Credit risk assessment is a core task in finance—it determines whether and under what conditions a loan is granted. Traditional models often rely on rigid rules and linear regressions. However, more and more financial institutions are turning to machine learning to evaluate risks more accurately and quickly. A look at a typical case study:
Starting Point
A mid-sized bank aims to modernize its credit decision process. The existing scorecards are too inflexible, slow to adapt to new developments (e.g., pandemics, inflation), and have a high error rate in borderline cases.
Data Basis and Preparation
The bank has access to customer data: age, income, employment duration, credit history, transaction behavior, and more. This data is cleaned, anonymized, and split into training and test sets.
Model Selection and Training
Several ML models are tested: decision trees, random forests, gradient boosting, and neural networks. The best results come from an XGBoost model—accurate, robust, and explainable.
Validation and Results
The new model achieves significantly higher accuracy (AUC > 0.90) compared to the old system. It also detects subtle patterns, such as combinations of payment rhythms and seasonal fluctuations that indicate risk.
Implementation and Monitoring
The model is not used autonomously but supports credit officers. It provides risk assessments and probabilities as decision-making aids. Monthly monitoring ensures the model does not “drift” or discriminate.
Conclusion
The use of ML in credit risk assessment enables kürt decisions, faster processes, and lower default rates. At the same time, the case study shows: technology does not replace humans—it complements them.