How to choose the best machine learning models in credit risk

The growing adoption of financial digitalization, driven by technologies such as AI and machine learning, is transforming the way institutions manage their operations and the way customers interact with financial services.

PwC predictions indicate that AI could add up to 15.7% of global GDP by 2030.
Credit evaluation, a fundamental pillar of the financial sector, uses machine learning to analyze large volumes of data and identify complex patterns. However, the successful implementation of machine learning models in this area requires careful evaluation and selection.

According to a report by McKinsey & Company, the use of machine learning in financial services can improve the accuracy of credit decisions by up to 35% and reduce delinquency rates by 30%.

In this article, we will explore the current state of Open Finance in the main LATAM countries, breaking down the progress, challenges, and outlook in Brazil, Mexico, Colombia, Chile, Argentina, and Peru, basing the research primarily on “Open Banking in Latin America,” a study carried out by Mastercard in conjunction with the American consulting firm Gartner.

Although traditional statistical models, such as discriminant analysis and logistic regression, have been pillars of credit risk evaluation, the increasing complexity of data and the need for more precise models have driven the search for alternatives that capture non-linear, high-dimensional patterns.

The emergence of machine learning, with algorithms such as Random Forest and Support Vector Machines, has offered greater flexibility and predictive power. However, it is deep learning, with its deep neural networks, that makes it possible to extract information from highly structured and unstructured data.

Machine Learning models for credit risk and evaluation

De acuerdo a un análisis de Data credit risk: A systemic review, el desarrollo y selección de modelos de machine learning para la credit evaluation incluye varias etapas clave: preparación de datos (limpieza, transformación y balanceo), selección de algoritmos adecuados (regresión logística, árboles de decisión, random forest, XGBoost, redes neuronales), entrenamiento y ajuste de hiperparámetros, evaluación del desempeño con métricas como precisión y F1-score, validación cruzada y backtesting. El modelo final se elige según su desempeño, interpretabilidad y complejidad.

Below is a concise summary of the most widely used machine learning models for learning complex patterns from large volumes of data:

• Logistic regression: Although it is a generalized linear model, its ability to estimate probabilities of default (for example, that a customer will repay a loan) has made it a classic starting point. It is simple to interpret but assumes a linear relationship between the variables.
• Decision trees: They offer high interpretability, creating a tree-shaped structure to make rule-based decisions. They are easy to visualize and understand, but can be unstable in the face of small variations in the data.
• Random forest: This algorithm, based on an ensemble of multiple decision trees, mitigates overfitting and improves accuracy. It is versatile and can handle different types of data.
• Neural networks: Multilayer models that learn hierarchical representations of data, ideal for solving complex problems. However, they require large volumes of data, and training them is computationally expensive.
• AdaBoost: This boosting algorithm creates a strong classifier from multiple weak classifiers, assigning greater weight to misclassified observations. It is sensitive to outliers.
• XGBoost: A highly efficient and accurate boosting algorithm that combines multiple decision trees. It is fast, handles missing data, and prevents overfitting through regularization. Although it offers great performance, it can be less interpretable and requires careful hyperparameter tuning.

The choice of the optimal model depends on the quality and size of the data, the required interpretability, the complexity of the problem, and the available resources. There is no universally superior model; each case must be evaluated individually. Data preprocessing is key to a model’s success. Evaluation is carried out with accuracy metrics, ROC curve, KS, recall, and F1-score. Interpretability techniques such as SHAP and LIME help explain complex models. Information based on Credit Risk Assessment based on Gradient Boosting Decision Tree.

Hyperparameter search is essential to optimize model performance, since it adjusts key parameters such as tree depth or learning rate in algorithms like Gradient Boosting. Techniques such as Grid Search, Random Search, or Bayesian optimization make it possible to explore optimal combinations of hyperparameters.

To ensure the robustness of the model, cross-validation is used, dividing the data into subsets to train and evaluate the model repeatedly, reducing the risk of overfitting. In financial contexts, such as credit risk assessment, backtesting is essential to test model performance on historical data, ensuring its predictions are reliable in real-world scenarios.

Impact of machine learning on credit risk assessment and data quality

In conclusion, machine learning has transformed credit risk assessment by providing models that analyze large volumes of data and uncover complex patterns. These models must adapt continuously to accurately measure potential defaults, taking into account the external landscape, such as the social, economic, and personal circumstances of applicants. It is essential to pay attention to data quality, as well as to the use of techniques such as undersampling and oversampling.

As new machine learning techniques advance to minimize the risk of fraud and nonpayment, it is essential that these solutions be complemented by human knowledge and experience. Artificial intelligence should be seen as a tool that, together with critical data analysis, can improve decision making and benefit both service providers and users.

Learn how Finvero’s technology, through its artificial intelligence platform for credit management, can transform your company. From application to collection, we cover the entire credit life cycle using traditional and alternative data. Discover how our machine learning models can raise your loan approval rate and reduce fraud risk. Contact us!

Follow us on our social networks

Facebook Linkedin Instagram Whatsapp

See all