Anticipating results is probably every company's dream—to be able to make data-driven decisions that bring real benefits and are completely risk-proof. This might sound like a utopia for the banking industry, but it’s possible with our predictive model, which can be specifically implemented to reduce delinquency risk.
This type of model simply performs an analysis, as Google Cloud explains: “The process uses data analytics, machine learning, artificial intelligence, and statistical models to detect patterns that can predict future behavior.”
Why use a predictive model to control delinquency?
Delinquency directly impacts profitability, liquidity, and trust in your financial institution. Implementing predictive modeling applications makes it possible to anticipate risks, optimize credit policies, and protect results.
A recent study revealed that applying early warning models based on macroeconomic variables—such as GDP, unemployment, and inflation—allows delinquency to be anticipated with enough lead time to take preventive action. Additionally, an analysis in the UK compared traditional models and machine learning models using macroeconomic data, showing that a multi-split decision tree algorithm reached 95% accuracy and highlighted how inflation and unemployment are key predictors.
What techniques are used in these models?
a) Classic statistical models
Logistic regression is one of the most widely used: its strength lies in its clear interpretation and ease of implementation. It relies on key financial indicators to estimate the probability of default.
b) Machine Learning algorithms
When larger datasets are available, techniques such as random forest, LightGBM, XGBoost, or neural networks can capture complex interactions and non-linear patterns. An ensemble model combining LightGBM, XGBoost, and local modules significantly improved default prediction accuracy.
There are also deep learning approaches, such as combining CNN and Transformer, which have demonstrated greater robustness and accuracy compared to traditional models like XGBoost.
c) Hybrid models and macro-variable-based models
Combining traditional models with machine learning offers an ideal balance between accuracy and interpretability—a mix especially useful when managing sensitive risks. Meanwhile, the macroprudential approach based on economic variables allows regulators to anticipate financial crises.
How does the model work in practice?
A predictive delinquency model is built through the following stages:
- Data collection: historical client data, transactions, macroeconomic indicators.
- Processing and variable selection: identify the most relevant factors.
- Modeling: apply logistic regression, ML, or hybrid models.
- Evaluation: measure accuracy, AUC, recall, F1, etc.
- Interpretability: tools like SHAP help understand what drives predictions.
- Implementation and monitoring: adjust risk thresholds, evaluate impact, and train continuously.
A predictive model to reduce delinquency risk is not just a technological tool: it’s a competitive advantage and a profitability safeguard.
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