Modern Credit Risk Modelling: The Use of Machine Learning Algorithms

Credit scorecards remain a key part of how lenders assess risk. Built using methods like logistic regression, they offer transparency and consistency and have become standard across retail and commercial lending. But as lenders work with more complex borrower data and aim to make faster, more tailored decisions, traditional scorecards are increasingly being supplemented by machine learning (ML) models. This article outlines how machine learning is being applied in credit scoring, with a focus on specific algorithms, and discusses the benefits and limitations that financial institutions need to consider.

Why Move Beyond Logistic Regression?

Most scorecards are based on logistic regression: a linear model that estimates the probability of default using a set of manually engineered features. The main benefit of this approach is the model’s simplicity: each input feature contributes in a clear, weighted way to the outcome.

However, linear models make strong assumptions. They don’t handle non-linear relationships well, and they can miss interactions between variables unless those interactions are explicitly modelled. This is where tree-based machine learning models offer a significant advantage.

Key ML Models in Credit Scoring

Random Forest

Random Forest is an ensemble method that builds multiple decision trees using bootstrapped samples of the data. Each tree votes on the outcome, and the final prediction is typically the average (for regression) or majority vote (for classification).

• Strengths: Robust to overfitting, good baseline model, handles high-dimensional data well.

• Drawbacks: Less interpretable than a single decision tree, relatively slow on large datasets.

Gradient Boosting Machines (GBMs)

GBMs build trees sequentially, where each new tree focuses on correcting the errors of the previous ones. This makes them highly flexible and powerful. Two popular implementations are the following:

XGBoost (Extreme Gradient Boosting)

Known for its speed and performance, XGBoost minimises a loss function using a convex optimisation algorithm. It includes a regularisation term to penalise model complexity and reduce overfitting. It’s widely used in production credit risk models.

LightGBM (Light Gradient Boosting Machine)

Optimised for speed and memory efficiency, LightGBM grows trees leaf-wise (rather than level-wise) and uses Gradient-based One-Side Sampling (GOSS) to evaluate information gain efficiently. This allows it to skip data points with small gradients, improving training speed and sometimes accuracy but at the risk of overfitting if not properly tuned.

These boosting models have become the standard for many ML credit scoring applications due to their high predictive accuracy and ability to model complex, non-linear relationships.

Benefits of Machine Learning Models

• Predictive Performance: Tree-based models tend to outperform linear models in ROCAUC or Gini scores. This translates to better risk differentiation, especially in segments with thin credit files or limited traditional data.

• Automatic Handling of Interactions: GBMs naturally capture interactions between features, which would need to be manually specified in a regression-based scorecard.

• Flexible Feature Input: ML models are more tolerant of skewed distributions, outliers, and missing values. They can use raw or minimally transformed inputs, which speeds up development.

• Feature Importance: While ML models are complex, they can still offer useful insights through feature importance metrics, highlighting which variables most influence predictions.

• Improved Credit Decisions: With better segmentation and risk ranking, lenders can more confidently approve marginal applications, set more competitive pricing, and reduce false declines resulting in higher profits and more financial inclusion.

Interpretability

Explainability is often the primary barrier to using ML models for regulated lending. Fortunately, tools such as SHAP (SHapley Additive exPlanations) can help. SHAP assigns an importance value to each feature for a given prediction, making it possible to provide account level explanations. An example can be seen below.

Model Governance and Fairness

ML models must meet the same governance standards as traditional scorecards: clear documentation, robust validation, and regular monitoring for performance drift or data shifts.Fairness is an equally important part of governance. Because machine learning models learn from historical data, they can unintentionally reinforce biases if those patterns exist in the training data. To mitigate this, fairness checks (such as evaluating model outcomes across different groups) should be part of both model development and ongoing monitoring.

What’s Next? Neural Networks

As lenders continue to collect richer data, neural networks may represent the next step in credit risk modelling. Neural networks can learn highly complex, non-linear relationships without requiring manual feature engineering and are particularly well suited to high-dimensional or sequential data (such as transaction histories).

However, their adoption in credit scoring remains limited due to challenges in interpretability, longer training times, and even more complex governance requirements. That said, advances in explainability tools and the growing availability of compute power make neural networks an increasingly viable option for institutions looking for the best predictive power.

Final Thoughts

Machine learning is not a replacement for traditional credit scoring, it’s an extension. Models like XGBoost, LightGBM, and Random Forests can uncover patterns and relationships that linear models can’t, leading to stronger credit decisions and more tailored risk management strategies. For institutions looking to push boundaries further, even more complex approaches such as neural networks may offer more predictive power, especially as explainability tools continue to improve.

Still, the key to successful adoption lies not just in the models themselves, but in governance, explainability, and the alignment of ML development with broader risk policies. When approached thoughtfully, ML can significantly enhance a lender’s ability to manage credit risk in a more data-driven, dynamic way.