Prediction of Credit Risk();

Problem Solved

Traditional known Parameters used for Prediction

Manual Rules

Automating and Learning changing Behaviors

Solution

Fine Tuned to Individuals or Corporates

Non-Traditional Parameters

End to End Automation

Benefits

Better Accuracy through Machine Learning

Train better and faster for Credit Risk Automation

Reduction of NonPerforming Assets (NPA)

Approach

Build of Features from Data Set during Data Preparation Stage

Transform data

Build model

Algorithm Used

  • Neural Networks
  • Random forest algorithm
  • Bayesian statistics
  • Support Vector Machine