Choosing Between Structural and Machine Learning Models for Loan PD

Summary

The note compares traditional approaches to estimating the probability of default on commercial bank loans with machine learning methods. It names Merton and KMV as structural credit-risk models and random forests as a possible machine learning alternative. The response characterizes the Merton model as useful for ranking borrowers by risk but criticized for accurately quantifying default risk, then recommends considering machine learning or deep learning.

It also stresses that credit modeling requires business understanding alongside quantitative methods: flexible or opaque models may have limits when users do not understand the underlying borrowers and lending context. The answer gives an example of a recurrent neural network using time-series inputs such as amounts due and monthly income, but reports no validation metrics or comparative evidence. Although the question asks about regulatory preferences, the response does not discuss regulations, model governance, interpretability requirements, or when one approach is permitted.

Key ideas

• The note contrasts structural credit models such as Merton and KMV with machine learning approaches.
• The response says Merton may rank credit risk more effectively than it quantifies default probabilities.
• Machine learning models can use borrower time series such as amounts due and income.
• Model choice should account for business understanding as well as predictive methods.
• The response does not address regulation or provide comparative performance evidence.

Tags

# Calculating PD of commercial bank loan


# Calculating PD of commercial bank loan












I have two main options to calculate PD of a loan in a commercial bank; with and without machine learning.

On one hand, there are traditional methods such as Merton or KVM. On the other hand, I could use machine learning with random forests.

What are the pros/cons of using vs. not using machine learning? Are there any regulations inclined on one or the other?

## Answer by Dhruv Mahajan (score 1, accepted)

https://quant.stackexchange.com/a/45712

Merton model has been highly criticized in academic literature for its accuracy, though it provides good ranking of credit risk, it fails to quantify it. I'd say use machine learning or better yet deep learning. I used a recurrent neural network with time series inputs like amount due and changing monthly income among many more. It provided a good estimate but modelling credit risk is not just a quantitative task, black box models can only get you so far unless you understand the business you are investing inside-out

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