Radix Analytics Pvt Ltd

Calibration of Expert Scorecard by ML Methods

Radix Analytics Pvt Ltd > Machine Learning > Calibration of Expert Scorecard by ML Methods

Issues & Objectives

  • For the first time in India, a scorecard was developed for the client to keep vigil on the listed companies to avoid potential financial disaster

  • Scorecard was based on financial as well nonfinancial events such as auditors, board of directors, litigation, news etc. 

  • The task was to refine expert scorecard with ML methods

Challenges

  • Listed and unlisted flag was incomplete in the database

  • Many companies had large number of missing data

  • Frequent modification of event logic

  • Running ML models and processing score with new weights took several hours posing a challenge to multiple iteration

Project information

Skills

Advanced Statistical Techniques 

Client

Corporate Data Aggregator 

Domain

Scoring Models

Location

India

Solution

  • Decision tree, Random forest and Gradient boosting were used to obtain weights of the events

  • ML methods were run in h2o

  • Models for listed and unlisted companies were built

  • Separate weights for listed and unlisted companies were used to arrive at the consolidated score of parent companies

Benefits

  • Discriminatory power of the calibrate scorecard was found to be higher than the expert scorecard

  • Apply a decision overlay which enhanced the predictive power of the scorecard

  • Better separation between GOOD and BAD companies in modelled score

GBM Score

Expert Score