Improving High-Risk Consumer Credit Scoring with Financial Transaction Data - Stålhammar,Jon; Hvarfner,Carl (Master's Theses in Mathematical Sciences, 2020)
Credit scoring, the process of evaluating loan applicants with the help of numerical methods, is widely used in the financial sector. When used correctly, it can aid the decision process and lower default rates on loans. New EU regulation has allowed third-party actors to access personal banking information, following the applicant's consent. The increase in available information that financial transaction data provides can be used to improve credit scoring models, and can thus be of great value to lenders. Using a data set from a Swedish consumer institute containing raw transaction data, the authors have tried to extract useful features which can improve credit scoring precision. The authors describe transaction categorization, feature construction and selection as well as model evaluation. A large number of features were created, which lead to a substantial and involved feature reduction process. Four types of models were evaluated: a Logistic Regression, a Random Forest, an XGBoost and a Neural Network. As reference, the company's old model, a Logistic Regression using socioeconomic factors, was used. After careful evaluation using Bayesian Optimization for hyperparameter tuning, a combination of Logistic Regression models were considered to be the best and most consistent. Of the transaction-derived features evaluated, bailiff expenses, salary and ATM withdrawals were considered to be the most influential across all models. Compared to an optimized Logistic Regression base model, a four percentage point average precision improvement could be observed in the final model.
Keywords: Machine Learning; Scorecard Modelling; Feature Engineering; Feature Selection; Average Precision Score; SHAP; SMOTE; Logistic Regression; Random Forest; XGBoost; Artificial Neural Network