Developing a comprehensive PD model for accurate credit risk assessment in banking

Overview: The banking industry faces a significant challenge in accurately assessing the creditworthiness of borrowers to make informed lending decisions. Traditional credit scoring models, which often rely on limited information such as credit history, may not accurately reflect the risk of default. To address this challenge, our team was tasked with developing a more accurate and comprehensive model for predicting the probability of default.

Solution: Our team developed a PD (Probability of Default) model that uses a wide range of customer data, including behavioural and demographic data, to predict the probability of default. The model is based on machine learning algorithms that are trained on historical data to identify patterns and correlations between different variables and default risk. 

We first collected and cleaned a large dataset of loan applications and associated customer data and used various machine learning algorithms, such as logistic regression and decision trees, to train and test the model. We also conducted feature selection and engineering to identify the most relevant variables for predicting default risk.

The output of the model is a single score that reflects the probability of default, which can be used to inform lending decisions and set appropriate interest rates.

Results: The PD model was tested on a dataset of loan applications and compared to traditional credit scoring models. The results showed that our PD model outperformed the traditional models in terms of predictive accuracy and ability to identify high-risk borrowers. The model was subsequently deployed in production and has been used to inform lending decisions and set interest rates for loan products.

Case Studies

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