Moody's Machine Learning Case Study
Carlos Salas
Portfolio Manager and Data Scientist
Let’s see how a credit rating agency used machine learning model training. Join Carlos Salas as he explores how data science and machine learning can be used in finance to improve credit scoring tools.
Let’s see how a credit rating agency used machine learning model training. Join Carlos Salas as he explores how data science and machine learning can be used in finance to improve credit scoring tools.
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Moody's Machine Learning Case Study
5 mins 12 secs
Key learning objectives:
Understand how Moody’s use machine learning
Overview:
Moody’s is a credit rating agency who compared their internal parametric model against several machine learning models to assess the credit risk of small and medium-sized corporate borrowers. This assessment yielded surprising results, with the machine learning models outperforming Moody’s model.
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How has Moody’s used data science?
Moody’s is a credit rating agency who compared their internal parametric model against several machine learning models to assess the credit risk of small and medium-sized corporate borrowers. Moody’s proprietary model uses a traditional parametric general additive framework (GAM). The alternative machine learning models used were random forest, boosting, and neural networks. Two different datasets were used for the comparison study. The first dataset utilises only firm information and financial ratios, while the second dataset adds additional data to the first such as behavioural information including credit line usage, loan payment behaviour, and other alternative data.
Generalisation results were impressive with machine learning models outperforming Moody’s proprietary model from 2 to 3% using both datasets. There is also an 8-10% jump in accuracy when adding loan behavioural information.
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Carlos Salas
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