Creditworthiness and Features used in Machine Learning models

Some readings on features used.

Paper #1 - Determining Secondary Attributes for Credit Evaluation in P2P Lending

Key Features

Paper #2 - Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending

Key Notes

1. Socio-Demographic: including bank history, income and debit account behavior.
2. Calling Behavior: aggregated values for number and duration of phone calls made and received on different days and at different times of the day.
3. Link-Based: counts of the number of good and bad credit card holders in each customer’s egonet.
4. Influence Score: the scores each customer obtained after two distinct influence propagation algorithms were applied to the network.

Paper #3 - Machine Learning approach for Credit Scoring

Key Notes

• LIME, Local Interpretable Model-agnostic Explanations, is a novel technique that explains the predictions of any classifier in an interpretable and faithful manner
• SHAP, which stands for (SHapley Additive exPlanation) [23], is a novel approach for model explainability which exploits the idea of Shapley regression value16 to model feature influence scoring.

Paper #4 - A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees

Key Notes

More Reads

Learning Latent Representations of Bank Customers With The Variational Autoencoder

Eliciting Social Knowledge for Creditworthiness Assessment

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