Success case

Risk prediction for non-affiliated companies in the banking sector

The challenge

The banking entity has tools to detect the risk of default and establish pre-approved credit limits. The model is accurate for companies where the bank has sufficient information. However, for those companies where there is limited information, the bank applies a restrictive expert criterion and only offers minimum limits, regardless of their economic solvency.


Expand the credit portfolio of healthy customers by incorporating non-affiliated clients who, although they do not have detailed financial information, have adequate economic solvency.

How have we done it?

  • Machine Learning
  • XGBoost
  • Feature engineering
  • Cost and optimization function
  • Characterization of clients based on their risk of default
  • Characterization of unknown clients from limited information on known clients
  • Risk Scoring


The current model only allows for 250,000 pre-approved companies to apply for credit limits. The new models improve this number up to 150,000 non-affiliated companies.