AnyFin Hiring Task Report

Data Science Case - Probability of Default Prediction

Name: NGUYEN Thanh-Long

Email: nguyenthanhlong1990@gmail.com

Linkedin: https://www.linkedin.com/in/longtng/

Problem Statement

The purpose of this task is to build a predictive model that assigns default probabilities to loan applications to classify customers going to default (target=1) or non-default (target=0). The noticeable problem is the data is highly imbalanced as defaulted customers contributed only 6% in total.

Data Processing and Model Building Stages:

• Data Exploration and General Pre-processing:

  • Remove Duplicates - 2,256 duplicated data points were dropped.
  • Missing values - max_dpd_120d, min_dpd_120d, loans_created_120d, has_paid_120d, payments_120d, payment_vacation_120d, has_payment_plan were dropped due to it percentages of missing values or undetermined distribution of values.
  • Check high correlation features and create new crossing-feature to remove collinearity:
    • day vs. week_of_month: drop day, consider week_of_month as the binning of day.
    • credit_used vs. mortgage_house vs. mortgage_apartment: as (mortgage_house + mortgage_apartment) < credit_used, drop all three, then create new credit_used_left = credit_used - (mortgage_house + mortgage_apartment).
    • income_gross vs. salary_surplus: create new feature sal_sur_inc_gross = salary_surplus/income_gross.
    • creditors_count vs. credit_count: create new feature credit_creditors = credit_count/creditors_count.

• Split Data and Create Dummy Variables:

  • Data set is split into 80% train/ 20% test while the distribution of target remains unchanged in both train/ test sets.
  • Fine-classing/ Coarse classing for creating Dummy Variables on both train and test set:
    • Turn the features into Dummy categorical variables - via fine-classing/ coarse classing methods (binning into different small categories).
    • Binning creates dummy variables and drops the one with the lowest WoE value to avoid the Dummy trap.

• The Logistic model was trained with following strategy:

  • Train a baseline model with class_weight = {0:6, 1:94}, which is revert of the dataset classes distribution. The class_weight hyperparameter is used to penalty on the Loss Function due to the imbalanced data
  • The Logistic model's hyper-parameters optimization via GridSearchCV:
    • Hyperparameters tuning on C and class_weight.
    • RepeatedStratifiedKFold Cross-Validation was used to avoid overfitting/ data leakage.
    • The training task will focus on maximizing the roc_auc_score while fine-tuning the hyperparameters.
    • Calculate the p_values of each coefficient output from the best performance model and only keep the statistically significant ones (p_values < 0.05). Note that, only the features, which got all dummies variables are statistically non-significant, will be removed.
    • The address_count feature has been removed as its all dummies variables are statistically non-significant. It is also correct when the Information Value of address_count is also lowest within all features at 0.05.
    • Refit the old model with final selected feature for the final model output.

• Model Evaluation:

  • Calculate the best threshold of Area Under the Receiver Operating Characteristic Curve (AUROC) from the final model.
  • Calculate the GINI coefficient.

Further Discussions/ Suggestions:

• The feature definitions are not provided in the instructions, if provided, the feature engineering would be more focused and result in a better performance model.
• The task was conducted in the classic PD model fashion which concentrated on explainability and only used Logistic Regression. Further methods could be considered such as tree-based, ensemble models such as voting, boosting, bagging or stacking - refer to my Kaggle notebook about this issue Bank Marketing: Ensemble Learning Pipeline
• Other model tuning techniques could be considered, such as Random search, Bayesian, etc.
• The roc_auc_score was focused as there is no information about the required evaluation metrics, such as minimizing the False Negative/ focus on Positive Class, etc. The notebook could extend in this fashion by using other metrics such as Precision-Recall AUC, Fbeta, etc.