Synthetic Credit Data

An American multinational financial services company with a significant global presence. The company operates in 19 countries and serves over 5 million customers worldwide. It is a systemically important financial institution according to the Financial Stability Board.


Problem Statement

The bank faced challenges in creating a fair and inclusive credit scoring system, as traditional credit scoring models often relied on biased data and were prone to errors. The bank needed a solution that could generate synthetic credit data to mitigate these biases and improve the accuracy of credit decisions.

Our Solution

We developed a synthetic credit data solution that used machine learning and Gen AI algorithms to generate synthetic credit data that was similar to real-world credit data. It consisted of:

  • Data Augmentation: We used data augmentation techniques to expand the existing credit data and reduce the risk of overfitting.
  • Bias Mitigation: We used bias mitigation techniques with the help of Gen AI to ensure that the synthetic credit data was free from biases and errors.
  • Model Validation: We used model validation techniques to ensure that the synthetic credit data was accurate and reliable.

Potential Business Benefits

  • Improved Credit Scoring: The synthetic credit data solution improved credit scoring by reducing bias and improving the accuracy of credit decisions.
  • Increased Customer Trust: The solution increased customer trust by providing a fair and inclusive credit scoring system.
  • Enhanced Compliance: The solution enhanced compliance with regulatory requirements by providing a robust and transparent credit scoring system.
  • Increased Efficiency: The solution increased efficiency by reducing the need for manual data processing and improving the speed of credit decisions.
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