facilitate Customer Acquisition

Alternative scoring will increase a pool of customers for whom you will be able to accurately assess risk.

Drive Financial Inclusion

By facilitating access to finance for segments of unbanked or underbanked customers.

Gain Competitive Advantage

By extracting valuable information from datasets unavailable to your competition.